Providers and deployers of AI systems: You only have a couple of weeks left until you must explicitly inform users when they are interacting with AI content.

To assist in the effort, the European Commission (Commission) has published guidelines to help AI deployers get in line with the AI Act’s transparency obligations, which will begin to go into effect on August 2.

After that, companies providing AI systems must alert users when they are interacting with AI. They must also tell users when they have been exposed to deepfakes, “emotion recognition,” or biometric categorization systems, or when they are given AI-manipulated content in matters of “public interests without human review or editorial control.”

Henna Virkkunen, the Commission’s executive VP for tech sovereignty, security and democracy, said in a statement, “with today’s guidelines, the Commission supports the smooth and effective application of the AI Act to make AI systems interacting with people such as chatbots and AI agents and AI content more transparent and trustworthy. These guidelines support providers and deployers in meeting their obligations under the AI Act, while helping citizens know when they are interacting with AI.”

Systems must include machine-readable markers to reveal such content, to reduce “the risk of deception and manipulation” and build public trust in AI.

“Generative systems have collapsed the cost of producing convincing content while the cost of judging it stands where it always stood,” said Sanchit Vir Gogia, chief analyst at Greyhound Research. This requirement is “an attempt to restore friction to that imbalance.”

A company’s non-compliance could result in fines anywhere from €750K (about $856K) to €15M (about $17 million), or even up to 3% of its total worldwide annual revenue.

Transparency requirements

The EU AI Act’s transparency requirements apply to “natural or legal persons,” public authorities, agencies, or other bodies that develop AI systems, or have them developed, and place them on the EU market or into use under their name or trademark. This means all companies, regardless of whether or not they are EU-based.

“Systems placed on the European market, put into service there, or producing outputs used there are inside the field, wherever the developer sits,” Gogia noted.

Applicable systems must be intended to interact directly with “natural persons”; these systems include AI-enabled chatbots or conversational agents, AI companions, or coding agents. However, AI-enabled tools like recommender systems, spam filters, authentication, search and retrieval, transcription, text and code auto-completion, or predictive maintenance do not fall under the rule.

Specific outputs such as AI-generated text, images, video, and audio must contain a machine-readable mark. Deepfakes and public interest-related text created by AI without human review or control must be clearly labeled, however, deepfake content that is “artistic, creative, satirical, or fictional” is largely exempt.

AI content must be marked with one of three labels: “AI,” “Fully AI-generated,” or “Partially AI-modified.” For instance, “Fully AI-generated” applies when news summaries, music, art, or videos have been created without any human oversight (apart from prompting), while “partially AI-modified” could mean a person’s face is swapped into an authentic photograph to create a deepfake.

The three icons are publicly available for free use; enterprises can download zip files in PNG and SVG formats.

Most of the Act’s transparency rules begin to go into effect on August 2. But AI systems placed on the market before then will have some leeway; they must be in compliance by December 2.

However, a four-month allowance “on one obligation, for one population of systems, contingent on one procedural step, is not a strategy,” Gogia emphasized. Enterprises should plan to comply by August 2 and “treat any relief that arrives as margin.”

A consistent code of practice

Along with the transparency guidelines, the Commission has introduced a code of practice that essentially serves as a gesture of good faith. When signed, it can provide “legal certainty” and a “simple and practical” way to demonstrate compliance with the AI Act, according to the Commission. Signatories can also collaborate through the ‘Signatory Taskforce,’ which will share practices and advance technologies around marking and labeling practices.

Providers that choose not to sign must comply through other methods and demonstrate that those methods are “adequate” through assessment by surveillance authorities, according to the Commission.

Non-signatories “keep their flexibility, and will face more case-by-case scrutiny for it,” said Gogia.

Criteria for compliance

Shashi Bellamkonda, principal research director at Info-Tech Research Group, pointed out that the transparency requirements apply to content only when three criteria are met: It has been published, is informative to the public, or is on matters of public interest.

B2B business content or blogs may not need an AI disclosure if they do not meet these criteria, he noted. Also, published text that has undergone human review or is under editorial control does not need to be labeled. Editorial control means that a person must hold the ultimate legal responsibility for the publication of the content.

Many companies like Google, Adobe, and LinkedIn have already established ways to identify images marked as AI-generated. Meta has made it a requirement, but the creator has to add the AI-generated label, Bellamkonda said.

“This is a good move for guardrails around public information, and companies with good compliance and ethical oversight may not have to worry about this,” he noted. But as a general practice, companies should disclose AI-generated content and state whether it has been human reviewed.

Creating a transparency pipeline

Establishing full transparency means identifying who carries the responsibility for the content, whether the marking survives real use, not just testing, and what evidence will defend the decision, Gogia said.

Concerns cluster around responsibility, durability and evidence. Several organizations usually touch one piece of content, and none controls the whole chain, which is why contracts become the “pressure point,” he said. Most current agreements were written to deliver software and say “almost nothing” about provenance persistence, verification access, or evidence retention.

The durability concern is the most difficult, Gogia noted, because marking performs well in controlled settings but “badly in ordinary life.” Meta, for one, said its invisible watermark was designed to survive cropping; a published test, however, found the company’s preview detector missed 55% of cropped images.

“CIOs should ask which platform can actually provide evidence before believing its dashboard,” said Gogia.

Disclosure of AI use must be “clear, distinguishable and accessible,” he emphasized. “A notice buried in lengthy terms, or reachable only through determined clicking, satisfies nobody, least of all a market surveillance authority.”

Sustained compliance is a “living control” requiring a central record of systems, duties and evidence; testing taking place where the user meets the control rather than where the developer built it; and continuous supplier assurance. Enforcement will vary by country, so keep one common baseline with local overlays, Gogia said.

His advice: Inventory every system that talks to people, generates content, or gauges sentiment; classify provider and deployer roles; place disclosures at first interaction; define substantive human review; keep the evidence.

Marks and provenance signals should be tested after content undergoes cropping, compression, translation, transcription, and other editing, Gogia said. A useful audit starts from a real output and follows its “pulse” through generation, editing and publication, identifying at “each beat” the responsible party, the surviving mark, and evidence for exceptions. Missed labels should also be traced for root cause and recurrence.

To ensure compliance, before August 2, enterprises need a prioritized inventory, live disclosures on the highest-risk use cases, and a “named owner for every control,” he noted. In the first 30 days, they should stabilize and test; in the first 90 days, push requirements into procurement processes as a standing discipline. Procurement must secure commitments on marking methods, known failure modes, and evidence access, with explicit notice if/when any of them change.

“The sensible architecture is a common transparency baseline carrying traceability, responsibility, and evidence, with jurisdictional overlays for language, sector rules, and local practice,” Gogia said.

This article originally appeared on CIO.com.

Jobs involving physical labor are at high risk of disruption from automation, with new technologies such as AI robots becoming more prevalent, according to a recent study by the Organization for Economic Co-operation and Development (OECD). That means workers in construction and extraction, farming, fishing, forestry, production and material transportation could be affected by fast-moving technology changes.

“Routine and low-skilled jobs are at higher risk,” the Paris-based public policy group said, adding that “overall, jobs requiring non-routine cognitive, social and creative skills are less susceptible to automation.”

The kinds of creative and cognitive jobs still thought to be less exposed to automation include social work and community service roles. 

OECD also said management jobs — which often require workers to devise creative answers to solve problems — fall within the creative and cognitive category. “While AI has made some high-skill job requirements more susceptible to automation, many critical skills in these roles remain difficult to automate,” OECD said.

The same still holds true for some physical and manual labor jobs – including cleaners, agricultural workers, food-prep assistants, and laborers — which are less exposed to the affects of AI, OECD said.  

But people who work in programming, translating and interpretation positions could find their work affected by the quick rise of AI tools and services. According to the organization, global AI uptake rose from 7% in 2021 to 20% in 2025.

“In these occupations, GenAI could perform a significant share of tasks at least twice as fast today or in the near future,” OECD said.

The effects of the AI boom are not always uniform across industries or regions. In a separate 2026 Employment Outlook study released by the group, exposure to disruption from generative AI (genAI) ranges from about 16% in some areas to more than 70% elsewhere, depending on industries and occupations. 

Numerous research firms have said in recent years that AI is driving short-term job losses, though tech industry experts and analysts have argued AI will also create new careers and jobs as agentic AI takes over low-skilled work. 

AI technology has become so ubiquitous that it’s been compared to electricity — virtually all companies will need it or at least know how to use it. But it’s adoption has been hindered at times as companies struggle to find ROI from its use, and by regulatory and ethical hurdles.

In the US, AI blamed for June job losses

According to a Challenger, Gray & Christmas study released earlier this month, AI was cited as the top reason for job cuts in June. The outplacement firm said employers cut 45,849 job cuts in June, of which 14,029 were attributed to AI, with the tech industry leading the cuts.

“Tech remains the epicenter of this year’s cuts,” Challenger said. “AI is the dominant force as companies are restructuring around it, automating roles, and reallocating budgets toward new capabilities. The sector is being reshaped in real time.”

Overall, AI has been responsible for 173,568 job cuts since 2021, the company said.

AI is hurting jobs in customer service, internal reporting, telecommunications, and hosting automation, said Victor Janulaitis, a staffing consultant who was formerly CEO at Janco Associates Inc.

“C-level executives continue to be focused on eliminating ‘non-essential’ managers, staff, and services,” he said. “Coders and developers have limited opportunities with legacy applications.”

While jobs in the IT sector overall are declining, current hiring tends to skew in the direction of people with AI skills. A report this month by CompTIA put job listings with AI skills at around 500,000, which is close to double the number in January.

“Employers in other industries are accelerating digital transformation initiatives and moving from AI experimentation to implementation,” said Seth Robinson, CompTIA’s vice president for industry research.

That view dovetails with what ManpowerGroup, the recruitment firm, is seeing; demand for AI-related skills has nearly doubled over the past year, said Ger Doyle, regional president of North America at ManpowerGroup.

That growth extends well beyond traditional technology roles as companies move from experimenting to AI deployments at scale, Doyle said. “We’re seeing it influence hiring across occupations ranging from data science and engineering to project management and operational roles,” he said.

Healthy software companies typically don’t suddenly eliminate one-fifth of their workforce, but monday.com is doing just that as it bets on flatter teams, AI agents, and customer implementation expertise as the winning combination in the AI era.

Monday.com co-founder and co-CEO Eran Zinman today announced the “very difficult decision” to reduce the AI work platform company’s global workforce by about 20%, or 620 people.

The move has nothing to do with increasing margins or replacing humans with AI, he insisted in his post on LinkedIn; rather, it’s a calculated decision to trim down and hone the company’s focus as AI becomes integral to day-to-day workflows.

“This is not a distress signal; it is a deliberate reset, disclosed with its price attached,” said Sanchit Vir Gogia, chief analyst at Greyhound Research. “The industry has quietly swapped the meaning of productivity, and this filing is the clearest exhibit yet.”

A ‘significant opportunity’ in technology

In a SEC filing this week, monday.com said its restructuring plan reflects the “ongoing transformation of its product, marketing, and go-to-market strategy.” The move is intended to support a “leaner, more focused operating model” as the company continues to invest in its AI-driven strategy.

Zinman noted in his post that the company has shifted to “doing the work with AI and not just managing it,” and is focused on building environments where “people and AI agents [work] together in one workspace.”

In recent months, monday.com has evolved its products, strategy, and the way it serves its customers, and Zinman contended that “the organization we built for our previous chapter is not the organization that fits the new AI era.” Monday.com needs to “execute more decisively,” take on new challenges, and quickly respond to market changes, he said.

“We have never seen such a significant opportunity in software, driven by such exciting technology,” Zinman noted. He emphasized that the reduction is not to replace people with AI, nor to improve margins; the “vast majority” of savings will be reinvested into talent, products, and AI.

The restructuring will result in a “flatter organization” with fewer management layers and smaller, more autonomous teams, and monday.com also has a new go-to-market model, Zinman explained. Customers expect “deeper implementation support” as they deploy AI, and the company will work more closely with customers, increase its on-site presence, create new roles, and “adapt many existing ones.” In its SEC filing, the company said it expects to continue hiring in “key strategic areas” throughout 2026.

Workers will be expected to work better, “not harder,” Zinman noted. He pointed to several past examples where work could have been done in a few days, but instead took many months with “multiple meetings and endless friction.”

“This wasn’t people’s fault and everyone was frustrated by this,” he said. “Our new org changes ownership to allow people to make decisions and move fast.”

A spokesperson for monday.com declined to comment further on the staff reductions.

Monday.com’s key market advantages

Monday.com certainly isn’t struggling; the company expects 19% to 20% year-over-year growth in 2026.

“Companies in that position do not restructure because they must,” Greyhound’s Gogia noted. “They restructure because they have decided to become something else.”

Melody Brue, VP and principal analyst at Moor Insights & Strategy, pointed out that organizational redesign is important for real AI transformation, but while it can signal confidence to the market, it can still be “devastating” to humans.

While the company looks as though it’s trying to do right, that ultimately remains to be seen, she said. “There are often hidden internal bruises that can surface long after layoffs.”

Monday.com’s advantage is in its “structured substrate,” Gogia noted; its boards, permissions and typed workflows give agents something firmer to act on than just documents and chat history. The company highlights its natively built agents that can be configured by any team member, as well as connectors with Claude, Microsoft Copilot, and ChatGPT, and dedicated routes for external agents to authenticate and operate.

“For some time, the sharper enterprise question has been shifting from who has an agent to who owns the governed runtime in which an agent can safely act,” he said. “Structured work is a serious claim on that runtime.”

But parts of monday.com’s agent estate remain in staged release, and its product is ultimately “mid-transition,” Gogia pointed out; its agent builder carried a beta label as recently as March,. Also, the company’s pricing model changed in May to a hybrid model charging for seats as well as mandatory AI credits. And, while its AI-powered no-code builder monday vibe passed $1 million in annual recurring revenue within two and a half months, monday.com has not released subsequent outcomes, usage volumes, or attach rates.

Further, there’s an element of “gravity” with its competitors, he observed. Asana is reorganizing teams around agents, Atlassian is wiring agents into the developer estate, and others are simply bundling them into their offerings: Microsoft is doing so across the productivity stack, and ServiceNow across enterprise operations, each with identity and procurement built in.

“Their pull is strongest exactly where monday.com wants to grow, in the largest accounts, where control-plane depth and administrative reach decide the deal,” said Gogia.

Actions for the near-term

Going forward, buyers should focus on operating risk, not headline risk, Moor’s Brue noted. In practice, that’s continuity of service, roadmap consistency, and strength of enterprise support. Productivity should be valued as better outcomes per unit of organizational effort, not mere activity.

“It should be a measure of how much smoother, faster, and more effective the operating model becomes when AI is built into the work,” said Brue.

Gogia noted that strain surfaces first in customer service, and monday.com’s attention is being redistributed. The company’s annual report disclosed that its focus is now concentrated on the largest accounts, with support for medium-sized clients moved to an AI-first and human-supported model.

During the first month of the transition, buyers should track named account continuity and escalation times, he advised. By the first quarter, keep an eye on whether credit governance and admin controls mature on schedule, and if the roadmap beyond the AI estate keeps pace. By the half-year mark, determine whether promised implementation depth is producing outcomes or “simply more billable engagement.”

Support tiers should be enumerated in writing before renewal, and buyers should contract for “side exits,” Gogia emphasized, with overage pricing fixed in advance, the right to pause consumption, and portability for workflows and agent configuration “if the relationship sours.” Finance should also insist on monthly consumption reporting by capability. Further, integration efforts, partner dependency, and change management should be considered first-class costs of the agent era, “not as afterthoughts to a license.”

“A license was a known cost,” said Gogia. “A meter is a behavior, and behavior is harder to forecast than headcount.”

This article originally appeared on CIO.com.

OpenAI has launched Presence, an enterprise service for deploying voice and chat agents that can resolve customer and employee requests, potentially automating some work now handled by frontline support teams.

The agents can answer questions and operate IT systems, and enterprises can decide what actions the agents may take and when they should seek human approval for actions or transfer a case to a human.

OpenAI is already using Presence internally for its English-language phone support channel, where it verifies callers and uses account information to complete approved actions. The company said the system resolves 75% of inbound issues without human assistance.

Another OpenAI service, Codex, can be used to monitor agents and suggest updates or improvements to processes. In OpenAI’s own tests, suggestions from Codex helped reduce handoffs to humans by 15 percentage points over 10 days, it said. Presence also includes simulation and evaluation tools that allow companies to test an agent before deployment. The tests assess whether it reaches the correct outcome, follows company policy, and hands a case to an employee when required.

OpenAI intends each Presence deployment to deal with one kind of task, for example billing issues, insurance claims, or employee IT service requests, with agents getting only the knowledge and system access required for that task.

Presence is not a self-service product: Enterprises will have to sign up for the limited availability program, with integration performed by OpenAI or selected global systems integrators.

Companies exploring or testing Presence include Spanish bank BBVA, which is evaluating the service for everyday banking support in Mexico, and Japanese technology group SoftBank, which is using it in trials involving Japanese-language customer interactions. Australian insurer IAG is assessing whether the technology can help it respond to surges in customer demand during severe weather events.

Workforce impact

OpenAI’s announcement did not address the potential effect of Presence on employment. But its claimed automation rate raises questions about how the technology could affect staffing in customer service and other support functions.

Pareekh Jain, CEO of Pareekh Consulting, said CIOs should regard the 75% figure as evidence that the technology can work, rather than as a benchmark that every enterprise can expect to reach.

Jain said OpenAI’s deployment benefits from being built around the company’s own products and data. Large enterprises may achieve lower automation rates because they must contend with fragmented legacy systems, uneven knowledge bases and more complex compliance demands.

“Most organizations should expect lower initial automation levels that improve over time as the AI agent is refined,” Jain said.

The first workforce effect is more likely to be slower hiring than immediate layoffs, according to Tulika Sheel, senior vice president at Kadence International.

“The roles most exposed are likely to be repetitive, high-volume functions such as frontline customer support and routine back-office processing,” Sheel said. “However, I would expect the first impact to be on hiring and team growth rather than immediate large-scale job cuts. Over time, enterprises may redesign roles around AI-assisted workflows, with humans focusing more on complex cases, escalation, and relationship management.”

Jain said Tier-1 support agents handling predictable queries would face the most exposure. Broader reductions would become more likely only after companies reorganize their operations around the technology.

However, Lian Jye Su, chief analyst at Omdia, said Presence is unlikely to increase the threat of job displacement because companies have used similar customer-support automation from vendors such as Genesys, NiCE, Five9 and AWS for years.

Enterprises are more likely to use Presence alongside employees, with AI handling routine requests while people remain responsible for work requiring judgment and empathy, Su said.

Cost and operational risks

Analysts said CIOs should examine whether Presence can maintain resolution quality as usage grows, since fewer human handoffs could leave employees dealing with a more difficult mix of cases.

“The key question is not simply how many tasks AI can handle, but whether it can handle them reliably at scale,” Sheel said.

The financial case will depend partly on the cost of connecting Presence to existing systems and maintaining the controls needed to govern its use, according to Jain. “Often the biggest cost of enterprise AI is not tokens but integration and governance,” Jain added.

Companies will need to determine what systems and data the agents can access, monitor their performance, and audit the actions they take. Those investments could offset early savings.

Su said the complexity of enterprise IT will make it difficult for OpenAI to automate entire workflows on its own. Enterprises will still need to work with other technology providers and human employees, while CIOs will favor systems that can be audited and integrated with existing infrastructure.

Jain said the economics could improve if companies use the same integrations and governance controls across additional workflows.

This article first appeared on CIO.

Among a range of factors leading to a wave of tech sector layoffs in 2026 is the rapid rise of artificial intelligence and automation. Companies are reconfiguring their workforces to leverage AI for increased efficiency and reduced operating costs. This realignment and reduction is implemented even by companies reporting strong financial performance.

But it’s not just AI leading to workforce cuts. Complementing this technological shift are ongoing economic uncertainty, inflation, and higher interest rates, compounded by a chip shortage and rising energy costs. This mix is driving companies to cut costs and streamline operations for increased efficiency.

According to data compiled by Layoffs.fyi, an online tracker that keep tabs on job losses in the technology sector, 123,941 tech employees were laid off at 269 companies in 2025. The site also reports that 71,981 government employees were laid off by DOGE alone, with 182,528 total federal workers laid off.

Here is a list — to be updated regularly — of some of the most prominent technology layoffs the industry has experienced recently.

Notable tech layoffs in 2026

• Monday.com
• Microsoft
• Meta
• Cisco
• Cloudflare
• Oracle
• Atlassian
• Salesforce
• Amazon
• Ericsson

July 22, 2026: Monday.com cuts 20% of its workforce to restructure for the AI era

The company says the decision to cut 620 jobs isn’t about margins, but about creating a flatter organization built around AI agents, autonomous teams, and deeper customer engagement.

July 6, 2026: Microsoft cuts 4,800 jobs, primarily in sales and Xbox teams

As the company trims thousands of jobs, it’s also investing in embedded engineering teams and AI infrastructure. The layoffs come several weeks after the company offered 8,750 US employees voluntary retirement buyouts.

June 5, 2026: Tech industry cut 38,242 jobs in May, worst since 2024

AI was blamed for 40% of the job cuts in May, up from 7% in January, according to research by employment placement company Challenger, Gray & Christmas.

May 20, 2026: Meta cuts 8,000 jobs, around 10% of workforce

The cuts are expected to expected to hit Meta’s engineering and product teams the hardest, arriving as Meta pivots toward AI to boost efficiency across its organization, according to Yahoo Tech.

May 13, 2026: Cisco to cut nearly 4,000 jobs despite strong growth in AI, enterprise networking

Despite reporting positive financial news — including record third-quarter revenue of $15.8 billion, a 12% year-over-year increase — Cisco said it will eliminate almost 4,000 jobs.

May 7, 2026: Cloudflare to cut 1,100 jobs in AI-focused restructuring

About 20% of Cloudflare’s global workforce will be culled as the company pivots for the agentic AI era, Reuters reported.

April 1, 2026: Oracle to cut up to 30,000 jobs globally, putting enterprise support and roadmaps at risk

Oracle began laying off employees on March 31 in what could be the largest workforce reduction in the company’s history. Employees received termination emails at 6 a.m. local time with immediate system lockouts and no prior warning. (Note: in June, CNBC put the final layoff tally at 21,000.)

March 12, 2026: Atlassian cuts 1,600 jobs to fund AI and enterprise expansion

Atlassian will reduce its global workforce by approximately 10%, eliminating around 1,600 roles, as the collaboration software maker redirects capital toward artificial intelligence development and enterprise sales.

March 11, 2026: Tech layoffs surpass 45,000 in early 2026

A recent analysis by RationalFX found 45,363 job cuts globally so far this year—with roughly 68% or more than 30,000 occurring in the U.S. — highlighting ongoing workforce cuts even as many tech companies report strong revenue growth.

February 10, 2026: Salesforce lays off staffers as executive leadership churn continues

Salesforce has reduced close to 1,000 roles earlier this month across teams, including marketing, product management, data analytics, and its Agentforce AI unit, Business Insider reported, quoting employees familiar with the matter.

January 23, 2026: Amazon layoffs expected to disproportionately hit AWS and tech talent

As the market slows down, AWS and other Amazon units are preparing for another round of layoffs, which is expected to overwhelmingly impact tech talent. An email from HR leader Beth Galetti on Jan. 28 confirmed 16,000 job cuts.

January 15, 2026: Ericsson plans to shed 1,600 jobs in Sweden

 Ericsson lans to cut some 1,600 jobs in Sweden, the telecommunications equipment maker said doubling down on recent cost-saving measures that have helped it weather a prolonged downturn in telecoms spending, Reuters reports.

January 13, 2026: Meta plans to cut around 10% of employees in Reality Labs business

Meta plans to cut around 10% of the employees in its Reality Labs division who work on products including the metaverse, according to three people with knowledge of the discussions, according to The New York Times.

Layoffs in 2025

• Cisco
• Oracle
• Windsurf
• Intel
• Microsoft
• Crowdstrike
• HPE
• Autodesk
• HPE
• CISA
• Workday
• Salesforce
• Meta

Global tech-sector layoffs surpass 244,000 in 2025

Economic uncertainty, elevated interest rates, and AI adoption have driven workforce reductions across tech companies worldwide, according to a RationalFX report.

October 28, 2025: Amazon to cut 14,000 jobs across company

Amazon will reduce its overall workforce by 14,000, cutting layers of management across the company and hiring in some areas to support its “biggest bets”.

August 18, 2025: Cisco and Oracle to cut hundreds of Bay Area jobs

Tech companies Cisco and Oracle are cutting hundreds of jobs across the Bay Area. Cisco will eliminate 221 positions at its Milpitas and San Francisco offices, effective Oct. 13. Oracle is reducing 101 positions in Santa Clara on the same date 

August 5, 2025: 3 weeks after acquiring Windsurf, Cognition offers staff the exit door

Cognition, the AI coding startup that acquired rival company Windsurf three weeks ago, laid off 30 employees last week and is offering buyouts to the roughly 200 remaining employees on the team, reports The Information.

July 25, 2025, Intel to lay off 22% of workforce, CEO Tan signals ‘no more blank checks’

Intel will reduce its workforce to 75,000 employees by the end of 2025 as new CEO Lip-Bu Tan implements sweeping changes designed to transform the struggling chipmaker

July 8, 2025, Intel layoffs begin: Chipmaker is cutting many thousands of jobs

Intel has begun laying off employees across the company. CEO Lip-Bu Tan told workers back in April to expect major layoffs at Intel in the coming months as the chipmaker slashes costs and overhauls its organization after years of technical setbacks and falling sales.

July 2, 2025: Microsoft will cut 9,000 workers

Microsoft will lay off about 9,000 employees, a source familiar with the workforce cut told CNBC.  The cuts will reportedly affect less than 4% of Microsoft’s global workforce and will impact different teams, geographies and levels of experience. This is the latest in a string of cuts the tech giant has made this year.

June 17, 2025: Intel looks to factory layoffs to return to profitability

Intel will lay off up to 20% of its manufacturing sector employees starting in July, according to media reports, as the company looks for options as it seeks a return to profitability. The cuts reportedly will be made around the world, but some of the layoffs will be closer to home, according to a report in The Oregonian citing an internal company memo from Intel manufacturing Vice President Naga Chandrasekaran.

May 7, 2025: CrowdStrike to lay off 5% of staff

CrowdStrike announced a plan to cut about 500 roles, roughly 5% of its workforce, to streamline operations and reduce costs. The cybersecurity company will incur about $36 million to $53 million in charges related to the layoffs

March 6, 2025: HPE cuts 2,500 jobs, remains committed to Juniper buy

CEO Antonio Neri told Wall Street analysts that HPE would begin implementing a cost-cutting program involving layoffs of about 2,500 employees over the next 18 months. HPE employs about 61,000 people worldwide.

Feb. 27, 2025: Autodesk to lay off 9% of workforce

Software maker Autodesk is laying off 1,350 staff. With the rise of subscription and multi-year contracts billed annually, and self-service enablement, it finds it needs fewer sales staff, CEO Andrew Anagnost said in a message to employees. And with its cloud, platform, and AI products proving most profitable, it’s concentrating its staff and investments there.

Feb. 27, 2025: HP to lay off 2,000 more

As part of an ongoing restructuring, HP plans to lay off up to another 2,000 workers. In recent weeks, the company has tried — unsuccessfully — to do away with telephone support staff by forcing callers to wait for at least 15 minutes if they refuse to use self-service support resources online. The company swiftly backtracked, but wider job cuts are still on.

Feb. 21, 2025: CISA lays off 130

Government employees get laid off too: In this case, 130 workers at the US Cybersecurity and Infrastructure Security Agency are being shown the door as a result of a DOGE decision. Cybersecurity experts are concerned that the cuts will harm the international collaborations that CISA has fostered, quite apart from their concerns about the security of the DOGE layoff process itself.

Feb. 5, 2025: Workday lays off 1,750

As it moves to invest more in AI and international growth, Workday is laying off 8.5% of its workforce and disposing of unused office space. Some analysts fear the cutbacks will affect the company’s customer service — unless AI can pick up the slack.

Feb. 4, 2025: Salesforce lays off over 1,000

At the same time as it’s hiring sales staff for its new artificial intelligence products, Salesforce is laying off over 1,000 workers across the company, according to Bloomberg. As of June, 2024, the company had over 72,000 employees, according to its website. Salesforce did not comment on the report. In 2024 the company reportedly laid off around 1,000 staff too, in two waves: January and July.

Jan. 14, 2025: Meta will lay off 5% of workforce

Mark Zuckerberg told Meta employees he intended to “move out the low performers faster” in an internal memo reported by Bloomberg. The memo announced that the company will lay off 5% of its staff, or around 3,600 staff, beginning Feb. 10. The company had already reduced its headcount by 5% in 2024 through natural attrition, the memo said. Among those leaving the company will be staff previously responsible for fact checking of posts on its social media platforms in the US, as the company begins relying on its users to police content.

Tech layoffs in 2024

• Equinix
• AMD
• Freshworks
• Cisco
• General Motors
• Intel
• OpenText
• Microsoft
• AWS
• Dell

Nov. 26, 2024: Equinix to cut 3% of staff

Despite intense demand for its data center capacity, Equinix is planning to lay off 3% of its workforce, or around 400 employees. The announcement followed the appointment of Adaire Fox-Martin to replace Charles Meyers as CEO and the departures of two other senior executives, CIO Milind Wagle and CISO Michael Montoya.

Nov. 13, 2024: AMD to cut 4% of workforce

AMD will lay off around 1,000 employees as it pivots towards developing AI-focused chips, it said. The move came as a surprise to staff, as the company also reported strong quarterly earnings.

Nov. 7, 2024: Freshworks lays off 660

Enterprise software vendor Freshworks laid off around 660 staff, or around 13% of its headcount, despite reporting increased revenue and profits in its fourth fiscal quarter. The company described the layoffs as a realignment of its global workforce.

Sept. 17, 2024: Cisco lays off 6,000

After laying off around 4,200 staff in February, Cisco is at it again, laying off another 6,000 or around 7% of its workforce. Among the divisions affected were its threat intelligence unit, Talos Security.

Aug. 20, 2024: General Motors lays off 1,000 software staff

More than 1,000 software and services staff are on the way out at General Motors, signalling that it could be rethinking its digital transformation strategy. In an internal memo, the company said that it was moving resources to its highest-priority work and flattening hierarchies.

August 1, 2024: Intel removes 15,000 roles

Intel plans to cut its workforce by around 15% to reduce costs after a disastrous second quarter. Revenue for the three months to June 29 stagnated at around $12.8 billion, but net income fell 85% to $83 million, prompting CEO Pat Gelsinger to bring forward a company-wide meeting in order to announce that 15,000 staff would lose their jobs. “This is an incredibly hard day for Intel as we are making some of the most consequential changes in our company’s history,” Gelsinger wrote in an email to staff, continuing: “Our revenues have not grown as expected — and we’ve yet to fully benefit from powerful trends, like AI. Our costs are too high, our margins are too low. We need bolder actions to address both — particularly given our financial results and outlook for the second half of 2024, which is tougher than previously expected.”

July 4, 2024: OpenText to lay off 1,200

OpenText said it will lay off 1,200 staff, or about 1.7% of its workforce, in a bid to save around $100 million annually. It plans to hire new sales and engineering staff in other areas in 2025, it said.

June 4, 2024: Microsoft lays off staff in Azure division

Microsoft laid off staff in several teams supporting its cloud services, including Azure for Operations and Mission Engineering. The company didn’t say exactly how many staff were leaving.

April 4, 2024: Amazon downsizes AWS in a fresh cost-cutting round

Amazon announced hundreds of layoffs in the sales and marketing teams of its AWS cloud services division — and also in the technology development teams for its physical retail stores, as it stepped back from efforts to generalize the “Just Walk Out” technology built for its Amazon Fresh grocery stores.

April 1, 2024: Dell acknowledges 13,000 job cuts

Dell Technologies’ latest 10K filing with the US Securities and Exchange Commission disclosed that the company had laid off 13,000 employees over the course of the 2023 fiscal year; it characterized the layoffs and other reorganizational moves as cost-cutting measures. “These actions resulted in a reduction in our overall headcount,” the company said. A comparison to the previous year’s 10K filing, performed by The Register, found that Dell employed 133,000 people at that point, compared to 120,000 as of February 2024. Dell announced layoffs of 6,650 staffers on Feb. 6, but it is unclear whether those cuts were reflected in the numbers from this year’s 10K statement.

See news of earlier layoffs.

OpenAI has launched Presence, an enterprise service for deploying voice and chat agents that can resolve customer and employee requests, potentially automating some work now handled by frontline support teams.

The agents can answer questions and operate IT systems, and enterprises can decide what actions the agents may take and when they should seek human approval for actions or transfer a case to a human.

OpenAI is already using Presence internally for its English-language phone support channel, where it verifies callers and uses account information to complete approved actions. The company said the system resolves 75% of inbound issues without human assistance.

Another OpenAI service, Codex, can be used to monitor agents and suggest updates or improvements to processes. In OpenAI’s own tests, suggestions from Codex helped reduce handoffs to humans by 15 percentage points over 10 days, it said. Presence also includes simulation and evaluation tools that allow companies to test an agent before deployment. The tests assess whether it reaches the correct outcome, follows company policy, and hands a case to an employee when required.

OpenAI intends each Presence deployment to deal with one kind of task, for example billing issues, insurance claims, or employee IT service requests, with agents getting only the knowledge and system access required for that task.

Presence is not a self-service product: Enterprises will have to sign up for the limited availability program, with integration performed by OpenAI or selected global systems integrators.

Companies exploring or testing Presence include Spanish bank BBVA, which is evaluating the service for everyday banking support in Mexico, and Japanese technology group SoftBank, which is using it in trials involving Japanese-language customer interactions. Australian insurer IAG is assessing whether the technology can help it respond to surges in customer demand during severe weather events.

Workforce impact

OpenAI’s announcement did not address the potential effect of Presence on employment. But its claimed automation rate raises questions about how the technology could affect staffing in customer service and other support functions.

Pareekh Jain, CEO of Pareekh Consulting, said CIOs should regard the 75% figure as evidence that the technology can work, rather than as a benchmark that every enterprise can expect to reach.

Jain said OpenAI’s deployment benefits from being built around the company’s own products and data. Large enterprises may achieve lower automation rates because they must contend with fragmented legacy systems, uneven knowledge bases and more complex compliance demands.

“Most organizations should expect lower initial automation levels that improve over time as the AI agent is refined,” Jain said.

The first workforce effect is more likely to be slower hiring than immediate layoffs, according to Tulika Sheel, senior vice president at Kadence International.

“The roles most exposed are likely to be repetitive, high-volume functions such as frontline customer support and routine back-office processing,” Sheel said. “However, I would expect the first impact to be on hiring and team growth rather than immediate large-scale job cuts. Over time, enterprises may redesign roles around AI-assisted workflows, with humans focusing more on complex cases, escalation, and relationship management.”

Jain said Tier-1 support agents handling predictable queries would face the most exposure. Broader reductions would become more likely only after companies reorganize their operations around the technology.

However, Lian Jye Su, chief analyst at Omdia, said Presence is unlikely to increase the threat of job displacement because companies have used similar customer-support automation from vendors such as Genesys, NiCE, Five9 and AWS for years.

Enterprises are more likely to use Presence alongside employees, with AI handling routine requests while people remain responsible for work requiring judgment and empathy, Su said.

Cost and operational risks

Analysts said CIOs should examine whether Presence can maintain resolution quality as usage grows, since fewer human handoffs could leave employees dealing with a more difficult mix of cases.

“The key question is not simply how many tasks AI can handle, but whether it can handle them reliably at scale,” Sheel said.

The financial case will depend partly on the cost of connecting Presence to existing systems and maintaining the controls needed to govern its use, according to Jain. “Often the biggest cost of enterprise AI is not tokens but integration and governance,” Jain added.

Companies will need to determine what systems and data the agents can access, monitor their performance, and audit the actions they take. Those investments could offset early savings.

Su said the complexity of enterprise IT will make it difficult for OpenAI to automate entire workflows on its own. Enterprises will still need to work with other technology providers and human employees, while CIOs will favor systems that can be audited and integrated with existing infrastructure.

Jain said the economics could improve if companies use the same integrations and governance controls across additional workflows.

Late in a large enterprise SAP transformation, the strategic governance conversations began to drift. Instead of executive decisions, we found ourselves debating whether the program needed dedicated testing, whether cutover required a full weekend, whether twenty Agile teams really needed coordination support and whether offshore resources were adding value at all.

The questions were not coming from the delivery teams. They were coming from the executive sponsor.

The sponsor had recently been elevated into a senior leadership role and had never sponsored a technology transformation at this scale. The challenge was not authority. The sponsor had every right to ask any question they wanted. The challenge was that strategic governance had quietly turned into a debate about delivery practices, because the sponsor did not yet have the transformation context to focus the conversation anywhere else.

This is not a story about a bad sponsor. The executive in this case was a capable senior leader with strong judgment and authentic intent. They had been placed into a role they had not yet been prepared for, and the pattern that followed was structural, not personal. It is one of the more common patterns I have observed across enterprise transformation programs, and one of the most consistently misdiagnosed.

Most program failures are not execution failures. They are sponsor mismatches.

When governance becomes a debate about delivery practices

When the sponsor does not understand what an enterprise transformation actually requires, governance forums stop functioning as decision bodies and start functioning as practice debates.

You see it in the questions that get asked. Why do we need a dedicated testing phase? Can the Build timeline be compressed? Why does cutover need a full weekend? Why do we need more Scrum Masters across 20 product teams? Can the US team simply work longer hours instead of using offshore resources? On one program, the sponsor suggested shifting the entire project’s working hours to India time, despite roughly 85 percent of the delivery organization being based in the United States.

None of these questions are unreasonable in isolation. Each one targets a real cost or timeline pressure. The problem is what is missing underneath them: an understanding of the operational risks the original choices were designed to mitigate.

When sponsors ask delivery-practice questions without that context, the program leadership team ends up defending the work instead of advancing it. Decision velocity drops. Trust between the program and its sponsor erodes. Senior delivery talent disengages from governance forums where the conversation never reaches the decisions they need made. What looks from the outside like an active sponsor producing engagement is, from inside the program, an active drain on the cycles needed to deliver.

The compounding cost is not unique to any single program. PMI’s research on executive sponsorship consistently identifies sponsor engagement quality, rather than sponsor presence alone, as one of the strongest predictors of project success. The visible symptom is debate. The actual cost is unmade decisions.

Authority is rarely the issue. Literacy is

When transformations stall under a mismatched sponsor, the diagnostic instinct is to question the sponsor’s authority. Are they senior enough? Do they have the cross-functional reach? Can they unblock?

In most of the programs I have led or advised, authority was not the limiting factor. The sponsor in the SAP program above had ample authority. They could unblock any decision the program needed. What had not been developed was the transformation literacy to know which decisions mattered, which were technical noise and which were execution risks that should not be optimized away.

This is what I have come to think of as the literacy problem. Sponsors elevated into transformation roles often have deep functional expertise (finance, operations, business unit leadership) but limited exposure to the distinct functions of PMO, organizational change management, agile delivery, testing and cutover, and how each one reduces a specific category of implementation risk. They are not expected to be SAP configuration experts. But they need enough transformation literacy to recognize which questions actually belong in a steering committee.

Harvard Business Review’s research on effective executive sponsorship has emphasized that sponsorship effectiveness depends as much on judgment as on authority. Judgment is where literacy becomes operational. A sponsor with authority but limited transformation literacy will optimize for speed and cost in ways that consistently underestimate risk. A sponsor with both will make the tradeoffs the program actually needs.

Prosci’s longstanding benchmark studies on change management have ranked active and visible executive sponsorship as the single greatest contributor to change success for two decades. The word that matters in that finding is active. Active sponsorship without transformation literacy can introduce real cost. Not because the sponsor is acting against the program, but because the optimization choices they make are based on incomplete information about what the program is built to protect against.

Shift the conversation from delivery practices to business risk

When the sponsor relationship is already in place and cannot be changed, the program leadership team has one move that consistently works: shift the conversation.

On the SAP program above, we stopped explaining why the testing phase existed. We started explaining the business risk of reducing it. We stopped debating the number of Scrum Masters. We started connecting delivery capacity to coordination across more than twenty Agile teams and the business cost of losing that coordination. We reframed offshore support as a way to maintain delivery momentum around the clock rather than asking the U.S. team to sustain fifteen-hour days.

The shift is from defending delivery practice to explaining business risk. The sponsor does not need to understand why testing takes the time it does. They need to understand what the program is exposed to if testing is compressed. They do not need to know how many Scrum Masters are statistically optimal for twenty Agile teams. They need to know what coordination breaks when the number is wrong.

This reframing accomplishes two things. First, it brings the conversation back to the level at which sponsors actually make decisions: tradeoffs between business outcomes and business risks. Second, it builds transformation literacy in the sponsor over time, almost as a byproduct. By the third or fourth iteration of business-risk-framed conversations, the sponsor begins to ask the right questions on their own.

In practice, this happens through small but deliberate moves. When the sponsor asks why a phase needs the time it takes, the program lead names two or three things that could go wrong if the time is cut and what each would cost the business. When the sponsor asks why a role is needed, the program lead names the work that would not get done without it. Every delivery-practice question gets converted into a business-risk answer.

The program leadership team’s job is not to make the sponsor an expert in SAP delivery. It is to provide enough transformation context so that executive decisions reflect both business priorities and implementation realities.

There are a few phrases I have used with executive sponsors over the years that capture the underlying issue. The sharpest one:

If the decision has to go above the sponsor, they are not the sponsor.

Sponsorship is defined by what the sponsor can decide without asking someone else. That is the test. Anything else is the appearance of sponsorship, not the substance.

For CIOs supporting enterprise transformation, the implication is direct. Sponsor selection, or sponsor preparation when selection is not an option, is not a hierarchy question. It is a transformation capability question. The same execution discipline that goes into defining decision rights, structuring governance and protecting delivery momentum should apply, with equal rigor, to assessing sponsor fit and building sponsor literacy before the program begins.

A sponsor does not need to be the technical expert. They do need to know when to trust the people who are.

This article is published as part of the Foundry Expert Contributor Network.
Want to join?

Agentic AI has advanced from the promises-and-pilots phase of 2025 to reality and rollouts in 2026. In the process, agentic AI is transforming virtually every aspect of customer relationship management (CRM), the platform that manages sales, marketing, and customer service.

“Last year, everybody was dipping their toes into the water,” says Keith Kirkpatrick, research director at The Futurum Group. This year, agentic AI has built momentum from the boardroom down, with companies recognizing that having an AI strategy is imperative. “They feel like if they don’t embrace it now, their competitors will.”

Harry Datwani, a principal at Deloitte Digital, adds that enterprise CRM customers have transitioned from “proof of concept” to “scale and execution.”

“Across sales, service, marketing, even in the commerce space, enterprises are really using AI and agentic,” he says.

“CRM in 2026 is undergoing a structural shift, not just an incremental evolution,” says Forrester analyst Kate Leggett, noting that AI is becoming a core part of CRM infrastructure, not just a feature or an add-on. According to Forrester data, around 70% of companies are already using AI in their CRM systems, she says.

Here are the hot AI-driven trends in CRM this year.

CRM becomes an action hero

CRM platforms have traditionally served as passive, static systems of record. Now, agentic AI is transforming CRM into a powerful, real-time solution that can act autonomously.

“Organizations that rethink CRM as a real-time, AI-powered system of action — and embrace agentic AI to handle complex, unpredictable work — are better positioned to deliver exceptional customer experiences,” says IDC analyst Neil Ward-Dutton. “This approach not only enhances satisfaction and loyalty but also drives operational efficiency and business agility.”

Forrester’s Leggett says that AI-powered CRM platforms have advanced from simple data capture to real-time decision-making and execution. Standard capabilities include next-best action recommendations, call summaries, automated updates, generated emails, knowledge creation, predictive forecasting, and deal scoring.

She adds that AI agents can now execute workflows, such as routing cases, sending follow-ups, and updating records (with human oversight). They can also handle end-to-end service and sales tasks autonomously, including case resolutions and sales development activities.

Agentic drives workforce changes

AI use in CRM systems is also impacting workforce strategies.

“We used to hire for deep expertise,” says Constellation Research analyst Liz Miller. “AI has commoditized expertise because I can take all that data from my CRM and train my AI models to go deep, to know everything about any product I’ve ever sold, from what has worked, what hasn’t, every price, every sale.”

Now, instead of hiring candidates with deep expertise, organizations are looking for candidates who can go wide. “I can train a model to have deep expertise. What I can’t train for is experience, because experience is what happens when a person has gone broad across a lot of different scenarios and faced complexity across that broad scenario,” says Miller.

For example, AI systems can automate many aspects of marketing, Miller notes, but there’s no substitute for creativity: people who can interrogate the data and come up with innovative marketing campaigns that connect with customers.

Terence Chesire, group vice president of ServiceNow CRM and industry workflows, says that organizations are using agentic AI to free up team members from repetitive, lower-value activities. Those employees have now moved to higher-level roles “where they’re working on transformational deals rather than just building a spreadsheet.”

“That’s what we’re seeing as super-exciting as organizations not just free up people, but the speed and effort reduction and the friction reduction in what they can do,” he adds.

Data layer takes center stage

AI’s promise to deliver actionable customer and marketing intelligence has placed even greater emphasis on the importance on sound data management practices for CRM.

“The light bulb has flashed on very brightly for our clients,” says Deloitte’s Datwani. “Everyone is talking about AI agents, but your ability to really extract value is inextricably linked to the quality of your data and the ability to make that data accessible. What we’re finding is that despite large investments over time our clients still have fragmented data. And so, they are data rich and insight poor.”

The good news, says Datwani, is that AI agents themselves can help clean up and organize data. And vendors such as Snowflake and Databricks, along with the traditional CRM powerhouses, are offering powerful data analytics solutions. “Everyone is battling for that data layer,” Datwani says.

Forrester’s Leggett adds that CRM platforms are converging with customer data platforms (CDPs), real-time event streams, and external data sources to create connected customer data networks. These real-time, connected data models can help organizations deliver hyper-personalization at scale.

Agentic ushers in pricing complexity

The shift from license- or subscription-based pricing to an outcome or consumption pricing model has the potential to help CIOs tie their CRM costs to specific business metrics, such as the number of customer service calls resolved per hour. But it has also introduced a new level of complexity when it comes to budgeting for CRM costs.

For example, Chesire says ServiceNow’s CRM pricing plan starts with a baseline subscription model, and on top of that, customers get a certain number of AI tokens per user and can buy additional tokens as AI usage ramps up.

Meanwhile, Salesforce has rolled out pay-per-resolution pricing with its recently unveiled AI Help Agent and last month acquired usage-based billing specialist m3ter. Oracle is also piloting outcome-based AI pricing.

All these approaches undercut the predictability of the subscription model, which will complicate CIOs’ cost calculus, Deloitte’s Datwani says. “Now, as you start to think about consumption and tokens, costs might look different. As folks are opening up the architecture with things like headless CRM, what will the cost model look like for API calls or MCP server calls? So, there’s many more variables,” he adds.

The rise of multi-agent orchestration

To act autonomously, agents need to access multiple data sets and software platforms seamlessly. As a result, the proliferation of agents, some embedded within specific vendor platforms and some created in-house, is going to require an orchestration layer, Futurum’s Kirkpatrick says.

He points out that organizations need to monitor and manage agents, enforcing the same type of policy-based access control that exists for people. Organizations also need to set limits on what domains a specific agent can get into, what types of data they can access, what lines can’t they cross.

Kirkpatrick predicts that a new class of orchestration tools will emerge, although it’s not clear whether that orchestration layer will be provided by the leading CRM vendors, hyperscalers, or third parties.

Datwani agrees. “The orchestration layer is an interesting area, where the traditional vendors are in on it, the hyperscalers are also offering it, and there are third parties. It’s my belief that there’s not going to be a clear winner.”

The interface becomes conversational

Enterprise users who have traditionally had to manually wrangle with CRM systems are likely to find the ability to employ voice commands using a natural language interface to be a game changer. For starters, a salesperson can say, “I have a meeting today with Customer X. Help me prepare.” The agent will collect relevant data, ingest it, and provide a summary with recommendations.

ServiceNow’s Chesire says voice-enabled CRM systems have an “almost magical” ability to record, transcribe, and understand the content of a call between a salesperson and a customer or potential customer. The system can then “build a quote” based on that conversation.

On the customer service side of the equation, AI-driven voice technology enables customers to speak to an AI agent, describe the problem using natural language, and get a response. The agent has the capability to, for example, solve a credit card dispute, order a replacement product, send out a service rep, or do whatever is needed to resolve the issue, says Chesire.

Beyond that, agentic technology is capable of understanding the underlying business process flaws that led to the product snafu, and make recommendations for ways to fix whatever led to the issue in the first place, he adds.

Agentic drives business process transformation

With the emergence of outcome-based pricing, organizations are taking a fresh look at how they measure the benefits of CRM systems. That conversation is leading to an even more important analysis of underlying business processes. Or, as Constellation’s Miller says, “The old adage of applying new technology to old processes only gets you more expensive old processes.”

“When we survey customers, we hear time and time again that the reason why they want to apply AI into their organizations is to foster exponential opportunity and exponential growth,” she says. “How do we get there with CRM has started to become the new conversation.”

According to Miller, AI systems breach the walls of siloed data and can take a fresh look at legacy workflows. They also don’t get sucked into turf wars between marketing and sales teams. As a result, they often recommend new actions that can lead to better processes. “I think it’s starting to happen. You’re starting to see applications where AI is beginning to accelerate decision-making and decision velocity,” she says.

“The next phase of maturity is going to be, how do we start to spread AI across our platforms so that we are seeing that holistic end-to-end relationship that we have always wanted to optimize. How do we thread that across platforms and across solutions. We’re starting to see organizations on the leading edge really start to pull those strategies together,” says Miller.

ISC2 has begun developing a vendor-neutral AI security certification aimed at cybersecurity professionals working to secure AI systems and manage emerging AI risks.

The nonprofit organization, best known for the CISSP certification, says it is seeking volunteers worldwide to help define the knowledge and skills that will shape the new credential. While ISC2 has not finalized the certification domains, the certification is expected to address both technical AI security and governance topics, with a pilot exam planned before the end of 2026.

According to Casey Marks, ISC2 chief operating officer, feedback from cybersecurity practitioners led ISC2 to conclude that AI security had grown beyond expanding AI content within existing certifications.

“AI has reached a tipping point,” Marks says. “AI no longer is just another tool; instead, it has fundamentally changed the cybersecurity practice itself.”

ISC2 already includes AI-related content in certifications including CISSP and CCSP, but Marks says practitioners have identified new responsibilities and risks that extend beyond those programs. “Enterprise security teams are currently grappling with significant knowledge gaps, particularly around securing model architectures against new vulnerabilities like prompt injection, data poisoning, and model inversion,” Marks adds.

Organizations are working to understand emerging governance frameworks, including the NIST AI Risk Management Framework and ISO/IEC 42001, while adapting traditional application security and security operations workflows to AI systems, he says.

ISC2 has not finalized the certification domains, but Marks says the organization expects the credential to address both technical controls and governance practices for securing AI systems and managing AI risk. The certification will use ISC2’s established certification development process, which relies on cybersecurity practitioners to define job roles, develop exam content, and validate competencies.

Marks says ISC2 will continue to update the certification through ongoing input from cybersecurity professionals, in addition to its regular certification review process.

The organization is also determining which professionals the certification will target. Marks says AI security responsibilities are emerging across security architecture, risk management, security operations, software development security, governance and compliance, communication and network security, and security assessment and testing. ISC2 says the certification will reflect how those roles are evolving.

For organizations that are building AI security programs now, Marks recommends using existing AI training resources, adopting established governance frameworks, creating cross-functional AI security working groups, and participating in the certification development process.

Marks says ISC2 expects AI knowledge to become part of most cybersecurity roles while a more specialized AI security discipline continues to develop. He says organizations will increasingly need professionals with foundational AI security knowledge, as well as specialists in areas such as adversarial machine learning, model architectures, and AI data pipelines.

Looking ahead, Marks says he expects AI security expertise to evolve into both a foundational skill for cybersecurity professionals and a specialized discipline of its own.

“At this time, we are seeing a hybrid evolution occurring in real time: AI security is simultaneously becoming a baseline expectation for all security roles, while also carving out a dedicated, highly specialized discipline,” Marks says.

Late in a large enterprise SAP transformation, the strategic governance conversations began to drift. Instead of executive decisions, we found ourselves debating whether the program needed dedicated testing, whether cutover required a full weekend, whether twenty Agile teams really needed coordination support and whether offshore resources were adding value at all.

The questions were not coming from the delivery teams. They were coming from the executive sponsor.

The sponsor had recently been elevated into a senior leadership role and had never sponsored a technology transformation at this scale. The challenge was not authority. The sponsor had every right to ask any question they wanted. The challenge was that strategic governance had quietly turned into a debate about delivery practices, because the sponsor did not yet have the transformation context to focus the conversation anywhere else.

This is not a story about a bad sponsor. The executive in this case was a capable senior leader with strong judgment and authentic intent. They had been placed into a role they had not yet been prepared for, and the pattern that followed was structural, not personal. It is one of the more common patterns I have observed across enterprise transformation programs, and one of the most consistently misdiagnosed.

Most program failures are not execution failures. They are sponsor mismatches.

When governance becomes a debate about delivery practices

When the sponsor does not understand what an enterprise transformation actually requires, governance forums stop functioning as decision bodies and start functioning as practice debates.

You see it in the questions that get asked. Why do we need a dedicated testing phase? Can the Build timeline be compressed? Why does cutover need a full weekend? Why do we need more Scrum Masters across 20 product teams? Can the US team simply work longer hours instead of using offshore resources? On one program, the sponsor suggested shifting the entire project’s working hours to India time, despite roughly 85 percent of the delivery organization being based in the United States.

None of these questions are unreasonable in isolation. Each one targets a real cost or timeline pressure. The problem is what is missing underneath them: an understanding of the operational risks the original choices were designed to mitigate.

When sponsors ask delivery-practice questions without that context, the program leadership team ends up defending the work instead of advancing it. Decision velocity drops. Trust between the program and its sponsor erodes. Senior delivery talent disengages from governance forums where the conversation never reaches the decisions they need made. What looks from the outside like an active sponsor producing engagement is, from inside the program, an active drain on the cycles needed to deliver.

The compounding cost is not unique to any single program. PMI’s research on executive sponsorship consistently identifies sponsor engagement quality, rather than sponsor presence alone, as one of the strongest predictors of project success. The visible symptom is debate. The actual cost is unmade decisions.

Authority is rarely the issue. Literacy is

When transformations stall under a mismatched sponsor, the diagnostic instinct is to question the sponsor’s authority. Are they senior enough? Do they have the cross-functional reach? Can they unblock?

In most of the programs I have led or advised, authority was not the limiting factor. The sponsor in the SAP program above had ample authority. They could unblock any decision the program needed. What had not been developed was the transformation literacy to know which decisions mattered, which were technical noise and which were execution risks that should not be optimized away.

This is what I have come to think of as the literacy problem. Sponsors elevated into transformation roles often have deep functional expertise (finance, operations, business unit leadership) but limited exposure to the distinct functions of PMO, organizational change management, agile delivery, testing and cutover, and how each one reduces a specific category of implementation risk. They are not expected to be SAP configuration experts. But they need enough transformation literacy to recognize which questions actually belong in a steering committee.

Harvard Business Review’s research on effective executive sponsorship has emphasized that sponsorship effectiveness depends as much on judgment as on authority. Judgment is where literacy becomes operational. A sponsor with authority but limited transformation literacy will optimize for speed and cost in ways that consistently underestimate risk. A sponsor with both will make the tradeoffs the program actually needs.

Prosci’s longstanding benchmark studies on change management have ranked active and visible executive sponsorship as the single greatest contributor to change success for two decades. The word that matters in that finding is active. Active sponsorship without transformation literacy can introduce real cost. Not because the sponsor is acting against the program, but because the optimization choices they make are based on incomplete information about what the program is built to protect against.

Shift the conversation from delivery practices to business risk

When the sponsor relationship is already in place and cannot be changed, the program leadership team has one move that consistently works: shift the conversation.

On the SAP program above, we stopped explaining why the testing phase existed. We started explaining the business risk of reducing it. We stopped debating the number of Scrum Masters. We started connecting delivery capacity to coordination across more than twenty Agile teams and the business cost of losing that coordination. We reframed offshore support as a way to maintain delivery momentum around the clock rather than asking the U.S. team to sustain fifteen-hour days.

The shift is from defending delivery practice to explaining business risk. The sponsor does not need to understand why testing takes the time it does. They need to understand what the program is exposed to if testing is compressed. They do not need to know how many Scrum Masters are statistically optimal for twenty Agile teams. They need to know what coordination breaks when the number is wrong.

This reframing accomplishes two things. First, it brings the conversation back to the level at which sponsors actually make decisions: tradeoffs between business outcomes and business risks. Second, it builds transformation literacy in the sponsor over time, almost as a byproduct. By the third or fourth iteration of business-risk-framed conversations, the sponsor begins to ask the right questions on their own.

In practice, this happens through small but deliberate moves. When the sponsor asks why a phase needs the time it takes, the program lead names two or three things that could go wrong if the time is cut and what each would cost the business. When the sponsor asks why a role is needed, the program lead names the work that would not get done without it. Every delivery-practice question gets converted into a business-risk answer.

The program leadership team’s job is not to make the sponsor an expert in SAP delivery. It is to provide enough transformation context so that executive decisions reflect both business priorities and implementation realities.

There are a few phrases I have used with executive sponsors over the years that capture the underlying issue. The sharpest one:

If the decision has to go above the sponsor, they are not the sponsor.

Sponsorship is defined by what the sponsor can decide without asking someone else. That is the test. Anything else is the appearance of sponsorship, not the substance.

For CIOs supporting enterprise transformation, the implication is direct. Sponsor selection, or sponsor preparation when selection is not an option, is not a hierarchy question. It is a transformation capability question. The same execution discipline that goes into defining decision rights, structuring governance and protecting delivery momentum should apply, with equal rigor, to assessing sponsor fit and building sponsor literacy before the program begins.

A sponsor does not need to be the technical expert. They do need to know when to trust the people who are.

This article is published as part of the Foundry Expert Contributor Network.
Want to join?

A new attack method found by Zenity Labs reveals that AI agents are becoming persistent insiders that attackers can recruit, rather than malware they have to install.

Its researchers have discovered AgentForger, a phishing-based attack that silently creates and launches a fully autonomous AI agent within OpenAI workspaces.

Once running, the agent has full access to apps like Outlook, Slack, SharePoint, and Google Drive. It is configured to operate indefinitely without further user interaction, can approve its own access by toggling “never ask” settings, and can continue to act on new assignments sent via email by the attackers that control it. Broad, unfettered access to systems allows it to perform reconnaissance, harvest sensitive data and credentials, impersonate victims, and launch phishing campaigns.

While OpenAI resolved the vulnerability four days after disclosure, on a larger scale, AgentForger sheds light on what can happen when AI agents go rogue.

“We’re moving into a world where software doesn’t just help people work. It works alongside them,” said Michael Bargury, co-founder and CTO of agentic AI security platform Zenity. “As AI agents become more capable, attackers will naturally look for ways to influence them, just as they’ve always looked for ways to influence people.”

A ‘persistent operator’ that acts without approval

OpenAI’s Workspace Agents can connect and work autonomously across Outlook, Gmail, Slack, Google Drive, SharePoint, and Teams. Users open the agent builder, describe what the agent can do in natural language, connect to tools, set approvals, review and test, schedule actions, then publish. For instance, an agent can autonomously handle incoming emails, review and take actions with approval, gather information from various sources to send out daily briefings, or automatically respond to questions in ChatGPT or Slack channels.

Normally, this is “useful automation,” Zenity AI red team researcher Mike Takahashi wrote in a blog post. But in this attack, “the same scheduler becomes the persistence mechanism.”

The creation workflow kicks off the moment a user clicks on a phishing link containing instructions from the threat actor. For the attack to work, a victim must be logged into ChatGPT and Workspace Agents, and have at least one integration with another app, such as Outlook, Gmail, Slack, Google Drive, SharePoint, or Teams.

Because those connections already exist, OAuth consent screens are not triggered. Furthermore, the victim does not need to click on another link, keep a Builder tab open, or even visit ChatGPT again.

The forged agent is a “persistent operator;” it is installed on the original click and given a schedule, and at those predetermined times, the agent invokes itself, scans for emails from attacker addresses with the subject line “task”, carries those orders out, then returns results to the same attacker-controlled email address.

It goes undetected because the attacker prompt instructs the Builder to toggle Outlook to never ask for approval of its actions. Typically, the default is “always ask,” to keep agents from taking unauthorized action; that switch gives agents the ability to act without asking for human approval.

“AgentForger showed that an attacker could deploy an autonomous insider agent inside your ChatGPT workspace with a single click,” said Bargury. From there, it can continue to access information, harvest credentials from various sources, impersonate employees, and carry out phishing attacks and fraud while “leveraging the trusted victim’s identity.”

A ‘planted accomplice’ that does all the work

Once activated, AgentForger can perform reconnaissance to create an internal map of a company. For instance, agents can scan Outlook, Slack, Teams, Google Drive, SharePoint, or calendar data to identify people, roles, active projects, internal discussions, or all-hands recurring meetings. This can help attackers identify where in the enterprise to target next, based on active teams and channels, projects in the works, or prominent users.

“This is the kind of internal context an attacker normally has to build slowly,” Takahashi noted. But in this scenario, action is based on a single emailed assignment. The attacker’s “planted accomplice” does all the work.

In another scenario, the agent can steal data by searching for and identifying financial documents, business agreements, or invoices. Or, it can steal credentials by scanning for messages containing passwords, one-time codes, access tokens, password recovery links, or API keys. Further, it can impersonate victims to carry out phishing scams, for instance, by sending legitimate-looking Teams messages instructing users to confirm their credentials on a fake Microsoft login page.

In all cases, collected information is organized, analyzed, and sent back to the attacker.

“AgentForger points to something much bigger than a single vulnerability,” said Bargury. “It’s less about one bug and more about understanding how the security model changes as AI becomes part of everyday business operations.”

FOMO exposing security gaps

This isn’t necessarily about trust, but more about the need to move fast and adapt, Bargury emphasized. AI agents are helping employees automate work, make decisions faster, and get more done. But enterprises fear they’ll fall behind if they don’t move quickly enough.

“The challenge is that we’re introducing a fundamentally new kind of technology into the enterprise,” said Bargury. “The pressure to integrate the next AI feature is outpacing the security controls needed to safely deploy it.”

However, the answer isn’t to slow down adoption, he emphasized; the business value is too significant. Rather, the first step is understanding where AI agents exist, who created them, what they’re connected to, and what they’re allowed to do. And when it comes to autonomous agents, enterprises need to pay attention to the processes that trigger them: A schedule, an incoming email, or another automated event.

“Those triggers should be governed just as carefully as the agent itself,” said Bargury.

High-impact actions should require approval where appropriate, and security teams should be able to quickly disable an agent or its triggers if something doesn’t look right, he said.

More broadly, AI agents are introducing the need for a new security model, he pointed out. The question is no longer just “Does this agent have permission?” It’s also, “Is this the behavior we intended?”

“The organizations that answer both questions will be in the strongest position to adopt AI safely,” Bargury said.

OpenAI has launched Presence, an enterprise service for deploying voice and chat agents that can resolve customer and employee requests, potentially automating some work now handled by frontline support teams.

The agents can answer questions and operate IT systems, and enterprises can decide what actions the agents may take and when they should seek human approval for actions or transfer a case to a human.

OpenAI is already using Presence internally for its English-language phone support channel, where it verifies callers and uses account information to complete approved actions. The company said the system resolves 75% of inbound issues without human assistance.

Another OpenAI service, Codex, can be used to monitor agents and suggest updates or improvements to processes. In OpenAI’s own tests, suggestions from Codex helped reduce handoffs to humans by 15 percentage points over 10 days, it said. Presence also includes simulation and evaluation tools that allow companies to test an agent before deployment. The tests assess whether it reaches the correct outcome, follows company policy, and hands a case to an employee when required.

OpenAI intends each Presence deployment to deal with one kind of task, for example billing issues, insurance claims, or employee IT service requests, with agents getting only the knowledge and system access required for that task.

Presence is not a self-service product: Enterprises will have to sign up for the limited availability program, with integration performed by OpenAI or selected global systems integrators.

Companies exploring or testing Presence include Spanish bank BBVA, which is evaluating the service for everyday banking support in Mexico, and Japanese technology group SoftBank, which is using it in trials involving Japanese-language customer interactions. Australian insurer IAG is assessing whether the technology can help it respond to surges in customer demand during severe weather events.

Workforce impact

OpenAI’s announcement did not address the potential effect of Presence on employment. But its claimed automation rate raises questions about how the technology could affect staffing in customer service and other support functions.

Pareekh Jain, CEO of Pareekh Consulting, said CIOs should regard the 75% figure as evidence that the technology can work, rather than as a benchmark that every enterprise can expect to reach.

Jain said OpenAI’s deployment benefits from being built around the company’s own products and data. Large enterprises may achieve lower automation rates because they must contend with fragmented legacy systems, uneven knowledge bases and more complex compliance demands.

“Most organizations should expect lower initial automation levels that improve over time as the AI agent is refined,” Jain said.

The first workforce effect is more likely to be slower hiring than immediate layoffs, according to Tulika Sheel, senior vice president at Kadence International.

“The roles most exposed are likely to be repetitive, high-volume functions such as frontline customer support and routine back-office processing,” Sheel said. “However, I would expect the first impact to be on hiring and team growth rather than immediate large-scale job cuts. Over time, enterprises may redesign roles around AI-assisted workflows, with humans focusing more on complex cases, escalation, and relationship management.”

Jain said Tier-1 support agents handling predictable queries would face the most exposure. Broader reductions would become more likely only after companies reorganize their operations around the technology.

However, Lian Jye Su, chief analyst at Omdia, said Presence is unlikely to increase the threat of job displacement because companies have used similar customer-support automation from vendors such as Genesys, NiCE, Five9 and AWS for years.

Enterprises are more likely to use Presence alongside employees, with AI handling routine requests while people remain responsible for work requiring judgment and empathy, Su said.

Cost and operational risks

Analysts said CIOs should examine whether Presence can maintain resolution quality as usage grows, since fewer human handoffs could leave employees dealing with a more difficult mix of cases.

“The key question is not simply how many tasks AI can handle, but whether it can handle them reliably at scale,” Sheel said.

The financial case will depend partly on the cost of connecting Presence to existing systems and maintaining the controls needed to govern its use, according to Jain. “Often the biggest cost of enterprise AI is not tokens but integration and governance,” Jain added.

Companies will need to determine what systems and data the agents can access, monitor their performance, and audit the actions they take. Those investments could offset early savings.

Su said the complexity of enterprise IT will make it difficult for OpenAI to automate entire workflows on its own. Enterprises will still need to work with other technology providers and human employees, while CIOs will favor systems that can be audited and integrated with existing infrastructure.

Jain said the economics could improve if companies use the same integrations and governance controls across additional workflows.

OpenAI has launched Presence, an enterprise service for deploying voice and chat agents that can resolve customer and employee requests, potentially automating some work now handled by frontline support teams.

The agents can answer questions and operate IT systems, and enterprises can decide what actions the agents may take and when they should seek human approval for actions or transfer a case to a human.

OpenAI is already using Presence internally for its English-language phone support channel, where it verifies callers and uses account information to complete approved actions. The company said the system resolves 75% of inbound issues without human assistance.

Another OpenAI service, Codex, can be used to monitor agents and suggest updates or improvements to processes. In OpenAI’s own tests, suggestions from Codex helped reduce handoffs to humans by 15 percentage points over 10 days, it said. Presence also includes simulation and evaluation tools that allow companies to test an agent before deployment. The tests assess whether it reaches the correct outcome, follows company policy, and hands a case to an employee when required.

OpenAI intends each Presence deployment to deal with one kind of task, for example billing issues, insurance claims, or employee IT service requests, with agents getting only the knowledge and system access required for that task.

Presence is not a self-service product: Enterprises will have to sign up for the limited availability program, with integration performed by OpenAI or selected global systems integrators.

Companies exploring or testing Presence include Spanish bank BBVA, which is evaluating the service for everyday banking support in Mexico, and Japanese technology group SoftBank, which is using it in trials involving Japanese-language customer interactions. Australian insurer IAG is assessing whether the technology can help it respond to surges in customer demand during severe weather events.

Workforce impact

OpenAI’s announcement did not address the potential effect of Presence on employment. But its claimed automation rate raises questions about how the technology could affect staffing in customer service and other support functions.

Pareekh Jain, CEO of Pareekh Consulting, said CIOs should regard the 75% figure as evidence that the technology can work, rather than as a benchmark that every enterprise can expect to reach.

Jain said OpenAI’s deployment benefits from being built around the company’s own products and data. Large enterprises may achieve lower automation rates because they must contend with fragmented legacy systems, uneven knowledge bases and more complex compliance demands.

“Most organizations should expect lower initial automation levels that improve over time as the AI agent is refined,” Jain said.

The first workforce effect is more likely to be slower hiring than immediate layoffs, according to Tulika Sheel, senior vice president at Kadence International.

“The roles most exposed are likely to be repetitive, high-volume functions such as frontline customer support and routine back-office processing,” Sheel said. “However, I would expect the first impact to be on hiring and team growth rather than immediate large-scale job cuts. Over time, enterprises may redesign roles around AI-assisted workflows, with humans focusing more on complex cases, escalation, and relationship management.”

Jain said Tier-1 support agents handling predictable queries would face the most exposure. Broader reductions would become more likely only after companies reorganize their operations around the technology.

However, Lian Jye Su, chief analyst at Omdia, said Presence is unlikely to increase the threat of job displacement because companies have used similar customer-support automation from vendors such as Genesys, NiCE, Five9 and AWS for years.

Enterprises are more likely to use Presence alongside employees, with AI handling routine requests while people remain responsible for work requiring judgment and empathy, Su said.

Cost and operational risks

Analysts said CIOs should examine whether Presence can maintain resolution quality as usage grows, since fewer human handoffs could leave employees dealing with a more difficult mix of cases.

“The key question is not simply how many tasks AI can handle, but whether it can handle them reliably at scale,” Sheel said.

The financial case will depend partly on the cost of connecting Presence to existing systems and maintaining the controls needed to govern its use, according to Jain. “Often the biggest cost of enterprise AI is not tokens but integration and governance,” Jain added.

Companies will need to determine what systems and data the agents can access, monitor their performance, and audit the actions they take. Those investments could offset early savings.

Su said the complexity of enterprise IT will make it difficult for OpenAI to automate entire workflows on its own. Enterprises will still need to work with other technology providers and human employees, while CIOs will favor systems that can be audited and integrated with existing infrastructure.

Jain said the economics could improve if companies use the same integrations and governance controls across additional workflows.

This article first appeared on CIO.

Among a range of factors leading to a wave of tech sector layoffs in 2026 is the rapid rise of artificial intelligence and automation. Companies are reconfiguring their workforces to leverage AI for increased efficiency and reduced operating costs. This realignment and reduction is implemented even by companies reporting strong financial performance.

But it’s not just AI leading to workforce cuts. Complementing this technological shift are ongoing economic uncertainty, inflation, and higher interest rates, compounded by a chip shortage and rising energy costs. This mix is driving companies to cut costs and streamline operations for increased efficiency.

According to data compiled by Layoffs.fyi, an online tracker that keep tabs on job losses in the technology sector, 123,941 tech employees were laid off at 269 companies in 2025. The site also reports that 71,981 government employees were laid off by DOGE alone, with 182,528 total federal workers laid off.

Here is a list — to be updated regularly — of some of the most prominent technology layoffs the industry has experienced recently.

Notable tech layoffs in 2026

• Monday.com
• Microsoft
• Meta
• Cisco
• Cloudflare
• Oracle
• Atlassian
• Salesforce
• Amazon
• Ericsson

July 22, 2026: Monday.com cuts 20% of its workforce to restructure for the AI era

The company says the decision to cut 620 jobs isn’t about margins, but about creating a flatter organization built around AI agents, autonomous teams, and deeper customer engagement.

July 6, 2026: Microsoft cuts 4,800 jobs, primarily in sales and Xbox teams

As the company trims thousands of jobs, it’s also investing in embedded engineering teams and AI infrastructure. The layoffs come several weeks after the company offered 8,750 US employees voluntary retirement buyouts.

June 5, 2026: Tech industry cut 38,242 jobs in May, worst since 2024

AI was blamed for 40% of the job cuts in May, up from 7% in January, according to research by employment placement company Challenger, Gray & Christmas.

May 20, 2026: Meta cuts 8,000 jobs, around 10% of workforce

The cuts are expected to expected to hit Meta’s engineering and product teams the hardest, arriving as Meta pivots toward AI to boost efficiency across its organization, according to Yahoo Tech.

May 13, 2026: Cisco to cut nearly 4,000 jobs despite strong growth in AI, enterprise networking

Despite reporting positive financial news — including record third-quarter revenue of $15.8 billion, a 12% year-over-year increase — Cisco said it will eliminate almost 4,000 jobs.

May 7, 2026: Cloudflare to cut 1,100 jobs in AI-focused restructuring

About 20% of Cloudflare’s global workforce will be culled as the company pivots for the agentic AI era, Reuters reported.

April 1, 2026: Oracle to cut up to 30,000 jobs globally, putting enterprise support and roadmaps at risk

Oracle began laying off employees on March 31 in what could be the largest workforce reduction in the company’s history. Employees received termination emails at 6 a.m. local time with immediate system lockouts and no prior warning. (Note: in June, CNBC put the final layoff tally at 21,000.)

March 12, 2026: Atlassian cuts 1,600 jobs to fund AI and enterprise expansion

Atlassian will reduce its global workforce by approximately 10%, eliminating around 1,600 roles, as the collaboration software maker redirects capital toward artificial intelligence development and enterprise sales.

March 11, 2026: Tech layoffs surpass 45,000 in early 2026

A recent analysis by RationalFX found 45,363 job cuts globally so far this year—with roughly 68% or more than 30,000 occurring in the U.S. — highlighting ongoing workforce cuts even as many tech companies report strong revenue growth.

February 10, 2026: Salesforce lays off staffers as executive leadership churn continues

Salesforce has reduced close to 1,000 roles earlier this month across teams, including marketing, product management, data analytics, and its Agentforce AI unit, Business Insider reported, quoting employees familiar with the matter.

January 23, 2026: Amazon layoffs expected to disproportionately hit AWS and tech talent

As the market slows down, AWS and other Amazon units are preparing for another round of layoffs, which is expected to overwhelmingly impact tech talent. An email from HR leader Beth Galetti on Jan. 28 confirmed 16,000 job cuts.

January 15, 2026: Ericsson plans to shed 1,600 jobs in Sweden

 Ericsson lans to cut some 1,600 jobs in Sweden, the telecommunications equipment maker said doubling down on recent cost-saving measures that have helped it weather a prolonged downturn in telecoms spending, Reuters reports.

January 13, 2026: Meta plans to cut around 10% of employees in Reality Labs business

Meta plans to cut around 10% of the employees in its Reality Labs division who work on products including the metaverse, according to three people with knowledge of the discussions, according to The New York Times.

Layoffs in 2025

• Cisco
• Oracle
• Windsurf
• Intel
• Microsoft
• Crowdstrike
• HPE
• Autodesk
• HPE
• CISA
• Workday
• Salesforce
• Meta

Global tech-sector layoffs surpass 244,000 in 2025

Economic uncertainty, elevated interest rates, and AI adoption have driven workforce reductions across tech companies worldwide, according to a RationalFX report.

October 28, 2025: Amazon to cut 14,000 jobs across company

Amazon will reduce its overall workforce by 14,000, cutting layers of management across the company and hiring in some areas to support its “biggest bets”.

August 18, 2025: Cisco and Oracle to cut hundreds of Bay Area jobs

Tech companies Cisco and Oracle are cutting hundreds of jobs across the Bay Area. Cisco will eliminate 221 positions at its Milpitas and San Francisco offices, effective Oct. 13. Oracle is reducing 101 positions in Santa Clara on the same date 

August 5, 2025: 3 weeks after acquiring Windsurf, Cognition offers staff the exit door

Cognition, the AI coding startup that acquired rival company Windsurf three weeks ago, laid off 30 employees last week and is offering buyouts to the roughly 200 remaining employees on the team, reports The Information.

July 25, 2025, Intel to lay off 22% of workforce, CEO Tan signals ‘no more blank checks’

Intel will reduce its workforce to 75,000 employees by the end of 2025 as new CEO Lip-Bu Tan implements sweeping changes designed to transform the struggling chipmaker

July 8, 2025, Intel layoffs begin: Chipmaker is cutting many thousands of jobs

Intel has begun laying off employees across the company. CEO Lip-Bu Tan told workers back in April to expect major layoffs at Intel in the coming months as the chipmaker slashes costs and overhauls its organization after years of technical setbacks and falling sales.

July 2, 2025: Microsoft will cut 9,000 workers

Microsoft will lay off about 9,000 employees, a source familiar with the workforce cut told CNBC.  The cuts will reportedly affect less than 4% of Microsoft’s global workforce and will impact different teams, geographies and levels of experience. This is the latest in a string of cuts the tech giant has made this year.

June 17, 2025: Intel looks to factory layoffs to return to profitability

Intel will lay off up to 20% of its manufacturing sector employees starting in July, according to media reports, as the company looks for options as it seeks a return to profitability. The cuts reportedly will be made around the world, but some of the layoffs will be closer to home, according to a report in The Oregonian citing an internal company memo from Intel manufacturing Vice President Naga Chandrasekaran.

May 7, 2025: CrowdStrike to lay off 5% of staff

CrowdStrike announced a plan to cut about 500 roles, roughly 5% of its workforce, to streamline operations and reduce costs. The cybersecurity company will incur about $36 million to $53 million in charges related to the layoffs

March 6, 2025: HPE cuts 2,500 jobs, remains committed to Juniper buy

CEO Antonio Neri told Wall Street analysts that HPE would begin implementing a cost-cutting program involving layoffs of about 2,500 employees over the next 18 months. HPE employs about 61,000 people worldwide.

Feb. 27, 2025: Autodesk to lay off 9% of workforce

Software maker Autodesk is laying off 1,350 staff. With the rise of subscription and multi-year contracts billed annually, and self-service enablement, it finds it needs fewer sales staff, CEO Andrew Anagnost said in a message to employees. And with its cloud, platform, and AI products proving most profitable, it’s concentrating its staff and investments there.

Feb. 27, 2025: HP to lay off 2,000 more

As part of an ongoing restructuring, HP plans to lay off up to another 2,000 workers. In recent weeks, the company has tried — unsuccessfully — to do away with telephone support staff by forcing callers to wait for at least 15 minutes if they refuse to use self-service support resources online. The company swiftly backtracked, but wider job cuts are still on.

Feb. 21, 2025: CISA lays off 130

Government employees get laid off too: In this case, 130 workers at the US Cybersecurity and Infrastructure Security Agency are being shown the door as a result of a DOGE decision. Cybersecurity experts are concerned that the cuts will harm the international collaborations that CISA has fostered, quite apart from their concerns about the security of the DOGE layoff process itself.

Feb. 5, 2025: Workday lays off 1,750

As it moves to invest more in AI and international growth, Workday is laying off 8.5% of its workforce and disposing of unused office space. Some analysts fear the cutbacks will affect the company’s customer service — unless AI can pick up the slack.

Feb. 4, 2025: Salesforce lays off over 1,000

At the same time as it’s hiring sales staff for its new artificial intelligence products, Salesforce is laying off over 1,000 workers across the company, according to Bloomberg. As of June, 2024, the company had over 72,000 employees, according to its website. Salesforce did not comment on the report. In 2024 the company reportedly laid off around 1,000 staff too, in two waves: January and July.

Jan. 14, 2025: Meta will lay off 5% of workforce

Mark Zuckerberg told Meta employees he intended to “move out the low performers faster” in an internal memo reported by Bloomberg. The memo announced that the company will lay off 5% of its staff, or around 3,600 staff, beginning Feb. 10. The company had already reduced its headcount by 5% in 2024 through natural attrition, the memo said. Among those leaving the company will be staff previously responsible for fact checking of posts on its social media platforms in the US, as the company begins relying on its users to police content.

Tech layoffs in 2024

• Equinix
• AMD
• Freshworks
• Cisco
• General Motors
• Intel
• OpenText
• Microsoft
• AWS
• Dell

Nov. 26, 2024: Equinix to cut 3% of staff

Despite intense demand for its data center capacity, Equinix is planning to lay off 3% of its workforce, or around 400 employees. The announcement followed the appointment of Adaire Fox-Martin to replace Charles Meyers as CEO and the departures of two other senior executives, CIO Milind Wagle and CISO Michael Montoya.

Nov. 13, 2024: AMD to cut 4% of workforce

AMD will lay off around 1,000 employees as it pivots towards developing AI-focused chips, it said. The move came as a surprise to staff, as the company also reported strong quarterly earnings.

Nov. 7, 2024: Freshworks lays off 660

Enterprise software vendor Freshworks laid off around 660 staff, or around 13% of its headcount, despite reporting increased revenue and profits in its fourth fiscal quarter. The company described the layoffs as a realignment of its global workforce.

Sept. 17, 2024: Cisco lays off 6,000

After laying off around 4,200 staff in February, Cisco is at it again, laying off another 6,000 or around 7% of its workforce. Among the divisions affected were its threat intelligence unit, Talos Security.

Aug. 20, 2024: General Motors lays off 1,000 software staff

More than 1,000 software and services staff are on the way out at General Motors, signalling that it could be rethinking its digital transformation strategy. In an internal memo, the company said that it was moving resources to its highest-priority work and flattening hierarchies.

August 1, 2024: Intel removes 15,000 roles

Intel plans to cut its workforce by around 15% to reduce costs after a disastrous second quarter. Revenue for the three months to June 29 stagnated at around $12.8 billion, but net income fell 85% to $83 million, prompting CEO Pat Gelsinger to bring forward a company-wide meeting in order to announce that 15,000 staff would lose their jobs. “This is an incredibly hard day for Intel as we are making some of the most consequential changes in our company’s history,” Gelsinger wrote in an email to staff, continuing: “Our revenues have not grown as expected — and we’ve yet to fully benefit from powerful trends, like AI. Our costs are too high, our margins are too low. We need bolder actions to address both — particularly given our financial results and outlook for the second half of 2024, which is tougher than previously expected.”

July 4, 2024: OpenText to lay off 1,200

OpenText said it will lay off 1,200 staff, or about 1.7% of its workforce, in a bid to save around $100 million annually. It plans to hire new sales and engineering staff in other areas in 2025, it said.

June 4, 2024: Microsoft lays off staff in Azure division

Microsoft laid off staff in several teams supporting its cloud services, including Azure for Operations and Mission Engineering. The company didn’t say exactly how many staff were leaving.

April 4, 2024: Amazon downsizes AWS in a fresh cost-cutting round

Amazon announced hundreds of layoffs in the sales and marketing teams of its AWS cloud services division — and also in the technology development teams for its physical retail stores, as it stepped back from efforts to generalize the “Just Walk Out” technology built for its Amazon Fresh grocery stores.

April 1, 2024: Dell acknowledges 13,000 job cuts

Dell Technologies’ latest 10K filing with the US Securities and Exchange Commission disclosed that the company had laid off 13,000 employees over the course of the 2023 fiscal year; it characterized the layoffs and other reorganizational moves as cost-cutting measures. “These actions resulted in a reduction in our overall headcount,” the company said. A comparison to the previous year’s 10K filing, performed by The Register, found that Dell employed 133,000 people at that point, compared to 120,000 as of February 2024. Dell announced layoffs of 6,650 staffers on Feb. 6, but it is unclear whether those cuts were reflected in the numbers from this year’s 10K statement.

See news of earlier layoffs.

Healthy software companies typically don’t suddenly eliminate one-fifth of their workforce, but monday.com is doing just that as it bets on flatter teams, AI agents, and customer implementation expertise as the winning combination in the AI era.

Monday.com co-founder and co-CEO Eran Zinman today announced the “very difficult decision” to reduce the AI work platform company’s global workforce by about 20%, or 620 people.

The move has nothing to do with increasing margins or replacing humans with AI, he insisted in his post on LinkedIn; rather, it’s a calculated decision to trim down and hone the company’s focus as AI becomes integral to day-to-day workflows.

“This is not a distress signal; it is a deliberate reset, disclosed with its price attached,” said Sanchit Vir Gogia, chief analyst at Greyhound Research. “The industry has quietly swapped the meaning of productivity, and this filing is the clearest exhibit yet.”

A ‘significant opportunity’ in technology

In a SEC filing this week, monday.com said its restructuring plan reflects the “ongoing transformation of its product, marketing, and go-to-market strategy.” The move is intended to support a “leaner, more focused operating model” as the company continues to invest in its AI-driven strategy.

Zinman noted in his post that the company has shifted to “doing the work with AI and not just managing it,” and is focused on building environments where “people and AI agents [work] together in one workspace.”

In recent months, monday.com has evolved its products, strategy, and the way it serves its customers, and Zinman contended that “the organization we built for our previous chapter is not the organization that fits the new AI era.” Monday.com needs to “execute more decisively,” take on new challenges, and quickly respond to market changes, he said.

“We have never seen such a significant opportunity in software, driven by such exciting technology,” Zinman noted. He emphasized that the reduction is not to replace people with AI, nor to improve margins; the “vast majority” of savings will be reinvested into talent, products, and AI.

The restructuring will result in a “flatter organization” with fewer management layers and smaller, more autonomous teams, and monday.com also has a new go-to-market model, Zinman explained. Customers expect “deeper implementation support” as they deploy AI, and the company will work more closely with customers, increase its on-site presence, create new roles, and “adapt many existing ones.” In its SEC filing, the company said it expects to continue hiring in “key strategic areas” throughout 2026.

Workers will be expected to work better, “not harder,” Zinman noted. He pointed to several past examples where work could have been done in a few days, but instead took many months with “multiple meetings and endless friction.”

“This wasn’t people’s fault and everyone was frustrated by this,” he said. “Our new org changes ownership to allow people to make decisions and move fast.”

A spokesperson for monday.com declined to comment further on the staff reductions.

Monday.com’s key market advantages

Monday.com certainly isn’t struggling; the company expects 19% to 20% year-over-year growth in 2026.

“Companies in that position do not restructure because they must,” Greyhound’s Gogia noted. “They restructure because they have decided to become something else.”

Melody Brue, VP and principal analyst at Moor Insights & Strategy, pointed out that organizational redesign is important for real AI transformation, but while it can signal confidence to the market, it can still be “devastating” to humans.

While the company looks as though it’s trying to do right, that ultimately remains to be seen, she said. “There are often hidden internal bruises that can surface long after layoffs.”

Monday.com’s advantage is in its “structured substrate,” Gogia noted; its boards, permissions and typed workflows give agents something firmer to act on than just documents and chat history. The company highlights its natively built agents that can be configured by any team member, as well as connectors with Claude, Microsoft Copilot, and ChatGPT, and dedicated routes for external agents to authenticate and operate.

“For some time, the sharper enterprise question has been shifting from who has an agent to who owns the governed runtime in which an agent can safely act,” he said. “Structured work is a serious claim on that runtime.”

But parts of monday.com’s agent estate remain in staged release, and its product is ultimately “mid-transition,” Gogia pointed out; its agent builder carried a beta label as recently as March,. Also, the company’s pricing model changed in May to a hybrid model charging for seats as well as mandatory AI credits. And, while its AI-powered no-code builder monday vibe passed $1 million in annual recurring revenue within two and a half months, monday.com has not released subsequent outcomes, usage volumes, or attach rates.

Further, there’s an element of “gravity” with its competitors, he observed. Asana is reorganizing teams around agents, Atlassian is wiring agents into the developer estate, and others are simply bundling them into their offerings: Microsoft is doing so across the productivity stack, and ServiceNow across enterprise operations, each with identity and procurement built in.

“Their pull is strongest exactly where monday.com wants to grow, in the largest accounts, where control-plane depth and administrative reach decide the deal,” said Gogia.

Actions for the near-term

Going forward, buyers should focus on operating risk, not headline risk, Moor’s Brue noted. In practice, that’s continuity of service, roadmap consistency, and strength of enterprise support. Productivity should be valued as better outcomes per unit of organizational effort, not mere activity.

“It should be a measure of how much smoother, faster, and more effective the operating model becomes when AI is built into the work,” said Brue.

Gogia noted that strain surfaces first in customer service, and monday.com’s attention is being redistributed. The company’s annual report disclosed that its focus is now concentrated on the largest accounts, with support for medium-sized clients moved to an AI-first and human-supported model.

During the first month of the transition, buyers should track named account continuity and escalation times, he advised. By the first quarter, keep an eye on whether credit governance and admin controls mature on schedule, and if the roadmap beyond the AI estate keeps pace. By the half-year mark, determine whether promised implementation depth is producing outcomes or “simply more billable engagement.”

Support tiers should be enumerated in writing before renewal, and buyers should contract for “side exits,” Gogia emphasized, with overage pricing fixed in advance, the right to pause consumption, and portability for workflows and agent configuration “if the relationship sours.” Finance should also insist on monthly consumption reporting by capability. Further, integration efforts, partner dependency, and change management should be considered first-class costs of the agent era, “not as afterthoughts to a license.”

“A license was a known cost,” said Gogia. “A meter is a behavior, and behavior is harder to forecast than headcount.”

This article originally appeared on CIO.com.

Healthy software companies typically don’t suddenly eliminate one-fifth of their workforce, but monday.com is doing just that as it bets on flatter teams, AI agents, and customer implementation expertise as the winning combination in the AI era.

Monday.com co-founder and co-CEO Eran Zinman today announced the “very difficult decision” to reduce the AI work platform company’s global workforce by about 20%, or 620 people.

The move has nothing to do with increasing margins or replacing humans with AI, he insisted in his post on LinkedIn; rather, it’s a calculated decision to trim down and hone the company’s focus as AI becomes integral to day-to-day workflows.

“This is not a distress signal; it is a deliberate reset, disclosed with its price attached,” said Sanchit Vir Gogia, chief analyst at Greyhound Research. “The industry has quietly swapped the meaning of productivity, and this filing is the clearest exhibit yet.”

A ‘significant opportunity’ in technology

In a SEC filing this week, monday.com said its restructuring plan reflects the “ongoing transformation of its product, marketing, and go-to-market strategy.” The move is intended to support a “leaner, more focused operating model” as the company continues to invest in its AI-driven strategy.

Zinman noted in his post that the company has shifted to “doing the work with AI and not just managing it,” and is focused on building environments where “people and AI agents [work] together in one workspace.”

In recent months, monday.com has evolved its products, strategy, and the way it serves its customers, and Zinman contended that “the organization we built for our previous chapter is not the organization that fits the new AI era.” Monday.com needs to “execute more decisively,” take on new challenges, and quickly respond to market changes, he said.

“We have never seen such a significant opportunity in software, driven by such exciting technology,” Zinman noted. He emphasized that the reduction is not to replace people with AI, nor to improve margins; the “vast majority” of savings will be reinvested into talent, products, and AI.

The restructuring will result in a “flatter organization” with fewer management layers and smaller, more autonomous teams, and monday.com also has a new go-to-market model, Zinman explained. Customers expect “deeper implementation support” as they deploy AI, and the company will work more closely with customers, increase its on-site presence, create new roles, and “adapt many existing ones.” In its SEC filing, the company said it expects to continue hiring in “key strategic areas” throughout 2026.

Workers will be expected to work better, “not harder,” Zinman noted. He pointed to several past examples where work could have been done in a few days, but instead took many months with “multiple meetings and endless friction.”

“This wasn’t people’s fault and everyone was frustrated by this,” he said. “Our new org changes ownership to allow people to make decisions and move fast.”

A spokesperson for monday.com declined to comment further on the staff reductions.

Monday’s key market advantages

Monday.com certainly isn’t struggling; the company expects 19% to 20% year-over-year growth in 2026.

“Companies in that position do not restructure because they must,” Greyhound’s Gogia noted. “They restructure because they have decided to become something else.”

Melody Brue, VP and principal analyst at Moor Insights & Strategy, pointed out that organizational redesign is important for real AI transformation, but while it can signal confidence to the market, it can still be “devastating” to humans.

While the company looks as though it’s trying to do right, that ultimately remains to be seen, she said. “There are often hidden internal bruises that can surface long after layoffs.”

Monday.com’s advantage is in its “structured substrate,” Gogia noted; its boards, permissions and typed workflows give agents something firmer to act on than just documents and chat history. The company highlights its natively built agents that can be configured by any team member, as well as connectors with Claude, Microsoft Copilot, and ChatGPT, and dedicated routes for external agents to authenticate and operate.

“For some time, the sharper enterprise question has been shifting from who has an agent to who owns the governed runtime in which an agent can safely act,” he said. “Structured work is a serious claim on that runtime.”

But parts of monday.com’s agent estate remain in staged release, and its product is ultimately “mid-transition,” Gogia pointed out; its agent builder carried a beta label as recently as March,. Also, the company’s pricing model changed in May to a hybrid model charging for seats as well as mandatory AI credits. And, while its AI-powered no-code builder monday vibe passed $1 million in annual recurring revenue within two and a half months, monday.com has not released subsequent outcomes, usage volumes, or attach rates.

Further, there’s an element of “gravity” with its competitors, he observed. Asana is reorganizing teams around agents, Atlassian is wiring agents into the developer estate, and others are simply bundling them into their offerings: Microsoft is doing so across the productivity stack, and ServiceNow across enterprise operations, each with identity and procurement built in.

“Their pull is strongest exactly where monday.com wants to grow, in the largest accounts, where control-plane depth and administrative reach decide the deal,” said Gogia.

Actions for the near-term

Going forward, buyers should focus on operating risk, not headline risk, Moor’s Brue noted. In practice, that’s continuity of service, roadmap consistency, and strength of enterprise support. Productivity should be valued as better outcomes per unit of organizational effort, not mere activity.

“It should be a measure of how much smoother, faster, and more effective the operating model becomes when AI is built into the work,” said Brue.

Gogia noted that strain surfaces first in customer service, and monday.com’s attention is being redistributed. The company’s annual report disclosed that its focus is now concentrated on the largest accounts, with support for medium-sized clients moved to an AI-first and human-supported model.

During the first month of the transition, buyers should track named account continuity and escalation times, he advised. By the first quarter, keep an eye on whether credit governance and admin controls mature on schedule, and if the roadmap beyond the AI estate keeps pace. By the half-year mark, determine whether promised implementation depth is producing outcomes or “simply more billable engagement.”

Support tiers should be enumerated in writing before renewal, and buyers should contract for “side exits,” Gogia emphasized, with overage pricing fixed in advance, the right to pause consumption, and portability for workflows and agent configuration “if the relationship sours.” Finance should also insist on monthly consumption reporting by capability. Further, integration efforts, partner dependency, and change management should be considered first-class costs of the agent era, “not as afterthoughts to a license.”

“A license was a known cost,” said Gogia. “A meter is a behavior, and behavior is harder to forecast than headcount.”

At Cisco, VP of engineering Jason Andrews deals with all the same technical issues as every other company deploying AI, including ensuring it’s governed, secure, and integrating multiple data sources, legacy systems, and AI models.

But these issues are relatively straightforward compared to the bigger challenges relating to the fast pace of change, specifically how AI can touch and transform nearly every aspect of business.

“We’re thinking about it every day,” he says. “My belief is we’ll be seeing a massive acceleration of everything.”

In coding, for example, he’s witnessing productivity increases up to 110% with AI assistants. “I can build apps or custom integrations a lot faster,” he adds.

And the real benefit of AI isn’t just in speeding up individual steps in a process, but in making AI the core of a new business process. But building it from scratch puts even more pressure on organizations trying to get employees up to speed on new ways of doing things.

“We want to move fast, train people, and get them onboarded,” he says. “But what I thought AI was going to do for my organization nine months ago is different from three months ago.” So by the time something is rolled out, it’s changed three times.

“I struggle with the change management aspect,” he says. “The legacy model of change management isn’t fast enough. How do you create that constant learning?”

One of the ways Cisco approaches it is to create communities where people can talk about these issues and share best practices and governance, and you have to keep people’s minds open that every day is going to be different than the last, Andrews adds.

Testing the AI waters

Cisco isn’t the only organization struggling with change management in the face of the AI tsunami. In a survey of 2,000 global CEOs IBM released in May, 83% of them said AI success depends more on adoption than on the technology itself, and 77% said talent and technology roles are converging.

“Thanks to Claude Code, our entire development cadence is exponentially greater than a year ago,” says Andrew Johnson, CIO at Brownstein Hyatt Farber Schreck, a Denver-based law firm with about 700 employees and clients around the US. But, as with Cisco, the biggest challenge isn’t technical.

“In our industry, with our circumstances, we’re probably less constrained by technical capability than organizational constraints, culture, aptitude, the need to bind people to technology, and what helps me and the client,” he says. “There’s a tremendous amount of cultural shift that has to happen in our organization, which is far more demanding of my attention and complexity of thought than the technical stuff.”

Companies that bill by the hour, such as law firms, may face additional challenges as attorney productivity increases because billable hours might go down. Alternatively, the total number of cases could go up as litigation becomes less expensive. Either way, firms that adapt will see competitive advantage, and the rest will fall behind, putting more pressure on the need for change management.

“If people can’t embrace technology, we won’t be able to get a lot of value out of it,” says Johnson. “I’m talking to people about adapting their way of work. There are certainly a lot of people intrigued and anxious to dive in. They recognize the connection between the potential of the technology and what we do.”

But helping everyone see that connection and then working with them to change their habits is difficult, and requires solid relationships and good communications. “That’s been far more of a bottleneck for us,” he says.

To address the issue, the firm has developed a network of technology champions who also understand the legal side of the business. “Now we need lawyers who know how to use the technology and can articulate these things to the people we’re trying to reach,” Johnson says.

But change management is only one leadership bottleneck slowing AI adoption. Companies also struggle with figuring out their vision for AI, with slow decision-making, and a tendency to focus on the past instead of the future.

Vision and strategy

In another survey, this time of 950 business leaders released by Grant Thornton in April, 51% said strategy is the biggest driver of ROI when it comes to AI adoption, but 79% of operations leaders said they don’t have a fully developed and implemented AI strategy.

“Having leadership understanding why AI is needed and what objective they’re trying to achieve is very important,” says Shivi Verma, senior manager of engineering at Docusign. “Sometimes leadership doesn’t have a strategy for their organization on how AI should be adopted. Many times it’s bottom-up, which creates a chaotic experience.”

When Docusign started adopting gen AI, different teams and organizational units wanted to go in different directions. “All were coming up with their own strategy and tooling,” he says. So Docusign brought business leaders together to understand the pain points, and decide on the technology.

“Getting requirements and placing a bet on a specific technology was important,” he says, “as well as pivoting to a different technology if needed.”

In order to adapt to changes, the company wanted to have a nimble approach, starting with smaller use cases, with power users, and problem areas.

“We try to plan for four to six months,” he adds. “We set expectations for our leadership that we place a bet with a specific technology, but want to be able to pivot.”

Today, the leadership challenge front lines have moved yet again, to agentic AI. “Folks are creating their own agents and deciding their own permissions,” Verma adds. “We’re still coming up with a governance strategy.”

Slow decision-making

When it comes to AI deployments, Dan Diasio, global AI consulting leader at EY and CTO for its US consulting business, admits he’s a bottleneck.

There’s a great deal of interest in what AI can do, and using a variety of new AI tools. But since the firm deals with sensitive client data, safety is paramount. It’s a slow process, but important to build secure infrastructure, and to have trust in the technology. “That’s a reasonable bottleneck that makes sense,” he says.

Trust in the tools they work with is essential because clients expect it. “Every tool we use has to go through a detailed security and information privacy impact assessment, as well as a whole other set of controls so they can be used appropriately and safely,” he says.

These reviews can take a lot of time, though, and in the age of AI, speed is a highly valued currency. So how do you balance the two, when safety reviews can require input from a lot of different stakeholders and be extremely time intensive?

“We’ve stood up a team to be able to quickly certify and address a variety of platforms,” Diasio says. “Instead of working with different departments in the way we used to, we’ve started identifying representatives from different departments into a cohort. Decisions we used to make in months now take weeks.”

According to a West Monroe survey of more than 1,200 leaders released earlier this year, slow decision-making is already showing up on the bottom line. Nearly three out of four leaders said their organizations lose up to 5% of annual revenue to slow decision-making and delayed execution.

And the top reasons for the delays? According to 40% of the managers surveyed, the problem was the skills gaps of overwhelmed teams, and 35% pointed to layers of management or approvals. Nearly half said they’re spending 10 to 25% of their time on rework, excessive approvals, and unnecessary meetings, and more than half say up to 50% of their projects fail or lose momentum to delays.

Focus on the future, not the past

When it comes to the decision about where to apply AI in an organization, the tendency, Diasio says, is to turn to the experts with the most expertise in the business. But these are the same people most likely to focus on improving on what they’re already doing.

“And that often blinds people to what’s possible in the future,” he says. “That becomes a significant bottleneck.” So the solution is to revamp the decision-making process around the new reality.

“What we see some advanced companies do is give people who don’t understand the process but understand the technology equal footing with people who don’t understand the technology but understand the process,” he says. “A lot of companies are disproportionately focused on just addressing their operating model right now.”

Instead of focusing on what they’re currently doing, AI-native companies will start with a focus on the customer, he says. This shift in focus isn’t likely to show up immediately on the bottom line, or result in the highest possible number of pilots going into production.

“If leaders are in a position where they’re justifying the use of a technology to the board or their CFO, they become a bottleneck when they start demonstrating their value in terms of the number of things they’re doing,” Diasio says.

But 150 or 200 use cases deployed into production may feel like progress, like things are happening in the organization. But all these use cases are a waste of time and money if they’re applied to existing processes that don’t move the needle. “We see that happen in organizations today,” he says. “Maybe we need to reinvent the processes.”

It’s no secret that companies will need to change in order to adapt to AI. Deloitte recently surveyed 660 global technology leaders and 81% said their current operating model can deploy and govern AI enterprise-wide, but 75% also said their organization must change its operating model within the next 12 to 18 months to drive greater value.

AI ROI is real, says China Widener, Deloitte vice chair and US tech, media, and telecom industry leader. But it’s currently weighted toward efficiency gains, with broader business transformation and revenue upside still developing.

Another Deloitte survey showed that the clearest results from AI were in productivity, with 66% of organizations reporting gains, and cost efficiency, with 40% saying AI reduces costs. “However, revenue impact is still emerging,” says Widener. “Only one in five companies says AI is driving top-line growth today.” But optimism prevails, with 74% expecting it to do so in the future.

When Anthropic unveiled Project Glasswing and the Mythos model, much of the discussion focused on the capabilities themselves.

Security leaders debated what these systems could mean for vulnerability discovery, exploit development and the pace of offensive innovation. Researchers examined technical benchmarks. Industry observers questioned how quickly these capabilities might fall into attackers’ hands.

Those conversations are important. They also point to a larger question that predominates my discussions with CISOs: How much time do we have?

Over the past year, conversations about AI in cybersecurity have changed noticeably. Twelve months ago, security leaders wanted to understand whether AI could meaningfully improve security operations. They wanted to know whether it could accurately investigate alerts, reduce analyst workload and operate reliably in production environments.

Today, security leaders are asking about timelines, implementation, how quickly AI is changing the threat landscape and what that means for how security teams operate.

Anthropic’s Mythos and Glasswing, OpenAI’s Daybreak and advances in DeepSeek accelerate those conversations. Each development provides another glimpse into the pace at which AI capabilities are advancing.

AI now reasons through security problems that historically required highly specialized expertise. The implications span vulnerability discovery, attack-path analysis, reconnaissance, social engineering and security operations.

The shift reflects a broader reality: cybersecurity is entering a period where the pace of adaptation may matter as much as the quality of defenses themselves. AI is accelerating both offense and defense simultaneously. Organizations are quickly redesigning security operations around that reality.

One consequence is becoming increasingly visible. For years, cybersecurity teams invested enormous effort in discovering threats, identifying vulnerabilities, gathering telemetry and collecting intelligence. AI is accelerating many of those activities simultaneously. Visibility is improving. Discovery is accelerating. Investigations are becoming faster and more comprehensive.

The bottleneck is beginning to move. The challenge increasingly centers on how quickly organizations can act on what they know. The organizations that gain an advantage may not be the ones with the most information. They will be the ones who can operationalize that information the fastest.

The timeline is compressing

Cybersecurity has experienced many major technology transitions. Cloud computing changed infrastructure. Mobile devices expanded the attack surface. Digital transformation connected systems that were previously isolated.

AI introduces a different dynamic.

Most technology transitions unfolded over years. Organizations had time to evaluate, pilot, deploy and gradually adapt operating models.

The current AI cycle moves at a different pace.

Capabilities improve continuously. New models arrive every few months. New research emerges every few weeks. Security teams absorb developments at the same time attackers do.

Vulnerability discovery provides a useful example. Security teams have long operated around a familiar cycle of discovery, validation, remediation and protection. AI systems accelerate every stage of that process. Similar patterns exist in phishing, reconnaissance, social engineering and attack planning.

A vulnerability that once moved through that cycle over weeks increasingly now moves through those stages in days or, in some cases, hours.

Attackers are already operating at the speed of AI. Defenders are now focused on reaching the same level of operational speed.

This shift is changing the questions CISOs ask.

Early discussions focused on capability. Could AI investigate alerts accurately? Could it operate reliably in production environments? Could it be trusted with meaningful security work?

As organizations gained experience with AI, the discussion shifted toward implementation. Security teams began evaluating where AI could create operational leverage and how quickly they could deploy it into existing workflows.

Today, many CISOs are focused on timing.

The pace of advancement is influencing planning horizons, budget decisions and operating-model discussions. Security leaders are evaluating how quickly they can introduce AI into investigations, threat hunting, detection engineering and response workflows. Boards are asking questions. Executive teams are paying attention.

Security programs that once viewed AI as a future initiative increasingly view it as a current operational priority.

The industry is moving from evaluating AI as a technology to incorporating AI as a security capability.

The timeline compression creates pressure on the traditional security operations model. Investigation speed, response speed and defensive coverage increasingly determine whether organizations can keep pace with adversaries operating with AI assistance.

Security operations are entering a new phase

The impact of AI is becoming particularly visible inside the SOC.

Many security operations centers were built around a straightforward assumption: alerts flow to human analysts who conduct investigations. Operational capacity scales primarily through hiring.

The volume of security data, the number of alerts and the complexity of modern environments have steadily increased. Security teams have responded by building processes, adding tools and creating specialized analyst roles.

AI introduces a new source of operational capacity.

Investigations that require analysts to examine dozens or hundreds of artifacts across endpoint, identity, cloud, network and email systems can now be performed in minutes. Analysts gain access to investigative depth and consistency that would be difficult to achieve manually at scale.

Many security leaders now view this capability through the lens of operating model design. They are examining how investigations are performed, how work is distributed and where human expertise creates the greatest value.

The evolution of the analyst role

One of the most important developments emerging from early production deployments is the evolution of analyst responsibilities.

Security analysts remain central to security operations. Their expertise becomes even more valuable as AI systems take on larger portions of investigative work.

Threat hunting, detection engineering, response strategy, governance and oversight are receiving increased attention. Analysts spend more time shaping how investigations are conducted, evaluating outcomes and improving overall security operations.

Many organizations are already beginning this shift.

Teams are investing more heavily in proactive security activities. Detection engineering programs are expanding. Threat hunting is becoming more accessible. Analysts are spending more time improving systems and less time repeating investigative tasks.

These changes create what I think of as an analyst-amplified SOC: an environment where AI expands the reach of security professionals and enables deeper security work across the organization.

Trust is critical and it doesn’t have to compromise speed

Faced with a compressing timeline, the instinct is to treat speed and trust as a trade-off, i.e., move faster, verify less. That trade-off feels inevitable. It isn’t.

You don’t trust AI in the abstract. You trust that a system understands your tools, your telemetry and the edge cases that only exist in your network. The problem was never speed. It’s speed without context. The faster a context-blind system runs, the more decisions you’re left unable to verify.

The tension eases when the system is quick to deploy and tunes to your environment once it’s there, rather than treating every network the same. Speed stops being the thing you trade against trust. The more it learns about your environment, the sharper and more trustworthy it becomes, so the two compound rather than compete. Trust still develops through operational evidence such as measurable outcomes, visibility into decisions and consistent performance. But where that evidence accrues matters.

The organizations making the fastest real progress understand this. They don’t compromise quality and trust for speed. They invest in AI that earns trust inside their own environment, so they don’t have to choose.

Leadership during a period of rapid change

The conversations surrounding Mythos and Glasswing reflect a broader reality facing security leaders.

AI is becoming part of both offense and defense. Security teams are incorporating it into investigations, detection engineering, response workflows and threat hunting. Attackers are incorporating it into their own operations.

Security leaders have an opportunity to modernize operating models, expand defensive capacity and build organizational experience while these capabilities continue to evolve.

The organizations making progress today are investing in readiness. They are building experience, adapting workflows and preparing teams for a new model of security operations.

The next phase of cybersecurity will be defined by how effectively organizations combine human judgment with machine-scale execution.

The question facing security leaders is increasingly clear: How quickly can their organizations adapt to a continuously changing threat environment?

This article is published as part of the Foundry Expert Contributor Network.
Want to join?

As the CSO grows in prominence, security leaders are increasingly earning a seat at the executive table, reporting directly to the CEO with the expectation to help drive business strategy and ensure organizational success.

Reporting to the CEO unlocks greater access and influence for security leaders, and while CSOs who report to their organization’s CIO still have clout, it’s a very different experience picking up the phone to speak directly with the CEO as a strategic partner.

Regardless of reporting structure, CSOs must clearly understand what they are being tasked to solve. That might sound simple, but making the leap to being a CEO’s direct report requires a new perspective, a different set of skills, and a business-level focus on metrics to do so.

We asked several current CSOs, CEOs, and IT staffing experts for advice on how security executives can best navigate a direct reporting relationship with their CEO. Offering insights below are George Gerchow, CSO at Bedrock Data and member of the IANS faculty; Matt Chiodi, CSO of Cerby; Chris Schueler, CEO at Cyderes; and Greg Fuller, vice president of the Technology Skills Suite at Skillsoft.

1. Understand how the CEO views your role

Most CEOs expect that, when you report directly to them, you fully own your functional area. Whether it’s cybersecurity, operations, or finance, they look to you as the expert in that domain. The CEO may have opinions, but ultimately, you are expected to lead and provide direction.

CEOs expect their CSO to be a true strategic partner, not just a risk reporter — connecting cybersecurity to revenue protection, regulatory compliance, customer trust, and operational resilience. In turn, CSOs should expect CEOs to treat governance as a strategic enabler, not a bureaucratic necessity.

2. Power up on skills vital to your organization at an executive level

On the technology side, AI and machine learning, cloud security, incident response, zero trust architecture, and governance, risk, and compliance (GRC) are the areas where threats evolve fastest and strategic leadership has the greatest impact. 

Equally important are “power skills”: communication, critical thinking, adaptability, and emotional intelligence. The ability to translate complex risk into business terms is what separates a strong CSO from a purely technical one. Skills, not titles, define effectiveness in the eyes of a CEO.

3. Take advantage of your direct access

Direct access to the CEO will enable you to influence strategy, shape resilience planning, and ensure cybersecurity is treated as a business imperative rather than a cost center. That authority is strongest when the CEO understands cybersecurity as a strategic lever, not just a technical function. 

While a direct reporting relationship gives you access to the CEO, it also comes with the responsibility to operate at that level. You need to provide clear, executive-level visibility into your cybersecurity program.

4. Brush up on business translation

A CSO who leads with business alignment will always carry more influence when they can translate risk into business language rather than technical jargon. Building programs that must survive an IPO, a FedRAMP audit, and real customer scrutiny forces you to tie security to revenue and trust.

The most valuable skill is translation — defining technical risk in terms of executive action and business impact that a CEO and a board can act on. You must build trust through transparency. These are the human skills that complement technology, creating a collaborative human-AI dynamic where leaders make faster, better-informed decisions. 

5. Treat conversations as risk assessment opportunities

Highly effective security leaders treat every business conversation as a risk conversation in disguise. That mindset is what largely separates a great CSO from a great technologist. Earn the CEO’s trust by speaking business first, security second. Translate every risk into revenue, reputation, or regulatory exposure.

Remember, a good CEO wants a translator, not an alarm system. They expect no surprises, a clear read on the risks that matter, and a security leader who helps the business move faster rather than slowing it down.

6. Define what a successful relationship should look like and put it in writing

Regardless of the reporting relationship, start by defining the end goal and putting it in writing. It will evolve over time, but having that initial clarity is critical. This is especially important when you’re new in a role and aiming to make your first 60, 90, or 120 days, and your first year, successful. In such cases, it’s essential to align early.

Do that collaboratively, and document it.

7. Prioritize trust and candor

The CEO needs to trust that the CSO isn’t sandbagging, and the CSO needs enough psychological safety to deliver bad news fast. When those conditions exist, security becomes a strategic asset — not a cost center.

To that end, focus on clear communication above all, and present yourself as part of a team, not a solo player. Stay calm under pressure during incidents, and treat people as peers rather than policing them. The leaders who last build trust before they need it.

8. Treat governance as a strategic competitive advantage

The strongest partnerships also share a commitment to governance as a competitive advantage.

Governance is the brakes that let you drive fast safely. When a CSO and CEO are aligned on that principle, the organization can innovate with AI while maintaining oversight and protecting against unnecessary risk. The result is an organization that does not just react to threats but builds resilience into how it operates.

9. Set clear goals and measure progress

Setting clear goals and measuring progress against those goals is essential. When expectations are clear, the areas you need to focus on become much clearer. It doesn’t solve every problem, but aligning early with your leadership, whether that’s a CEO or a CIO, can significantly reduce the pressure you may feel.

Also, never let your boss be surprised. This is where being clear on goals and consistently tracking both leading and lagging metrics becomes especially important, particularly in a direct reporting relationship with the CEO.

10. Be willing to endure challenge and discomfort

Finally, persistence and a willingness to endure discomfort for something that matters more than the pain itself are critical to surviving in this relationship. The role of a cybersecurity leader is often thankless. If you’re doing your job well, no one really notices.

Many enterprises are investing tens of millions in modernizing their SAP landscapes, but are often underestimating a crucial factor for success, the training of their own developers, according to the German-Speaking SAP User Group, DSAG.

The user association urges CIOs to treat the continuing education of SAP developers not as a voluntary training measure but as a strategic investment program in a new report on upskilling [PDF, in German]. Well-trained developers are essential for building stable, maintainable in-house projects without accumulating technical debt, but without continuing education during the upgrade to S/4HANA, the potential of the new technologies will remain untapped, it warned.

Outdated expertise becomes a project risk

As the authors explain, while many ABAP developers have decades of experience with SAP R/3 or ECC and possess extensive process knowledge, development paradigms have fundamentally changed with S/4HANA, Clean Core, and ABAP Cloud.

In the long term, this threatens to lead to poor architectural decisions, time-consuming workarounds, and in-house developments that will need to be maintained with every release, according to DSAG. Many business consultants, too, are still relying too heavily on classic GUI transactions and not taking modern Fiori technologies sufficiently into account.

The result is what the SAP user group refers to as “skills debt.” This debt remains invisible at first but later becomes apparent in the form of longer projects, rising maintenance costs, and a growing dependence on external service providers.

Skill building must start before the project

The DSAG authors view the timing of training as particularly critical. Those who wait until an ongoing S/4HANA migration project is underway to begin building expertise significantly increase the project risk. A lack of knowledge about CDS, RAP, or Fiori leads to architectural decisions that must later be corrected at great expense. At the same time, the necessary learning effort can hardly be managed alongside day-to-day business operations.

But even after the migration is complete, SAP developers must continue their training, according to DSAG. The authors warn that anyone who continues to work as they did on ECC will miss out on the opportunities offered by current SAP technologies — even if everything still works technically. At the same time, they can immediately apply what they’ve learned, which helps solidify their new knowledge.

AI no replacement for developer expertise

While AI tools can generate and explain code, this requires that developers be able to evaluate the results from a technical perspective, and according to DSAG the same applies to development in the SAP environment: “Only those who understand what constitutes good SAP code can use AI as an accelerator,” the authors write. Otherwise, AI acts as a risk amplifier and, in the worst case, merely accelerates the accumulation of technical debt.

The prerequisites for successful AI deployment are solid software engineering knowledge, automated testing, and an understanding of modern SAP development.

DSAG’s five recommendations

DSAG recommends that CIOs firmly integrate continuing education into their transformation strategy with five measures:

• defining mandatory learning paths for different roles, such as ABAP, CAP, or integration developers, as well as business consultants,
• providing suitable sandbox and test environments,
• mandatorily including training time in capacity planning,
• coordinating training schedules with migration and modernization projects, and
• using existing DSAG guidelines as a reference framework for development.

Jobs involving physical labor are at high risk of disruption from automation, with new technologies such as AI robots becoming more prevalent, according to a recent study by the Organization for Economic Co-operation and Development (OECD). That means workers in construction and extraction, farming, fishing, forestry, production and material transportation could be affected by fast-moving technology changes.

“Routine and low-skilled jobs are at higher risk,” the Paris-based public policy group said, adding that “overall, jobs requiring non-routine cognitive, social and creative skills are less susceptible to automation.”

The kinds of creative and cognitive jobs still thought to be less exposed to automation include social work and community service roles. 

OECD also said management jobs — which often require workers to devise creative answers to solve problems — fall within the creative and cognitive category. “While AI has made some high-skill job requirements more susceptible to automation, many critical skills in these roles remain difficult to automate,” OECD said.

The same still holds true for some physical and manual labor jobs – including cleaners, agricultural workers, food-prep assistants, and laborers — which are less exposed to the affects of AI, OECD said.  

But people who work in programming, translating and interpretation positions could find their work affected by the quick rise of AI tools and services. According to the organization, global AI uptake rose from 7% in 2021 to 20% in 2025.

“In these occupations, GenAI could perform a significant share of tasks at least twice as fast today or in the near future,” OECD said.

The effects of the AI boom are not always uniform across industries or regions. In a separate 2026 Employment Outlook study released by the group, exposure to disruption from generative AI (genAI) ranges from about 16% in some areas to more than 70% elsewhere, depending on industries and occupations. 

Numerous research firms have said in recent years that AI is driving short-term job losses, though tech industry experts and analysts have argued AI will also create new careers and jobs as agentic AI takes over low-skilled work. 

AI technology has become so ubiquitous that it’s been compared to electricity — virtually all companies will need it or at least know how to use it. But it’s adoption has been hindered at times as companies struggle to find ROI from its use, and by regulatory and ethical hurdles.

In the US, AI blamed for June job losses

According to a Challenger, Gray & Christmas study released earlier this month, AI was cited as the top reason for job cuts in June. The outplacement firm said employers cut 45,849 job cuts in June, of which 14,029 were attributed to AI, with the tech industry leading the cuts.

“Tech remains the epicenter of this year’s cuts,” Challenger said. “AI is the dominant force as companies are restructuring around it, automating roles, and reallocating budgets toward new capabilities. The sector is being reshaped in real time.”

Overall, AI has been responsible for 173,568 job cuts since 2021, the company said.

AI is hurting jobs in customer service, internal reporting, telecommunications, and hosting automation, said Victor Janulaitis, a staffing consultant who was formerly CEO at Janco Associates Inc.

“C-level executives continue to be focused on eliminating ‘non-essential’ managers, staff, and services,” he said. “Coders and developers have limited opportunities with legacy applications.”

While jobs in the IT sector overall are declining, current hiring tends to skew in the direction of people with AI skills. A report this month by CompTIA put job listings with AI skills at around 500,000, which is close to double the number in January.

“Employers in other industries are accelerating digital transformation initiatives and moving from AI experimentation to implementation,” said Seth Robinson, CompTIA’s vice president for industry research.

That view dovetails with what ManpowerGroup, the recruitment firm, is seeing; demand for AI-related skills has nearly doubled over the past year, said Ger Doyle, regional president of North America at ManpowerGroup.

That growth extends well beyond traditional technology roles as companies move from experimenting to AI deployments at scale, Doyle said. “We’re seeing it influence hiring across occupations ranging from data science and engineering to project management and operational roles,” he said.

Site reliability engineers (SREs) have the tough assignment of resolving thorny performance and reliability issues. But their primary mission is to provide devops teams with operational insights and to suggest implementation improvements on business system performance, security, and overall robustness.

Google introduced its SRE playbook in 2003, but it took some time for the role’s definition, tools, and techniques to become mainstream. Startups were the first to adopt observability for cloud-native applications and create dedicated SRE positions. As tools matured and SRE responsibilities became more clearly defined, larger enterprises assigned SREs to work as a bridge between devops and IT ops teams to improve resilience across a wider range of applications, APIs, and data pipelines.

SRE is a career path for multidisciplinary engineers with strong investigative instincts, sharp data analytics skills, and the temperament to perform under pressure. It has become a critical responsibility as tech became mission-critical for enterprises, and it is a growing role in the genAI era as more businesses deploy AI agents.

But the critical need for resiliency and greater technological complexity brings new challenges for SREs. According to the 2026 State of Production Reliability and AI Adoption report, 44% of respondents experienced an outage linked to ignored or suppressed alerts in the past year, and 35% report their engineers occasionally ignore or dismiss alerts due to alert fatigue. More than 70% of alerts received are not actionable, according to 57% of organizations.

So, is AI making the SRE’s role easier and helping businesses run more reliable technology operations? On the other hand, AI is also driving complexity, as companies deploy genAI tools and AI agents across more business functions and seek to automate more decision-making across operations.

AIops and agentic ops aid SREs

Over the past decade, SRE responsibilities have become somewhat easier through improvements in monitoring platforms, observability practices, tools for centralizing operational data, and AI applied in IT operations (AIops). But during the heat of resolving an outage or performance issue, it’s not easy to correctly identify what system triggered the issue versus other downstream systems impacted by it.

According to the Komodore 2025 Enterprise Kubernetes Report, 79% of production incidents originate from recent system changes, including deployments and changes to compute environments. But the other 21% of incidents stem from issues outside of the business’s control, including network failures, third-party changes, and cloud provider failures.

“SREs using AI capabilities succeed or fail in the moment an incident unfolds, when engineers are deciding what to investigate next,” says Itiel Shwartz, CTO at Komodor. “If the system streamlines root cause detection, connects signals to recent changes, and explains its reasoning in a way engineers recognize, it earns trust. If it adds uncertainty or demands extra validation, it gets sidelined, regardless of how bespoke the model behind it may be. What’s less obvious is what it takes to make AI for SREs work in production, and how different that reality is from prototypes, demos, or early internal builds.”

AIops is not a new capability, especially in using machine learning to correlate logs, metrics, and traces across monitoring and alerting systems. IT service management and SREs have been using AIops to reduce the mean time to resolve incidents and to perform accurate root cause analysis (RCA) efficiently. Agentic ops is the next wave of genAI operational capabilities, including tools for monitoring AI agents, managing their access rights, and detecting AI model accuracy drift.

 “AI is useful during major incidents because it can pull together a lot of context into a few clear sentences, which is exactly what an SRE needs in the moment,” suggests Shani Shoham, chief revenue officer at OpenObserve. “The complexity of architecture and the different tooling make it easier for AI than for a human, but autonomous resolution is still a way off.”

AI’s impact on people and burnout

The business pressure to keep systems up, secure, and performing well is a 24/7 stressful responsibility. According to The SRE Report 2025 from Catchpoint, 36% of SREs often or always experience elevated stress during an incident, and 28% said the stress persists even after the incident is resolved. AI capabilities may prove to be a game-changer in helping SREs avoid burnout and reduce stress.

“AI can improve RCA by taking in a much larger incident context than any engineer can hold at 3am, reasoning across traces, logs, metrics, deploys, config changes, alerts, ownership, and recent production behavior,” says Noam Levy, founding engineer and field CTO at Groundcover. “Beyond attempting a full RCA, its immediate value is distilling the signals that actually matter, reconstructing a clear timeline of cause and effect, and helping engineers separate correlation from likely causality. Once a fix is deployed, agents can also verify remediation by comparing pre- and post-fix behavior, but this depends on broad access to rich, correlated production signals and a cost model that does not discourage adoption or experimentation.”

Not only are incidents resolved faster and with less stress, but AI can also free up SRE time to focus on proactive work and create a career path for junior developers into SRE roles. Quais Taraki, CTO at EDB Postgres AI, adds, “AI reduces toil by automating repetitive tasks while accelerating incident resolution through copilots that correlate signals across distributed systems, allowing SREs to focus more on resilience strategies like chaos engineering and failure analysis.”

AI can have long-lasting operational impacts, especially for organizations looking to deploy more mission-critical technology and AI capabilities. Two longer-term benefits of AI for SREs are reducing the number of bridge calls needed for incident response and the number of engineers required in “war rooms” to coordinate root cause analyses.

“When something goes wrong, AI that guides SREs can do the full analysis, get to the root cause, and perform the remediation,” says Spiros Xanthos, founder and CEO of Resolve AI. “AI also helps avoid many escalations, and when escalations are needed, it targets the right people from the network, infrastructure, and the application teams. AI for SREs centralizes operational intelligence, exposes tribal knowledge, and can guide more junior developers.” 

AI agent reliability

While AI capabilities have been a net positive in helping SREs improve system reliability, the growth of AI code generators, vibe coding, and spec-driven development is adding to their workloads. According to one study, 41% of all global code is now AI-generated, and Gartner predicts that 40% of new enterprise production software will be created using vibe coding techniques by 2028.

But coding velocity is creating new issues for SREs as AI pull requests have 1.4 times more critical issues and 1.7 times more major issues, according to CodeRabbit. “AI-assisted development has created an unprecedented velocity of code reaching production, expanding surface area, edge cases, and failure rates faster than traditional SRE practices can absorb,” says Vinod Jayaraman, cofounder and CTO at NeuBird AI. “The speed of shipping has far outpaced the speed of understanding what breaks in production. To close this loop, SREs need enterprise agents that can capture precise diagnostic context, including correlated traces, service dependencies, and anomaly timelines, and structure it as actionable input for the engineers and AI coding tools responsible for the fix.”

The growing number of AI agents deployed to production creates new challenges. AI agents are not just code; they have multiple failure points. They are built using language models, connect to proprietary sources for context, and integrate with Model Context Protocol servers to support more complex workflows. Changes are ongoing and not deployment events, so the SRE’s job of identifying the source of performance and accuracy drifts isn’t trivial. 

“Traditional SRE was built for systems that fail in reproducible ways, but agents fail differently and drift when a model provider pushes an update, and behavior shifts silently with no baseline for comparison,” says Mohammed Aboul-Magd, vice president of product at SandboxAQ. “Most organizations can’t even answer the basics: how many agents are running, what they have access to, and whether they’re still doing what they were built to do.”

“Every time a senior engineer leaves, they take years of learned failure patterns with them, and the next outage starts from square one,” adds Ronak Desai, cofounder and CEO at Ciroos. “Using AI for compounding operational memory changes that, and every incident your system resolves, the AI learns it.”

SREs should take a leadership role in emerging best practices, including defining their standards for AI agent non-functional acceptance criteria, observability practices, and release-readiness criteria. SREs should update their service-level objectives (SLOs) and define error budgets for AI agents in production.

Ryan Downing, vice president and CIO of enterprise business solutions at Principal Financial Group, says, “Standard SLOs and error budgets give teams the guardrails, and AI helps interpret the telemetry against those targets, reducing noise so engineers can get to the real issue faster and automate parts of remediation before customers are impacted.”

AI raises the SRE’s business impact

The more dramatic shift in site reliability engineering is an evolution of its business scope. IT leaders focus on uptime, performance, and issue resolution, as well as understanding their impacts. Business leaders will look to IT and SREs to identify, determine root cause, and remediate a broader class of issues, including rogue AI agents and the impacts of rapidly deploying new agentic capabilities. 

“AI agents are handing SREs categories of problems they’ve never had to solve before, specifically failures defined in business terms, not technical ones,” says Blake Sherwood, distinguished technologist for AI and platform strategy at Smarsh. “Traditional reliability engineering is built around latency, errors, and crashes, but agents now fail due to skipped compliance steps or outcomes that looked fine technically but were wrong contextually. Most SRE teams aren’t wired for that yet.”

The question is whether SREs with AI-augmented tools can keep up with the velocity, complexity, and business urgency of deploying new AI business capabilities.

Providers and deployers of AI systems: You only have a couple of weeks left until you must explicitly inform users when they are interacting with AI content.

To assist in the effort, the European Commission (Commission) has published guidelines to help AI deployers get in line with the AI Act’s transparency obligations, which will begin to go into effect on August 2.

After that, companies providing AI systems must alert users when they are interacting with AI. They must also tell users when they have been exposed to deepfakes, “emotion recognition,” or biometric categorization systems, or when they are given AI-manipulated content in matters of “public interests without human review or editorial control.”

Henna Virkkunen, the Commission’s executive VP for tech sovereignty, security and democracy, said in a statement, “with today’s guidelines, the Commission supports the smooth and effective application of the AI Act to make AI systems interacting with people such as chatbots and AI agents and AI content more transparent and trustworthy. These guidelines support providers and deployers in meeting their obligations under the AI Act, while helping citizens know when they are interacting with AI.”

Systems must include machine-readable markers to reveal such content, to reduce “the risk of deception and manipulation” and build public trust in AI.

“Generative systems have collapsed the cost of producing convincing content while the cost of judging it stands where it always stood,” said Sanchit Vir Gogia, chief analyst at Greyhound Research. This requirement is “an attempt to restore friction to that imbalance.”

A company’s non-compliance could result in fines anywhere from €750K (about $856K) to €15M (about $17 million), or even up to 3% of its total worldwide annual revenue.

Transparency requirements

The EU AI Act’s transparency requirements apply to “natural or legal persons,” public authorities, agencies, or other bodies that develop AI systems, or have them developed, and place them on the EU market or into use under their name or trademark. This means all companies, regardless of whether or not they are EU-based.

“Systems placed on the European market, put into service there, or producing outputs used there are inside the field, wherever the developer sits,” Gogia noted.

Applicable systems must be intended to interact directly with “natural persons”; these systems include AI-enabled chatbots or conversational agents, AI companions, or coding agents. However, AI-enabled tools like recommender systems, spam filters, authentication, search and retrieval, transcription, text and code auto-completion, or predictive maintenance do not fall under the rule.

Specific outputs such as AI-generated text, images, video, and audio must contain a machine-readable mark. Deepfakes and public interest-related text created by AI without human review or control must be clearly labeled, however, deepfake content that is “artistic, creative, satirical, or fictional” is largely exempt.

AI content must be marked with one of three labels: “AI,” “Fully AI-generated,” or “Partially AI-modified.” For instance, “Fully AI-generated” applies when news summaries, music, art, or videos have been created without any human oversight (apart from prompting), while “partially AI-modified” could mean a person’s face is swapped into an authentic photograph to create a deepfake.

The three icons are publicly available for free use; enterprises can download zip files in PNG and SVG formats.

Most of the Act’s transparency rules begin to go into effect on August 2. But AI systems placed on the market before then will have some leeway; they must be in compliance by December 2.

However, a four-month allowance “on one obligation, for one population of systems, contingent on one procedural step, is not a strategy,” Gogia emphasized. Enterprises should plan to comply by August 2 and “treat any relief that arrives as margin.”

A consistent code of practice

Along with the transparency guidelines, the Commission has introduced a code of practice that essentially serves as a gesture of good faith. When signed, it can provide “legal certainty” and a “simple and practical” way to demonstrate compliance with the AI Act, according to the Commission. Signatories can also collaborate through the ‘Signatory Taskforce,’ which will share practices and advance technologies around marking and labeling practices.

Providers that choose not to sign must comply through other methods and demonstrate that those methods are “adequate” through assessment by surveillance authorities, according to the Commission.

Non-signatories “keep their flexibility, and will face more case-by-case scrutiny for it,” said Gogia.

Criteria for compliance

Shashi Bellamkonda, principal research director at Info-Tech Research Group, pointed out that the transparency requirements apply to content only when three criteria are met: It has been published, is informative to the public, or is on matters of public interest.

B2B business content or blogs may not need an AI disclosure if they do not meet these criteria, he noted. Also, published text that has undergone human review or is under editorial control does not need to be labeled. Editorial control means that a person must hold the ultimate legal responsibility for the publication of the content.

Many companies like Google, Adobe, and LinkedIn have already established ways to identify images marked as AI-generated. Meta has made it a requirement, but the creator has to add the AI-generated label, Bellamkonda said.

“This is a good move for guardrails around public information, and companies with good compliance and ethical oversight may not have to worry about this,” he noted. But as a general practice, companies should disclose AI-generated content and state whether it has been human reviewed.

Creating a transparency pipeline

Establishing full transparency means identifying who carries the responsibility for the content, whether the marking survives real use, not just testing, and what evidence will defend the decision, Gogia said.

Concerns cluster around responsibility, durability and evidence. Several organizations usually touch one piece of content, and none controls the whole chain, which is why contracts become the “pressure point,” he said. Most current agreements were written to deliver software and say “almost nothing” about provenance persistence, verification access, or evidence retention.

The durability concern is the most difficult, Gogia noted, because marking performs well in controlled settings but “badly in ordinary life.” Meta, for one, said its invisible watermark was designed to survive cropping; a published test, however, found the company’s preview detector missed 55% of cropped images.

“CIOs should ask which platform can actually provide evidence before believing its dashboard,” said Gogia.

Disclosure of AI use must be “clear, distinguishable and accessible,” he emphasized. “A notice buried in lengthy terms, or reachable only through determined clicking, satisfies nobody, least of all a market surveillance authority.”

Sustained compliance is a “living control” requiring a central record of systems, duties and evidence; testing taking place where the user meets the control rather than where the developer built it; and continuous supplier assurance. Enforcement will vary by country, so keep one common baseline with local overlays, Gogia said.

His advice: Inventory every system that talks to people, generates content, or gauges sentiment; classify provider and deployer roles; place disclosures at first interaction; define substantive human review; keep the evidence.

Marks and provenance signals should be tested after content undergoes cropping, compression, translation, transcription, and other editing, Gogia said. A useful audit starts from a real output and follows its “pulse” through generation, editing and publication, identifying at “each beat” the responsible party, the surviving mark, and evidence for exceptions. Missed labels should also be traced for root cause and recurrence.

To ensure compliance, before August 2, enterprises need a prioritized inventory, live disclosures on the highest-risk use cases, and a “named owner for every control,” he noted. In the first 30 days, they should stabilize and test; in the first 90 days, push requirements into procurement processes as a standing discipline. Procurement must secure commitments on marking methods, known failure modes, and evidence access, with explicit notice if/when any of them change.

“The sensible architecture is a common transparency baseline carrying traceability, responsibility, and evidence, with jurisdictional overlays for language, sector rules, and local practice,” Gogia said.

This article originally appeared on CIO.com.

Providers and deployers of AI systems: You only have a couple of weeks left until you must explicitly inform users when they are interacting with AI content.

To assist in the effort, the European Commission (Commission) has published guidelines to help AI deployers get in line with the AI Act’s transparency obligations, which will begin to go into effect on August 2.

After that, companies providing AI systems must alert users when they are interacting with AI. They must also tell users when they have been exposed to deepfakes, “emotion recognition,” or biometric categorization systems, or when they are given AI-manipulated content in matters of “public interests without human review or editorial control.”

Henna Virkkunen, the Commission’s executive VP for tech sovereignty, security and democracy, said in a statement, “with today’s guidelines, the Commission supports the smooth and effective application of the AI Act to make AI systems interacting with people such as chatbots and AI agents and AI content more transparent and trustworthy. These guidelines support providers and deployers in meeting their obligations under the AI Act, while helping citizens know when they are interacting with AI.”

Systems must include machine-readable markers to reveal such content, to reduce “the risk of deception and manipulation” and build public trust in AI.

“Generative systems have collapsed the cost of producing convincing content while the cost of judging it stands where it always stood,” said Sanchit Vir Gogia, chief analyst at Greyhound Research. This requirement is “an attempt to restore friction to that imbalance.”

A company’s non-compliance could result in fines anywhere from €750K (about $856K) to €15M (about $17 million), or even up to 3% of its total worldwide annual revenue.

Transparency requirements

The EU AI Act’s transparency requirements apply to “natural or legal persons,” public authorities, agencies, or other bodies that develop AI systems, or have them developed, and place them on the EU market or into use under their name or trademark. This means all companies, regardless of whether or not they are EU-based.

“Systems placed on the European market, put into service there, or producing outputs used there are inside the field, wherever the developer sits,” Gogia noted.

Applicable systems must be intended to interact directly with “natural persons”; these systems include AI-enabled chatbots or conversational agents, AI companions, or coding agents. However, AI-enabled tools like recommender systems, spam filters, authentication, search and retrieval, transcription, text and code auto-completion, or predictive maintenance do not fall under the rule.

Specific outputs such as AI-generated text, images, video, and audio must contain a machine-readable mark. Deepfakes and public interest-related text created by AI without human review or control must be clearly labeled, however, deepfake content that is “artistic, creative, satirical, or fictional” is largely exempt.

AI content must be marked with one of three labels: “AI,” “Fully AI-generated,” or “Partially AI-modified.” For instance, “Fully AI-generated” applies when news summaries, music, art, or videos have been created without any human oversight (apart from prompting), while “partially AI-modified” could mean a person’s face is swapped into an authentic photograph to create a deepfake.

The three icons are publicly available for free use; enterprises can download zip files in PNG and SVG formats.

Most of the Act’s transparency rules begin to go into effect on August 2. But AI systems placed on the market before then will have some leeway; they must be in compliance by December 2.

However, a four-month allowance “on one obligation, for one population of systems, contingent on one procedural step, is not a strategy,” Gogia emphasized. Enterprises should plan to comply by August 2 and “treat any relief that arrives as margin.”

A consistent code of practice

Along with the transparency guidelines, the Commission has introduced a code of practice that essentially serves as a gesture of good faith. When signed, it can provide “legal certainty” and a “simple and practical” way to demonstrate compliance with the AI Act, according to the Commission. Signatories can also collaborate through the ‘Signatory Taskforce,’ which will share practices and advance technologies around marking and labeling practices.

Providers that choose not to sign must comply through other methods and demonstrate that those methods are “adequate” through assessment by surveillance authorities, according to the Commission.

Non-signatories “keep their flexibility, and will face more case-by-case scrutiny for it,” said Gogia.

Criteria for compliance

Shashi Bellamkonda, principal research director at Info-Tech Research Group, pointed out that the transparency requirements apply to content only when three criteria are met: It has been published, is informative to the public, or is on matters of public interest.

B2B business content or blogs may not need an AI disclosure if they do not meet these criteria, he noted. Also, published text that has undergone human review or is under editorial control does not need to be labeled. Editorial control means that a person must hold the ultimate legal responsibility for the publication of the content.

Many companies like Google, Adobe, and LinkedIn have already established ways to identify images marked as AI-generated. Meta has made it a requirement, but the creator has to add the AI-generated label, Bellamkonda said.

“This is a good move for guardrails around public information, and companies with good compliance and ethical oversight may not have to worry about this,” he noted. But as a general practice, companies should disclose AI-generated content and state whether it has been human reviewed.

Creating a transparency pipeline

Establishing full transparency means identifying who carries the responsibility for the content, whether the marking survives real use, not just testing, and what evidence will defend the decision, Gogia said.

Concerns cluster around responsibility, durability and evidence. Several organizations usually touch one piece of content, and none controls the whole chain, which is why contracts become the “pressure point,” he said. Most current agreements were written to deliver software and say “almost nothing” about provenance persistence, verification access, or evidence retention.

The durability concern is the most difficult, Gogia noted, because marking performs well in controlled settings but “badly in ordinary life.” Meta, for one, said its invisible watermark was designed to survive cropping; a published test, however, found the company’s preview detector missed 55% of cropped images.

“CIOs should ask which platform can actually provide evidence before believing its dashboard,” said Gogia.

Disclosure of AI use must be “clear, distinguishable and accessible,” he emphasized. “A notice buried in lengthy terms, or reachable only through determined clicking, satisfies nobody, least of all a market surveillance authority.”

Sustained compliance is a “living control” requiring a central record of systems, duties and evidence; testing taking place where the user meets the control rather than where the developer built it; and continuous supplier assurance. Enforcement will vary by country, so keep one common baseline with local overlays, Gogia said.

His advice: Inventory every system that talks to people, generates content, or gauges sentiment; classify provider and deployer roles; place disclosures at first interaction; define substantive human review; keep the evidence.

Marks and provenance signals should be tested after content undergoes cropping, compression, translation, transcription, and other editing, Gogia said. A useful audit starts from a real output and follows its “pulse” through generation, editing and publication, identifying at “each beat” the responsible party, the surviving mark, and evidence for exceptions. Missed labels should also be traced for root cause and recurrence.

To ensure compliance, before August 2, enterprises need a prioritized inventory, live disclosures on the highest-risk use cases, and a “named owner for every control,” he noted. In the first 30 days, they should stabilize and test; in the first 90 days, push requirements into procurement processes as a standing discipline. Procurement must secure commitments on marking methods, known failure modes, and evidence access, with explicit notice if/when any of them change.

“The sensible architecture is a common transparency baseline carrying traceability, responsibility, and evidence, with jurisdictional overlays for language, sector rules, and local practice,” Gogia said.

Security operations centers (SOCs) have spent years struggling under the weight of growing alert volumes, expanding attack surfaces, and chronic staffing shortages. Now artificial intelligence is adding a new complication: not just more information, but more machine-generated information that must itself be evaluated.

“There is an asymmetry here because you now have to parse through a lot of AI slop to get to, ‘Okay, is this real or not?’” Fernando Montenegro, vice president and practice lead at The Futurum Group, tells CSO.

His observation captures a growing concern among security leaders. AI is helping attackers and defenders move faster, but it is also creating new forms of cognitive burden for the humans tasked with separating signal from noise.

As AI accelerates vulnerability discovery and enables more automated reconnaissance and exploitation, defenders are increasingly responsible for overseeing systems whose outputs can be difficult to interpret or verify. The challenge is not simply more work. It is that the volume, speed, and complexity of that work are increasing simultaneously.

Yet experts who study and advise SOCs reject the idea that collapse is inevitable. Instead, they describe an industry entering a difficult transition that could reshape how security teams operate and how humans and machines share responsibility for defense.

The vulnerability surge is exposing years of security debt

One of the most immediate concerns is the possibility that AI dramatically increases the number of vulnerabilities organizations must identify and remediate.

Chris Crowley, a longtime cybersecurity instructor and SOC expert, argues that organizations are facing the consequences of years of accumulated technology debt.

“A lot of what we’re going to have to account for in the next couple of years is a technology debt of vulnerable software that has been deployed because it’s good enough to solve the problem, but then there are all these latent cyber issues, flaws, vulnerabilities that weren’t discovered prior to deployment,” he tells CSO.

AI-assisted vulnerability discovery has the potential to expose those weaknesses at a pace defenders have never experienced before.

“The compression of work that is being dropped on us is unprecedented,” Crowley says. “We’ve just been ignoring it for decades.”

He does not believe AI will necessarily create entirely new classes of vulnerabilities. Instead, he expects defenders to confront much larger volumes of familiar problems.

“We’re going to have 100 of these simultaneously,” he says, referring to the kinds of high-priority vulnerabilities security teams traditionally handle one at a time.

The AI challenge for many SOCs may be less a novelty problem than a volume problem. Security teams already know how to patch systems, prioritize remediation, and respond to critical exposures. What changes is the scale and speed at which those demands arrive.

Organizations with mature patching, prioritization, escalation, and response processes may struggle but adapt. Organizations that have treated security operations as a bare-minimum compliance function may find themselves overwhelmed.

For CISOs, Crowley says, that means treating “patch now” less as an occasional emergency state and more as a permanent operating posture. As AI accelerates vulnerability discovery, the distinction between routine maintenance and crisis response may continue to blur.

He compares the situation to disaster recovery planning. Organizations that wait until a crisis arrives to establish staffing plans, escalation paths, and remediation processes may discover there is not enough help available.

Cognitive overload may become the defining challenge

While vulnerability discovery receives much of the attention, Montenegro believes security leaders need a broader framework for understanding AI’s impact.

Organizations should think about AI through three lenses, he says: security for AI, AI for security, and security from AI. The first involves protecting AI systems. The second involves using AI to improve defensive operations. The third asks what happens when adversaries use AI against the organization.

For SOCs, all three categories are beginning to overlap.

As AI makes it easier to create reports, assessments, vulnerability submissions, and other operational artifacts, humans remain responsible for determining whether that information is accurate and useful.

“It becomes much easier to generate content,” Montenegro says, “but if you’re going to review that content as a human, the onus on you now is that much larger.”

The result is a new form of cognitive overload. Security professionals may spend increasing amounts of time evaluating machine-generated information instead of conducting higher-value security work.

Organizations can increasingly use AI to summarize reports, evaluate alerts, and assist with investigations, but humans remain responsible for validating the results.

“We’re not at the stage yet where people are comfortable” handing off critical decisions entirely to AI, he says.

That leaves defenders caught between two competing realities: AI is creating more information to process, but AI is also becoming one of the few viable tools for managing that growing workload.

For Montenegro, the principle should be to automate tasks, not roles. AI can absorb repetitive investigative steps, but organizations should be cautious about removing humans from the process entirely.

The risk, he says, is that if organizations hide too much complexity behind automated outputs, analysts may lose opportunities to develop the domain knowledge needed to advance.

“How is that professional who is reacting to those alerts growing as a professional?” he says.

The gap between mature and struggling SOCs may widen

Not every organization will experience the impact of AI in the same way.

John Hubbard, senior cybersecurity consultant and SANS instructor, believes the industry’s response will largely depend on how well organizations have prepared for operational stress before AI arrives at scale.

“I would roughly break security operations teams into two camps,” Hubbard tells CSO. “There are the ones that are definitely struggling, are already overwhelmed. And then some are doing really well.”

The struggling organizations tend to be understaffed, underfunded, undertrained, or dependent on ad hoc processes. Every incident feels different, forcing teams to improvise under pressure.

“Getting hit with something like this can certainly be an accelerant for burnout if they weren’t already experiencing it,” Hubbard says.

By contrast, mature security teams have already invested in processes, training, exercises, and automation. “The teams that are doing a really solid job now are probably not super overwhelmed because they’ve developed the processes and procedures to be ready for this kind of thing,” Hubbard says.

He compares successful SOCs to fire departments. Firefighters cannot predict exactly where the next emergency will occur, but they know how to respond because they have rehearsed those responses repeatedly.

“The teams that kind of can react like a fire department are the ones that are getting it right,” he says.

Those organizations conduct tabletop exercises, adversary emulation exercises, red-team assessments, and incident response drills. As a result, they can absorb additional workload without descending into panic.

Burnout remains the industry’s most difficult problem

Despite widespread concern about AI-enabled attacks, none of the experts view AI solely as a threat. Several argue that AI will become essential for helping defenders cope with the challenges it creates.

Jose-Marie Griffiths, president emerita and former CIO of Dakota State University, believes AI can help security teams sift through overwhelming volumes of information and identify the signals that matter most.

“People who work in SOCs are now seeing overwhelming volumes of data, and they’re getting fatigued,” Griffiths tells CSO.

AI can help automate portions of analysis, validate alerts, and improve visibility into complex environments. But Griffiths cautions that some AI-assisted vulnerability discovery tools are also producing large numbers of false positives.

That matters because false positives do not eliminate work. They create it. As organizations confront escalating volumes of findings, distinguishing genuine risk from erroneous results may become as important as discovering vulnerabilities in the first place.

The experts agree that technology alone will not determine outcomes. People will.

Crowley argues that cybersecurity professionals must recognize that uncertainty is intrinsic to the profession. “We are the group that deals with uncertainty,” he says. “That’s really and truly what cybersecurity is.”

That reality places responsibility on both individuals and organizations. Analysts need mechanisms for managing stress. Teams need to recognize when colleagues are approaching their limits. Managers need to establish healthy escalation practices and realistic expectations.

Hubbard rejects the notion that burnout is inevitable.

“It is not a foregone conclusion that security operations jobs have to be a painful grind that everyone hates,” he says.

He has seen organizations where employees remain engaged for years because leaders actively manage workload, create supportive cultures, and encourage open communication.

That includes making it safe for analysts to admit when they have reached their limits. “If people are unwilling to say, ‘I’m maxed out right now, and I’m going crazy,’ that’s going to be the thing that breaks a lot of teams,” Hubbard says.

Pay alone may not solve the problem. Crowley pointed to SANS/SOC survey findings showing that compensation ranked fourth among retention factors, behind meaningful work, training, and professional development.

The future SOC may look very different

Griffiths believes organizations will need to respond not only with better technology but with structural changes. Traditional tiered SOC models may need to evolve into more collaborative teams with diverse expertise working together in real-time.

“I think we’re going to have to eliminate the hierarchies a little bit and have teams of people with different expertise working together,” she says.

She also argues that organizations should invest in human expertise rather than simply increasing AI consumption. “Buy engineers, not tokens,” she says.

Professional networks and peer support will matter as much as any tool, Griffiths says, because defenders need trusted communities where they can compare notes, share practices, and avoid facing sustained pressure in isolation.

If there is a consensus emerging among experts, it is that AI is exposing weaknesses that already existed.

The staffing shortages, alert fatigue, burnout, and process failures affecting SOCs did not begin with generative AI. AI is simply amplifying them.

At the same time, AI is providing new tools that may help organizations manage those very challenges.

The future SOC may spend less time manually triaging alerts and more time validating automated findings, conducting threat hunting, and making strategic decisions. Human expertise may increasingly be paired with AI systems that act as operational partners.

The transition will not be painless. Some teams will struggle. Some practitioners may leave the field. Others will adapt and thrive.

“In a way,” Griffiths says, “we’re turning the whole SOC inside out.”

Montenegro sees the transition as a cybersecurity version of the Red Queen effect: defenders and attackers must keep running simply to stay in place.

Borrowing from science-fiction author William Gibson, Montenegro offered perhaps the simplest description of the industry’s current moment: “The future is already here. It’s just unevenly distributed.”

For security leaders, that future is arriving in the form of AI-generated vulnerabilities, AI-assisted investigations, and AI-enabled adversaries. The question is no longer whether security operations centers will change. It is whether organizations can adapt quickly enough to keep pace.

We are inundated with promises that artificial intelligence will save us and that the next governance framework will protect us. Buy this platform, adopt that model and the hard part finally gets easier. After 15 years in this field, I have wanted that shortcut as much as anyone.

But both promises are downstream of something neither one can produce. You cannot automate trust between two people. You cannot govern your way to a relationship. As AI moves into the core of how organizations operate, and accountability stops mapping cleanly to the org chart, what holds when the stakes are highest is not the platform or the policy. It is two human leaders who know each other well enough to carry the weight together.

I think about this often now, a year after publishing a book about the pressures bearing down on security leaders, “The CISO On The Razor’s Edge: Leading Cybersecurity When The System Is Designed To Break.” The partnership between the CIO and the CISO is the last human relationship in cybersecurity. AI raises the stakes. Governance sets the floor. The relationship is what holds.

I saw it work once, up close. When I worked in Washington State, the CIO, Bill Kehoe, talked to his CISO, Ralph Johnson, every day. Weekends included. Not because a policy required it, but because the mission did. That partnership is a large part of why the role stayed sustainable for them when it broke so many others.

The promise we keep believing

Walk any conference floor and you will hear the same pitch in a hundred variations. The next AI layer will close the gap. The next framework will lock down the risk. The technology is usually ready. The organization is not. I have watched too many well-funded programs stall to still believe the tool is the answer, and almost every time, the breakdown traced back to leaders who were not aligned before the work began. A framework run by misaligned leaders inherits the misalignment. You can buy the best controls on the market and still watch them fail when two leaders work from different assumptions about who owns what.

Bill and Ralph understood this. Security decisions were not handed to Ralph after the fact to bless or block. They were made with him, inside the technology decisions, because the two had already agreed on what mattered. That is not governance. That is leadership creating the conditions in which governance can work.

It is the real lesson I came to in the book. Technical knowledge matters, but it is not enough. As I wrote then, “Influence, trust and internal relationships are non-negotiable.” Without influence, CISOs cannot lead. Without technical substance, they cannot prioritize what matters. And without partnership, especially with their CIO, “they’re operating without a safety net.”

AI does not change that truth. It raises the cost of ignoring it.

When decisions move at machine speed

The ground under both roles is shifting. Work no longer flows through people alone. It moves across people, platforms, partners and agents at the same time, and it moves fast. Decisions that once waited for a meeting now form in seconds. The org chart, built for an era when humans did the work and reporting lines explained accountability, struggles to keep up.

This is where the partnership stops being a nicety and becomes infrastructure. When decisions form at machine speed, the human escalation path has to be instant. There is no time to negotiate a relationship in the middle of an incident. Either the trust is already there, built in the quiet stretches before anything goes wrong, or it is not there when it counts.

I asked Bill what he would lose if his daily calls with Ralph dropped to once a week. His answer cut straight to it.

“Cyber does not rest,” he told me. “It is active and dynamic and requires 24/7/365 attention.” Drop to a weekly check-in, he explained, and “I am treating the CISO like any other executive position.” For Bill, AI only raises the stakes on that daily contact. “Relationships and partnerships between the CIO and CISO will never die due to AI,” he said. “I can’t even imagine a scenario where I don’t talk to my CISO on a daily basis including weekends to discuss the latest risks and vulnerabilities or news on potential AI attacks.”

That is the point most of the market misses. A platform can flag the anomaly. It cannot decide what the organization is willing to risk, who carries that decision or how two leaders stand behind it together. The faster the machines move, the more the partnership has to already be in place.

The loneliest seat in the building

There is a reason some now call the CISO job the least desirable role in business. The seat carries enormous accountability and rarely the authority to match. As one security leader put it, the pressure has never been higher and the control has never felt lower. People are burning out and walking away from a role that has never mattered more.

Here is the hard part. There is no log file for burnout. No alert fires when the weight finally exceeds the leader. That drain is invisible right up until it is not, and it raises organizational risk as surely as any unpatched system. The structural fixes the industry debates are all real and all slow.

The fastest source of relief available to a CISO is not a framework. It is a CIO who treats the relationship as a daily partnership rather than a line on a chart. An isolated CISO is a vulnerability. A partnered one is an asset.

You see what that partnership is worth in the worst moment. I asked Bill what it looks like when an incident hits and public trust is on the line. He did not reach for a tool.

“I am accountable as CIO to everything that occurs in the state from a technology lens including cyber,” he said. When a severe incident hits, the call comes to him from agency leadership or the Governor’s Office. Then he follows the plan, but never alone: “I will be in constant contact with the CISO on the details of the incident.”

That is the safety net made real. The CISO is not carrying the mission alone at the moment it matters most. On the razor’s edge, leadership keeps you upright. Partnership keeps you in the fight.

The work no tool will do for you

In my advisory work, I sit with C-suite leaders who share values and still cannot find alignment. The barrier is rarely disagreement. It is that they are not communicating clearly or often enough to build the trust that alignment requires. I have watched negotiations that could only happen by proxy, over email, because two capable leaders had stopped talking directly.

I recently sat in an hour-long discussion where alignment and shared values were present the whole time. It did not become clear until the final fifteen minutes. That is what real alignment costs: patience, persistence and a stubborn commitment to clarity. If leaders cannot do that work themselves, no AI model or governance tool will do it for them.

This is why I stand up an AI review board for the organizations I work with and host the leadership conversations that decide whether a company’s AI ambitions thrive or stall. The board itself matters less than what it provides: neutral ground, a regular cadence and an agenda that forces the hard issues into the open before a crisis forces them. If your organization has no venue like that, that absence is its own form of dysfunction. The cadence is what makes communication effective. Not easy. Effective.

Build the bond on purpose

You cannot framework your way to trust. But you can build it deliberately, and that is a leadership act, not a governance one. The partnership and stakeholdering skills that once looked like soft extras are now the core executive work. A few moves matter most:

• Set a standing contact rhythm with your counterpart before you need one, daily or near-daily, not quarterly
• Make decision rights and accountability explicit while it is calm, so no one improvises them mid-incident
• Translate security into business outcomes together, so the board hears one aligned voice

Build the relationship as deliberately as you would build any critical control, because that is what it is. If you cannot connect the partnership to outcomes the business actually feels, you have a friendship, not a performance lever.

A year after writing “The CISO On the Razor’s Edge,” I am even more convinced that strong leadership precedes effective governance and partnership precedes them both. This is the good news, not the hard news. The CIO and CISO who build real trust do not just reduce risk. They move faster than their competitors, because they spend no energy fighting each other. They earn the board’s confidence, because the board hears one clear voice. And they unlock the AI strategy everyone else is still struggling to govern, because they have already done the human work that makes governance hold.

That is the upside waiting on the other side of this relationship. AI will keep advancing. Governance will keep maturing. But the organizations that win the next decade will be the ones where two leaders decided the partnership was worth building before they needed it. Bill and Ralph knew it every day, weekends included. The edge is there for anyone willing to do the same.

This article is published as part of the Foundry Expert Contributor Network.
Want to join?

Organizations usually face the same questions concerning XLAs: What should we measure, who owns the data, how should incentives work, and how will this change provider behavior after signature.

There are no easy answers either, but after advising clients in MSP relationships with major providers, I’ve seen what works and what doesn’t. Successful XLA programs rarely start with massive transformation, nor rely on perfection before adding experience accountability to the contract.

Start with the right metrics

The first concern I hear is what to measure. MSPs often steer that discussion toward metrics already in their reporting stack. That’s a trap.

Unlike SLAs, which measure operational outputs, XLAs should focus on employee experience and business outcomes. The strongest programs start with three to five high-signal metrics tied to the employee journeys creating the most friction. More than that and the program loses focus before it gains traction.

I typically recommend starting with employee satisfaction scores, perceived lost productivity time, repeat incident rates, task completion success, and ease of getting support. Then focus early measurement on common employee experiences like service desk interactions, employee onboarding, application reliability, and device performance.

Trying to measure everything is understandable, but it’s also one of the fastest ways to stall an XLA program.

Precisely define roles and responsibilities

This is the part of XLA contract design where I spend the most time with clients, and it’s the part that major MSPs are most likely to leave vague if you let them. Accenture and TCS both have mature commercial teams skilled at agreeing to things in principle while avoiding specific accountability in writing. Don’t let that happen here.

Employee experience isn’t solely the vendor’s responsibility. It’s genuinely shared, which is a more productive framing than pure vendor accountability, but only if the split is clearly spelled out. This is what I’ve found works in practice.

Customer responsibilities

• Selecting tools and platforms
• Managing data infrastructure
• Sharing experience data openly with the provider
• Supporting internal improvement initiatives that the provider flags

Vendor responsibilities

• Running the measurement cadence
• Delivering monthly experience reporting
• Identifying and surfacing improvement opportunities from the data
• Executing operational improvements within agreed timelines

Without this level of specificity, XLA programs almost always become reporting exercises. The data gets collected, the scorecard gets presented, and nothing actually changes.

Build flexible targets

One of the biggest mistakes in XLA design is treating experience targets like traditional SLAs,  setting once at contract signing and left unchanged for years. Employee expectations, workforce patterns, and technology environments, after all, evolve constantly. A target that feels ambitious in year one may become meaningless by year three.

The strongest XLA contracts include formal reviews every three to six months to recalibrate targets, align with business priorities, and raise expectations as experience improves. This prevents providers from locking in easy wins and coasting. When providers resist review cycles, it’s often a sign they believe the targets can be met on autopilot, a red flag in any XLA program.

Use the right scoring method

One overlooked XLA best practice is how experience scores are calculated. Point-in-time scores can be distorted by outages, isolated incidents, or low survey participation, and providers sometimes exploit that volatility.

I advise clients to calculate official XLA scores using rolling two-month averages instead of snapshots. It creates a more stable and accurate view of experience trends, and makes operational timing games much harder. Most importantly, define the scoring methodology explicitly in the contract. Don’t leave it to be worked out operationally after signing.

Structure incentives carefully

Relying on penalty-only incentives is one of the most expensive XLA mistakes. On paper, the model is simple: miss the target, pay the penalty. In practice, it drives the wrong behavior. Providers focus on protecting themselves instead of improving employee experience, optimizing survey timing, and managing averages rather than solving problems collaboratively.

I’ve seen this repeatedly in Infosys, HCL, and TCS relationships. The strongest XLA structures combine risk and reward where providers earn meaningful upside for exceeding targets, innovating, and improving outcomes. Penalties still matter, especially in mature programs, but they can’t be the only lever otherwise the contract becomes another SLA model with better branding.

Define escalation processes

When experience scores fall below threshold, the contract needs to specify what happens next. This sounds obvious, but I’ve reviewed many service delivery measurement frameworks in clients’ incumbent MPS contracts that specify financial consequences without defining any collaborative process to address the underlying problem.

The escalation language I push clients to include specifies:

• a joint review process triggered when scores fall below threshold.
• root cause analysis expectations and timelines.
• remediation planning requirements with named owners on both sides.
• timelines for corrective action and progress reporting.

The framing matters as much as the mechanics. Escalation should be positioned as collaborative problem-solving, not blame assignment. Contracts that turn every missed score into a commercial dispute damage the relationship when provider engagement matters most. The best MSPs treat escalation as a shared diagnostic exercise, not a contractual confrontation.

Establish an operating rhythm

Signing the contract is the beginning, not the end. In my experience, the organizations that get the most out of XLA programs are those that build a disciplined operating cadence and stick to it. The ones that treat XLAs as a reporting exercise almost never see meaningful improvement.

This is the cadence I recommend:

Daily: Both parties maintain live dashboards showing experience trends, application performance, regional issues, and persona-specific insights to catch emerging issues.

Weekly: Customer and vendor teams hold focused working sessions to determine what improved experience this week, what hurt it, which remediation actions were completed, and what’s the priority for next week.

Monthly: Formal governance meetings to review experience scores, improvement actions, root cause discussions, and cross-functional issues that need escalation.

Biannually: Leadership steering meetings to assess overall experience performance, recalibrate targets, and align the XLA program with evolving business priorities to honestly evaluate whether or not the program is driving the outcomes the organization actually cares about.

Common mistakes organizations make

After working through XLA design and implementation with clients across their MSP relationships, the failure modes are predictable. Here’s what to watch for.

Setting targets before establishing a baseline
Rushing into targets before understanding your current state is one of the fastest ways to create disputes. Spend the first three to six months gathering baseline data, then negotiate targets based on evidence rather than guesswork.

Measuring too much
More metrics don’t create more insight. Frameworks with 20 data points rarely survive operational reality. Start focused and expand gradually.

Hiding the data
Transparency is foundational to XLAs. Providers who obscure poor scores, especially when controlling the measurement platform, undermine the entire model. Clients who weaponize the data create the same problem. Build mutual transparency obligations into the contract.

Over-relying on penalties
Penalty-only structures recreate legacy SLA behaviors. Balanced incentives drive better long-term outcomes.

Treating XLAs as static
Employee expectations, technology, and business priorities evolve constantly. Without formal review cycles, XLA programs quickly become irrelevant.

Start smaller than you think you need to

The organizations that get XLAs right are rarely the ones with the most sophisticated tooling. They’re the ones that stopped waiting for a perfect program and introduced real accountability into the contract with what they had.

The most effective starting points are often simple: agree on a focused set of experience metrics, establish a six-month review cycle, commit to shared visibility and data transparency, and create joint accountability for continuous improvement.

From there, maturity develops over time. Governance builds trust, data becomes more actionable, and targets evolve alongside business priorities. The relationship shifts from compliance management to outcome-driven partnership.

In my experience, the organizations that succeed are the ones that stopped accepting green scorecards at face value and demanded something more meaningful.

Once upon a time, there were actual arguments as to whether CIOs should be business people, not technology people. With any luck, these arguments were stomped out back here: “The case against the ‘business-savvy CIO’” — which drove the arguments for this false dichotomy into the ground back in 2018.

Some complications have arisen in the near decade since, so I’m afraid we need to revisit the subject — especially as the most recent of this has made the drumbeat for business-savvy CIOs that much louder.

One source of this need was the case of the dreaded Digital adjectival abuse, also known as “Digital as a Noun.”

Digital was a big deal back in the pre-COVID era. It matters here because for Digital to work, business leaders needed to be technologists, not just business people.

As business leaders became better technologists, CIOs needed to keep up on the business potential for the various Digital technologies their business leader friends were suddenly asking for.

Another source of confusion was COVID itself, and the discovery it led to on the part of those business executives not already convinced that the entire business ran on IT, and that any area that still relied on manual processes should be presumed incompetent. Rather than insisting on a full-blown ROI to justify automating a function, those relying on manual methods were (or should have been) asked to justify this choice.

AI changes the equation

But as tendentious or tectonic as those shifts might have seemed at the time, AI is raising the now-what-do-I-do? equation to new heights.

That’s because CIOs are now being given a new set of alternatives:

• Whether they want to be business people after all;
• Whether they should become or remain classical business/technologists;
• Or, should they set their sights on becoming AI business/technologists.

You might have noticed an emerging trend in IT: The proliferation of articles about AI whose content even many tech-savvy CIOs can’t make heads or tails of.

And no, the problem isn’t that their texts include a bunch of unfamiliar buzzwords.

Much of the offending content is rooted in unfamiliar concepts, not vocabulary changes.

Or, even more frustrating, the puzzlement sometimes lies in familiar buzzwords whose meaning has changed and become obscure.

So never mind whether CIOs should be business people or technologists. A more challenging question is whether CIOs should be business people, classically tech-savvy people, or AI/tech-savvy people.

Or some combination of those alternatives.

But wait, there’s a whole other level we need to dig through. That’s because this collection of confusing questions isn’t the starting point. It’s because, as CIO, the questions that matter aren’t about how the CIO engages with the rest of the company as an executive.

It’s how the CIO engages as the company’s highest-level business analyst.

The CIO’s changing roles and directives

With classical IT organizational architectures, a CIO could make sense of all of IT’s slices, dices, and levels, how the pieces fit together to make the business more effective, and how adding and rearranging the pieces could help make the business more effective and competitive.

In the good ol’ days, that is, CIOs could succeed wearing their business analyst haberdashery without having to give up their executive function.

Read the average opinion piece on how AI affects the CIO’s role and you’ll get the same tired back-office-to-front-office recommendations we waded through when Digital was king. But AI isn’t what’s driving that shift, if it even is a shift.

No, here’s what I think the average CIO is in for:

• Elevating the business analyst: CIOs need to be smart about AI, but aren’t in a position to make themselves business-analyst-smart about AI. So it’s up to the CIO to give IT’s best business analysts assignments that will make them AI- smart, and to schedule regular debriefings to help the CIO become smart enough.
• Become architect-level smart about AI: CIOs should build a capability-level view of AI, collaborating with the whole IT department to gain a realistic understanding of what high-level business capabilities AI does and could bring to the business party.
• Adopt the CSO hat on the company’s behalf: No, notchief security officer. Chief skepticism officer: What the company needs the CIO to become is someone able to see through the hype and blather that sets implementation traps and leads to seductive but unachievable transformation programs.

Why this matters more than you might think

Once upon a time, one of the hallmarks of well-built IT was simplicity. IT professionals designed and engineered systems they and their colleagues could understand because the systems were designed to be graspable.

Among the many changes AI is bringing to the fore is that AIs don’t need the same level of simplicity, and we can anticipate that AIs won’t be instructed to make their designs human-graspable either.

We already have too many applications in the IT portfolio that are the only repositories of business logic the company has — the developers and business analysts who supported this business logic retired long ago.

That was the case when simplicity was a design goal.

Just imagine the scenario when AIs build systems for which simplicity isn’t a target they’re aiming for at all.

See also:

• AI is reducing leadership to simply managing work
• Can an AI be a competent leader? Let’s find out
• AI is about to get really weird. CIOs better be prepared.

With AI increasingly tucked into every cranny of the enterprise, someone has had to step up and provide the tools necessary to discover, track, and monitor all the agents and LLMs and keep them humming along in their various workflows. Thankfully, the DevOps world answered the call, building the tools to support our new overlords in an emerging subdiscipline interchangeably called “AIOps,” “AgentOps,” and sometimes “agent observability.”

Many of the challenges involved in AgentOps are similar to those tackled by traditional DevOps tools and processes. After all, at their foundation, LLMs are just software running on hardware somewhere. Typical issues involving RAM and disk space are just as important in the agent world, maybe more so because AI operations are even more greedy about consuming storage than regular software is.

Many of the companies supporting agent observability are big names in DevOps circles, having adapted their stacks to address the idiosyncrasies of modern LLMs. IT teams maintaining enterprise agents can treat the LLMs as just one node in a big graph filled with services that are constantly swapping packets and triggering software jobs. Latency and resource constraints must be managed because end-users don’t care whether it’s an LLM, a database, or a plain-old Python script that’s failing, bringing their work to a grinding halt.

But new AI-specific challenges are opening the door to newcomers that are building tools with the peculiarities of LLMs in mind — for example, keeping deeper logs filled with records of prompts. LLMs are also often very non-deterministic by design, making it trickier to pinpoint failure modes. And then there’s the fact that an agent will give a perfectly intelligent answer one minute and hallucinate the next.

Relying on many of the same approaches that DevOps tools do, AgentOps tools watch for misbehavior and flag anything out of the ordinary for deeper analysis. This may be as simple as fixing slow responses, but it can also include AI hallucinations and other issues born of LLMs’ non-determanism.

Teams trying to choose which agent observability tools is best for their use case should look at the size and nature of their agentic systems and projects. Are they adding AI agent features to an existing product or application, or are they building agentic systems from scratch? Are they more focused on maintaining a stable LLM operation or iterating on new approaches? Is AI the center of attention or just an add-on that’s meant to improve an existing stack?

The AgentOps and agent observability options listed below share many of the same features but differ in their focus and their attention to the challenges organizations will encounter when incorporating agents into their stacks. Each tool offers a worthwhile place to start understanding how to care for the growing presence of AI in the production world.

AgentOps.ai

When teams of agents work together, tracking the conversations are essential for understanding and debugging what’s happening. The SDK from AgentOps.ai records events so that the creators can replay past behavior to track details such as token counts, spending, latency, and more. Available as a service and on-premises.

Pricing: Starts at $40 per month plus usage costs at $0.20 per 1M tokens

Standout feature: Replay analytics with “time-travel debugging”

Best suited for: Complex agent debugging

Arize Phoenix

Debugging prompts and LLM responses requires a nuanced understanding of just what’s happening, in part because of the non-determinism that often enters the process. Phoenix from Arize supports this process with robust tracing and the ability to score the results for more precise iteration. Their system can track the results and tool calls from a variety of major platforms (Anthropic, AWS, OpenAI, etc.) that are initiated by the major frameworks (LangChain, LlamaIndex, DSPy, etc.). The result is insight into what data is triggering what chain of responses.

Pricing: Small free tier; Pro plan starts at $50 per month plus costs tied to events

Standout feature: LLM-as-a-Judge metrics for tracking quality

Best suited for: Teams focusing on iterating for accuracy and quality

BigPanda

BigPanda has always offered solutions for tracking performance of complex systems. Now the company is drilling deeper into the challenge of detecting and ending the problems that come from models that go awry. BigPanda’s main system relies on historical data and machine learning algorithms to flag issues. Its own agent layer connects the problematic nodes and errant models while dispatching alerts to the right team members.

Pricing: “Value-based” table on request

Standout feature: Automated triage for faster response

Best suited for: Large teams seeking to reduce alert fatigue from large customer base

Braintrust

Setting up an effective improvement cycle for an AI agent requires a strong feedback loop from production data to the agent’s next generation. Braintrust watches the production workload and creates test vectors that expose how an agent may be drifting, regressing, or departing from its path. The tool automates much of the testing and scoring feedback loop so problematic patterns can be discovered and addressed. A core part of the offering is a specialized data store that can track large and sometimes deeply nested collections of tests and their results. Their approach may be summarized by one of their tag lines: “trace everything.”

Pricing: Free starter tier; Pro plan starts at $249 with some usage-based costs covered

Standout feature: Highly scalable trace ingestion

Best for: Teams developing strong guardrails through continuous testing

Chronicle Labs

When it’s time to release a new version of an agent into the wild, the platform from Chronicle Labs specializes in staging it and testing it with a collection of use tests and regression cases. The tools are also helpful during development cycles. “Backtest your agent against reality,” their sales material promises, with a set of tools that mines the production telemetry for solid test vectors that stress every part of the agent with prompts and challenges that the agent will encounter after leaving the safety of the lab.

Pricing: On request

Standout feature: Back-testing options for complex testing regimes

Best for: Teams chasing strong models with good fidelity to reality

Comet Opik

Building a dashboard for tracking every in-flow and out-flow to agents is one way to be ready to watch for and solve problems. Opik from Comet is just such a tool. The DevOps teams can track each call and add its own automated routines to examine the results, score them based on 30-plus metrics, and if desired, send it off to another LLM to evaluate the results. Agents that are constantly failing stand out. DevOps teams can also ask questions like, “Who is using this model and racking up all of the bills?” The same goes for MCP skills and other cogs in the machine.

Pricing: Free tiers for open source and small projects; Pro plan starts at $19 per month with usage limits

Standout feature: Auto-scoring with 30-plus metrics for evaluating traces

Best for: Teams focusing on RAG and agentic workflows

Datadog

DevOps teams that rely on Datadog to track logs across collections of services can also use it to track LLM operations, which are, of course, just another source and sink for data. It will track performance such as time to first token and offer insight into what might be causing an issue, such as lack of memory. Results then get plugged into the same cost-tracking mechanism so the bean counters can predict when the budget will run out. After all, the CFO likely doesn’t care whether the bill comes from an LLM or an old-school S3 storage bucket. Datadog integrates AI into their tools by treating these models as just another source of data.

Pricing: Small free tier with multiple paid tiers for various levels of enterprise monitoring

Standout feature: Large installed base with broad focus on more than LLMs

Best for: Large enterprise teams working with established infrastructure

Dynatrace

For more than 20 years, Dynatrace has been delivering tools that track dataflows across the full stack. Now that AIs are finding roles in many of the nodes in this complex graph, they’re expanding to track how various AI agents can interact. They want to build one platform that helps track the root cause and, often now, deploy solutions autonomously. They want to focus on being ready to support complex networks of agents that detect problems in either performance or security and then work within defined guardrails to fix them. Determining the right role for their own AI-powered agents is a key part of the product.

Pricing: Plans start at $7 per month with larger plans designed for full enterprise monitoring

Standout feature: High level of autonomous monitoring designed for large installations

Best for: Complex, hybrid environments mixing LLMs with traditional services

Galileo

Placing some AI systems into production is often a harrowing experience because the actual performance is impossible to predict, even with the most rigorous tests. Galileo offers guardrails that track performance and watch for any behavior that deviates from the ground truth. Their “LLM-as-judge” systems are distilled into compact models that can be run locally for lower costs and faster performance.

Pricing: Small free tier; Pro plans start at $50 per month with usage-based limits and costs

Standout feature: Real-time guardrails for deployed agents

Best for: Security-conscious installations that need to defend against hallucination and data leakage

Grafana Labs

Long the go-to source for open source telemetry, Grafana Labs now tracks performance of AI models in constellations of services. Grafana tracks the evolution of answers across the agentic network to recognize how small changes or hallucinations can spin out of control. It bills its system as “actually useful AI” and has even trademarked it. Its cloud assistant can configure and reconfigure the Grafana dash to offer the right level of observability. Its system includes AI-level analysis that can flag models that are responding quickly but offering bad answers because of problems such as model drift or context degradation.

Pricing: Basic free tier; Pro plan begins at $19 per month, includes better retention and some usage-based fees 

Standout feature: Full-stack tool with fully integrated LLM tools

Best for: Large, enterprise-scale system adding AI

Helicone

Sometimes shoehorning in another tool into the chain can be tricky. Helicone is designed as a smart network proxy that will route all model requests while keeping solid debugging records from the data as it goes by. The data it captures can be turned into nice charts that make it easy to spot latency issues or model failures. Naturally, tracking AI spend is also a feature in much demand as bills continue to climb.

Pricing: Small free tier; Pro plan starts at $79 per month, includes features such as team collaboration and improved querying

Standout feature: Proxy-based integration

Best for: Development teams who want to add better monitoring features quickly

Laminar

Tracking agents in development and production means building strong storehouses of data enumerating what happened. Laminar works closely with OpenTelemetry to follow agents operating in production so that flaws and failure modes can be understood from log files stored efficiently with their own compression scheme. Developers can search through traces with an SQL-ish language and Laminar’s transcript view illuminates what happened. When necessary, the traces can enable developers to scroll back in time and replay the same inputs for debugging. The goal is to offer deep insights with high-level visibility of how well the agents are meeting business objectives.

Pricing: Small free tier; “Hobby” tier that adds more features at $30; Pro level starts at $150 per month

Standout feature: Open-source license makes self-hosting a viable option

Best for: Teams fully able to leverage open-source responsibilities

LangChain LangSmith

Real-time data from agents is essential for managing any mutli-agent system in production. LangSmith from LangChain traces costs, tools, and progress toward solutions for a wide collection of agents using SDKs for Python, TypeScript, Go, and Java. The OpenTelemetry-based solution watches for anomalies, issuing warnings and alerts through dashboards and communication channels such as PagerDuty. Deeper analysis can reveal issues such as topic clustering or odd patterns of failure. Coordination with agent deployment platforms such as LangGraph and deepagents ensures greater focus on successful resolution of assignments.

Pricing: Free for solo developers; Pro teams start at $39 per person per month 

Standout feature: Systematic approach to regression testing of prompts

Best for: Teams relying on LangChain and LangGraph frameworks for supporting complex agentic behavior

Lunary

Watching the user experience is essential for building AI applications such as chatbots and assistants. Lunary offers a proxy that traces all interactions and then builds analytical dashboards for measuring metrics such as user satisfaction or model costs. One common usage is finding frequent topics and looking at the responses to ensure they deliver. When prompts aren’t perfect, Lunary lets teams iterate on the prompt text until the right answers are coming out. Its proxy structure and common API format enables Lunary to promise to work with “any LLM, any framework.”

Pricing: Free tier; Pro plan starts at $20 per month

Standout feature: Deep integration with humans for reviewing and optimizing results

Best for: Startups focused on rapid prompt innovation

NewRelic

The platform that began tracking performance of some web applications is now powerful enough to track the flows of data through complex agentic ecologies. NewRelic’s AI-driven monitoring watches for golden signals that can indicate misbehavior or worse throughout the entire lifecycle. It tracks every detail of the interactions through protocols such as MCP and then makes this available to the AI engineers responsible for performance. The dashboard provides the insights necessary to watch for toxic behavior, overt bias, drift, and overblown hallucinations. Predicting and maybe even controlling the cost is also a growing role as tokenomics becomes as important as response time.

Pricing: Free tier; Pro plan fees available through website

Standout feature: Full-stack support with hundreds of integrations with other tools

Best for: Established enterprise teams mixing in AI

Nova AI Ops

The goal of Nova AI Ops is to deliver a team of agents that watch over a cloud and make it, at least partially, self-healing. Each agent uses a mixture of predictive AI and machine learning to watch cloud telemetry reports for anomalies. Then they calculate the “blast radius” and decide whether this is a problem that can be fixed automatically “while you sleep” or saved for the human supervisors. These tools are aimed not just on LLM operations but on the stack as a whole.

Pricing: Small free tier; Standard pricing  begins at $40 per user per month with usage billing

Standout feature: Focus on software reliability engineering helps teams deliver stable stacks

Best for: Teams that want to integrate LLMs into incident response and stability management

Splunk

The platform that began delivering smart logging is now fully AI capable, offering solutions that can watch over agents with much the same way that it continues to track microservices. Splunk now includes a fairly large amount of predictive AI for learning from the information in the logs and then turning this learning into fast solutions. This AI assistant can track deployed AI models connected by protocols such as MCP and watch over behavior while delivering the ability for users to drill down and explore what’s working and what’s failing. Their AI Canvas is meant to offer a central hub where the AI scientists can track both the local behavior of the models as well as their role in a larger data ecosystem.

Pricing: Activity-based pricing tracks usage of LLM backends and storage

Standout feature: Ready to scale to large enterprise stacks

Best for: Teams with legacy systems that are folding in agentic options

SuperPenguin

One of the most important parts of an AI service is the bill. SuperPenguin is a product designed to track consumption and make predictions so that the CFO won’t be surprised. The goal is to provide solid estimates about the total cost of each product by allocating costs to customers, features, and teams. If there’s a sudden shift, a “spike detector” will raise an alarm so that dev teams can ensure that the AI spend is worth it.

Pricing: Small free tier for experimentation; Growth tier for teams, starting at $30 per month; Pro tier offers deeper options starting at $200 per month

Standout feature: Strong accounting with invoice reconciliation and PR-level usage tracking

Best for: Teams that need precise cost accounting

Vellum

Prompt engineers spend time fussing over the details of tweaking, improving, and enhancing the words that guide the LLM. Vellum started as a company that would provide the pipeline so that you could manage and improve the prompts that ran again and again. Now the system is growing more powerful, offering a higher level of automation that lets you meta-manage the prompt chain. They’ve also begun marketing it as a form of personal assistant with pre-built connections to many of the major services such as Gmail. Its llm-cost-optimizer can juggle multiple options while finding a cheaper way to execute a prompt, a process the company suggests can save 60% or more.

Pricing: Open-source free tier; Pro plan starts at $35 per month

Standout feature: Focus on multi-model pipelines for true agentic solutions

Best for: Product teams with complex prompt engineering workflows

Recent data from the CompTIA Tech Jobs Report shows that tech jobs have seen a drop in unemployment, down to 3.1% in May from 3.5% in April, accounting for an increase of around 6,700 in May. Roles that saw the highest demand include software developers and engineers, systems engineers and architects, tech support specialists, cybersecurity engineers and analysts, and AI engineers.

And according to projections form the U.S. Bureau of Labor Statistics, the tech workforce is anticipated to grow twice as fast as the overall US workforce, with an expected replacement rate of 6% annually, or approximately 323,000 workers, for tech occupations between 2024 and 2034.

“More than ever, business success relies on technology,” said Seth Robinson, VP of  industry research at CompTIA. “Our research has shown a desire to build capability in core operational functions, which then allows companies to build advanced practices in AI, data, and cybersecurity.”Further data, this time from the CompTIA Sate of the Tech Workforce 2026 report, shows that 83% of IT leaders and HR professionals say their organizations are placing a high or moderately high priority on addressing skill concerns, and 62% say they expect the budget for AI training to increase in the next year. Organizations also seem to recognize the impact that skills development can have on employees, with 83% saying they expect these investments to have a high or moderate degree of impact on employee morale and engagement.

Cause and effect

There are two main factors driving the skills gap and pushing IT leaders to invest in training programs: AI accelerating technological change, and a shortage of AI skilled professionals in the hiring market. However, while AI is a main driver in the skills gap, 48% of IT leaders also say AI is crowding out other important needs, including a much-needed shift to skills-based hiring methodologies.

IT leaders are looking to build training programs that specifically address AI basics, data analysis, AI threat awareness, automation, data preparation, securing AI systems, building inputs and prompts, and creating AI agents. Currently IT leaders cite cost of training, training fatigue, turnover, lack of executive support, difficulty measuring ROI, and stale training curriculum as some of the biggest challenges when developing training programs, according to CompTIA.

So organizations that embark on upskilling and training will need a robust strategy in place to ensure employees take advantage of the training and remain engaged. Leaders will also need to set the expectations for how to integrate training into daily work, so they aren’t left feeling overwhelmed by the process on top of their current roles.

“What we’ve found that works is to embed AI into people’s workflows after the initial training, and pair people with colleagues who are further along in their AI utilization,” says Maruf Ahmed, CEO of IT solutions provider Dexian. “The gap between ‘I attended the training’ and ‘I’m actually using this differently in my job’ is where companies lose people, and closing it takes more than a single training cycle.”

This is Part 2 of a series on tech worker unionization. See Part 1: “A brewing battle: More IT workers want unions. The industry doesn’t.”

The best time for tech workers to unionize was 20 years ago, when they had plenty of leverage. The second-best time is now, when they don’t.

Mass layoffs, AI-driven displacement, corporate surveillance, workplace disillusionment have created conditions that have made organizing compelling for tech professionals. But the federal labor board that has historically protected workers’ right to organize has been weakened, and the companies that once feared it are openly defying it.

Here’s how organizers and labor experts describe the pros and cons to organizing — and how you can get started.

What unions can — and can’t — do for you

The single biggest benefit of a union contract for most tech workers isn’t pay — it’s protection against arbitrary termination, especially in the wake of recent mass layoffs in tech. In the United States, nonunion “at-will” workers can be fired at any time without a stated reason, while unionized workers negotiate protections written into their contracts.

“That fear of the company letting you go for anything at any time…with a union they just can’t do that,” says Zak Thompson, a senior software engineer at Kickstarter and union steward at Kickstarter United. Now that Kickstarter employees are unionized, people are less worried that saying something negative will result in termination.

“I’ve been shocked at the willingness of my co-workers to speak up against what they see as poor or controversial business decisions,” Thompson says.

Beyond job security, unions can deliver concrete material gains. Kickstarter United was formed in 2020, although getting there wasn’t easy: two employees were fired during the organizing campaign — which itself became a galvanizing event. And while the union hasn’t been able to prevent layoffs, it did negotiate better terms: four months of severance pay and four to six months of continued health insurance, versus the two to three weeks per year of work that management had initially proposed.

Other benefits include a four-day work week; AI protections; a minimum pay floor; and standards for raises, promotions, and time off for the company’s 59 employees.

Unions can give tech workers a voice in decisions that affect their daily work — including how AI tools are deployed. “Nobody I’ve spoken to is against new technology or getting trained in it,” says Max Belasco, a business systems analyst at the University of California Los Angeles School of Law and co-chair of the UCLA chapter of the University Professional and Technical Employees/Communications Workers of America (UPTE-CWA) Local 9119.

“But when new technology is being implemented, we want to know: what’s the five-year vision, the 10-year vision? Are we implementing this in a way that betters staffing, increases efficiency, or eases the lives of people already working? Or are we trying to take away jobs, automate people out of their pension or paycheck?” Belasco says.

The challenges are real. Tech professionals are less inclined to leave their jobs in the current market because wages haven’t been increasing as fast as they once were, and it can take longer to land another job.

“Tech moved from a very tight labor market in 2022 (1.85% unemployment rate) to a noticeably weaker one in 2024–2026 (3.49%),” although that’s still better than the national unemployment rate of 4.36% through May of this year, says Liya Palagashvili, senior research fellow and director of the Labor Policy Project at the Mercatus Center at George Mason University.

Flexibility is a concern. The more substantive challenge, raised by economists including Palagashvili, is that traditional union contracts impose uniform terms across an entire bargaining unit, limiting the flexibility that many tech workers — and their employers —currently enjoy. Tech firms need to move fast, adjusting teams, products, and roles on the fly.

“Collective bargaining agreements can make those adjustments much more difficult, whether by making them slower, costlier, or inconsistent with the contract,” she says.

Workers skeptical of unions in a survey of 1,900 tech professionals conducted by the career site Blind cited specific concerns: that unions are “not meritocratic,” “prevent innovation,” and “hold back earnings of top performers.”

Thompson from Kickstarter United pushes back: “We have nothing in our contract about ‘you can’t bend down and pick up a piece of trash because that’s someone else’s job.’ The company is free to give bonuses and individual raises as much as they like. This is all just up to the people who are bargaining the contract from the union side.”

Organizing carries potentially serious personal risks. During negotiations for a second three-year contract in 2025, Kickstarter United went on strike for 42 days. A few months later, the company announced layoffs.

“They let go strong union leaders, including a person who had bargained our last contract,” Thompson says. The union appealed, and the issue is now going to arbitration.

If you form a union, don’t expect much support from the National Labor Relations Board, the agency that certifies US labor unions and protects workers’ right to organize, in terms of prosecuting complaints of unfair labor practices, Thompson warns. “We’re in a political moment in this country with a pretty weakened NLRB. You have to be ready to organize and withhold worker power without any guarantee of safety.”

Organizers are up against an enormous union avoidance industry. Organizers can expect fierce pushback as soon as the business discovers that organizing is underway.

“There’s a multi-billion-dollar industry in union avoidance,” says Alan McAvinney, a Google software engineer and organizing chair, Alphabet Workers Union-CWA, a 1,400-member minority union of Alphabet employees. (Google is a subsidiary of Alphabet.)

US employers spend roughly $1.7 billion a year on union avoidance consultants and law firms, according to a May 2026 report by the Economic Policy Institute and LaborLab.

Expect hardball tactics. Management may play hardball during the time between when organizers announce their intention to unionize and the actual vote. For example, management can threaten to fire foreign-born workers in the US on H-1B visas if they support the union. Those workers would then have just 60 days to find a new sponsoring employer or lose their H-1B status, according to a recent Tech Workers Coalition blog post.

And at venture capital-backed startups, investment agreements sometimes require management to attest there is no union activity — meaning a public organizing drive can trigger funding withdrawal. Or, if a unionized company is acquired, the new management can dissolve the union overnight by reclassifying unionized workers as new hires.

With these sobering facts in mind, here is how organizers who have done it describe the process of creating a union.

Step 1: Start a conversation with your co-workers

At the University of California, a two-tier system had evolved where some tech workers were unionized and some weren’t, Belasco says. Management created new titles that fell outside the union even though they had similar job descriptions and responsibilities to those in the union. Those nonunion employees received lower pay and benefits than their unionized peers, which created resentment and instability.

Belasco and other organizers wanted to eliminate that division by bringing everyone under the same contract. But when they began their unionization drive, “the biggest barrier we faced wasn’t management opposition — it was that people felt this was just the best-case scenario realistically available: ‘We have this job at the university, we have concerns about automation and layoffs, but what can we really do about it?'” he says.

The antidote to that fatalism, organizers say, is simple: “Just start talking to your immediate co-workers. Are they experiencing the same challenges you are experiencing?” says McAvinney. “There’s no need to start talking about a union at this point.”

Just get a consensus and start building a group of like-minded individuals, Thompson advises. “Always start with one-on-one conversations, and that’s what you should do the whole time. That’s the key to organizing,” he says.

Tech workers often think they’re a special case, says Thompson, and therefore that unionization isn’t a good fit. “You’re not special. You are a company of workers, you are organizing, and there is a playbook for that. Trust the process, because it tends to work pretty well,” he says.

Step 2: Who’s on board, and who’s not? Map your workplace, but keep it quiet

Once there’s a consensus, continue to grow your network. Keep a list of everyone you’ve spoken with and note their disposition: “Is this person union-friendly or anti-union? Would they be a strong organizer?” Thompson says.

Maintaining secrecy early on is essential, because anti-union tactics will start immediately, and that can stop union organizing before it can gain momentum.

“Generally, employers do not want to share power with their workforce,” McAvinney says. Employers will deploy every means at their disposal to stop organizing efforts and peel away potential yes votes.

“If you look at historical examples, having 70% approval before the employer finds out about you results in a high percentage of wins when you actually cast the vote. Historically, that’s an effective buffer,” he says.

There’s a real threat of firing and layoffs. The traditional tech worker belief that job mobility makes collective action unnecessary is now being tested by a tighter job market, McAvinney says, noting that workers who many believe were fired for speaking out back in 2019 were a galvanizing factor in his union’s formation.

“You generally don’t want to be in a situation where the employer feels comfortable firing everyone. Part of that is thinking from a cynical standpoint about what the consequences would be to the employer if they did fire everyone,” he says.

One-on-one conversations that include personally asking co-workers to keep conversations confidential are key to keeping things quiet, Belasco says. When 2,100 UC tech workers voted to unionize in May, 96% voted in favor. To stay out of earshot of managers, avoid employee surveillance tools, and sidestep conference calls that could be recorded, organizers met with workers in their homes.

“That tactic is probably what made the difference between winning the election and getting the majority we got,” he says.

Step 3: Find the right union affiliation or go it alone

“Running a campaign against major employers requires the resources and expertise of the larger labor movement, even if workers publicly present as independent,” says Kate Bronfenbrenner, director of labor education research and senior lecturer emeritus at Cornell University’s School of Industrial and Labor Relations.

Options include the Communications Workers of America (CWA), Service Employees International Union (SEIU), and the Office and Professional Employees International Union (OPEIU), among others. Another resource, the Tech Workers Coalition (TWC), provides training on organizing tactics, AI-in-workplace issues, and contract negotiation, and can match workers to the right unions for their needs.

The Alphabet Workers Union decided early on to affiliate with CWA. “They gave us a bunch of support early on in our campaign with no strings attached,” McAvinney says.

Kickstarter is organized through OPEIU, Thompson says. “They’ll usually have resources and staff that can help you through the next steps: collecting signatures in support of a union, bringing that to management, holding a vote — the more formalized things that interact with US labor law. They’ll also help with organizing along the way,” he says.

For workers at institutions where a union already exists, there may be a faster path. Organizers at UCLA did what’s called a “unit modification,” aligning with UPTE. By organizing under UPTE, the workers didn’t have to negotiate a new contract from scratch — they joined an already-negotiated contract covering existing UPTE tech members, which put them in “a much stronger position” than starting fresh, Belasco says.

Step 4: Choose your union model: majority vs. pre-majority or minority

Assess what’s practical for your organizing effort. In a majority union, more than 50% of all workers in a defined bargaining unit must vote to join the union through an NLRB-supervised election in the private sector, or a Public Employment Relations Board (PERB)-supervised election for public sector workers.

The NLRB must certify the union, which then operates under its legal protections. This means, for example, that the employer must bargain, negotiated contracts are enforceable, violations must go to the NLRB or arbitration, and workers can’t be dismissed without just cause.

A pre-majority or minority union is a minority labor organization operating without NLRB protections or collective bargaining agreements. “Pre-majority means that workers are able to demonstrate majority support — through signed cards, petitions, a walkout, or everyone wearing solidarity T-shirts — without going through a formal election,” Bronfenbrenner says.

The Alphabet Workers Union-CWA (AWU-CWA) formed as a pre-majority union because achieving majority status across a globally distributed workforce of over 100,000 was not a realistic near-term goal. “An underground model where you try to reach 70% support across a workforce of over 100,000 people isn’t realistic,” McAvinney says.

A pre-majority union can still make a difference, he says. For example, the Alphabet Workers Union-CWA convinced management to offer voluntary exit packages — buyouts — prior to announcing layoffs.

For smaller organizations, a majority union may be the more practical option — it’s more attainable, McAvinney says. “I don’t think [the pre-majority union model] is the correct thing to do in all situations. I certainly would not recommend it to a 200-person shop.”

Kickstarter, which had fewer than 100 employees, was able to form a majority union, with 55% voting to organize.

Ultimately, says McAvinney, “there’s no inflection point where you go from being able to win nothing to winning everything, even with a contract and a supermajority. But the more people you have who are willing and able to fight for what they want, the more you’ll be able to get.”

Step 5: Who should — and should not — be in your union?

Belasco’s situation at UCLA illustrates a broader strategic choice that every organizing campaign must make. He had been in a union position in educational technology when he was told his role would be reclassified as a non-union position.

“I was given a choice: apply to the new non-union position to continue doing the work I’d trained for, or stay in my union position doing service desk work I wasn’t used to,” he says. “Essentially, it was a choice between job security and career progression.”

Belasco joined a “wall-to-wall” union, which represents a broad range of university professional and technical employees across the UC system rather than a single job category, such as engineers or tech professionals.

Kickstarter United is another example of a wall-to-wall union. “It’s not just the engineers who are unionized, but also customer support, designers — everyone,” Thompson says.

Wall-to-wall unions are more powerful, but they’re also more difficult to achieve. Under US labor law, “professionals have to vote separately on whether they want to be combined with other workers,” says Bronfenbrenner. “You can never have a wall-to-wall unit without giving professionals the chance to decide whether they want to be separate.”

The law’s “professional employees” category includes roles like software engineers and developers but not necessarily others. For example, customer support specialists and QA analysts would fall into the “non-professional workers” category.

“For decades, the pattern was either to organize everybody except the engineers, or manage to organize the engineers and fail to bring in everybody else — neither of which builds real worker power,” says Simone Robutti, an organizer with Tech Workers Coalition Global, an international branch of TWC based in Berlin.

Step 6: You won the vote. Get ready for what comes next

Winning a union vote means having a seat at the table, says Thompson. “Once the workers have come together and agreed they want that seat, you bring that to management, and they have a chance to voluntarily recognize a union,” he says.

But in most cases employers contest the results, which must be certified by the NLRB or PERB. That process, in which the employer uses various tactics to challenge the legitimacy of the outcome, can take weeks or months.

Unfortunately, the legal framework that is supposed to protect workers during this process has been significantly weakened in the last few years. In a potentially more ominous development, SpaceX, Amazon, Trader Joe’s, Starbucks, and the University of Southern California have in separate legal actions challenged the constitutionality of the NLRB, arguing that the agency’s structure violates the separation of powers. The Fifth Circuit Court of Appeals upheld injunctions against the NLRB in SpaceX’s case in August 2025 — a serious challenge to the agency’s authority.

In the meantime, some companies may disregard negotiated contracts, which can lead to lengthy legal appeals or extended arbitration.

“The NLRB can still force an election, but it can’t force a contract, and companies are saying they simply won’t comply,” Bronfenbrenner says. This is where the expertise and resources of affiliation with a major union can help, she adds.

As a result, contract negotiations can take far longer than workers might expect. At Kickstarter, for example, two years and four months elapsed from the time of the union vote to the first contract, and that was at a 59-person company with a relatively cooperative employer. At larger companies with more aggressive legal teams, the timeline will be longer.

Forming a union is hard work, Robutti says. “It’s not a service you pay for and they protect you. It doesn’t happen spontaneously, and it doesn’t happen magically. It’s the choice to take responsibility for improving your workplace.”

With the World Cup in full swing, stadiums across North America are currently accommodating thousands of fans every match day. That said, the stadiums’ biggest security challenge isn’t of a physical nature.

It is not hyperbolic to say that football stadiums are some of the most chaotic endpoint environments in enterprise IT. On game days, tens of thousands of unmanaged, unknown devices connect to stadium networks, alongside payment systems, digital displays, operations platforms and venue staff devices. This creates a massive attack surface with a potential for serious disruptions, such as payment outages at concessions, delays in live streaming and interruptions to other venue operations.

In this article, we’ll examine the World Cup stadiums’ unique cyber environments, while also providing steps that venues can take to harden their connectivity and ensure that their networks are protected.

For World Cup stadiums, real-time visibility is far more important than device control

Given that stadiums like Dallas’s AT&T Stadium, Mexico City’s Estadio Azteca and New Jersey’s MetLife Stadium can all accommodate over 80,000 soccer fans per game, it is impossible to control all these fans’ devices. Hence, network segmentation and real-time visibility are key.

The fan-device layer obviously must remain entirely separate from the payment systems and operational infrastructure. All fan devices need to be relegated to the public WiFi, and treated as hostile by default. Although this segmentation is technically a form of device control, real-time visibility is truly the only way to maintain a Zero Trust environment within these stadiums.

Continuous monitoring across networks, endpoints and identity systems is vital

To achieve a Zero Trust architecture inside these massive football venues, it is important to have identity-centric zero-trust solutions firmly in place.

With so many vendors, stadium personnel and operations workers requiring different levels of access to different systems, a robust identity security solution is crucial. All modern football stadiums require adaptive MFA, single sign-on, and conditional access based on users’ roles, locations, time of access request and device type.

Without a robust identity access tool in place, a bad actor could compromise a single user’s credentials and gain access to payment systems or other operational technologies within the stadium.

Besides an effective identity security tool, stadiums require network visibility and endpoint protection. All operational endpoints inside the arenas, including point-of-sale terminals, digital displays and staff devices, need to be managed and monitored via a robust endpoint management platform. With such a tool, IT teams can correlate telemetry across all network activity, which helps them to isolate compromised devices before a bad actor can execute malicious lateral movements.

With real-time traffic visibility, IT personnel can detect anomalies, monitor network performance across all segments and receive alerts whenever unusual traffic patterns emerge. Although stadiums can’t control 80,000 fan devices per se, empowered IT workers can observe everything from the network level.

Automation can help to ensure timely patching and audit readiness

A unified log management and security analytics tool is vital in the World Cup setting. During a high-stakes event like the World Cup, SIEM platforms pull real-time logs from all the devices, endpoints, applications on the network.

By using an effective patch management software in conjunction with a SIEM platform with automated alerts, stadium IT personnel can automatically patch hundreds of endpoints, while also accelerating incident response time.

The very best SIEM tools will also use behavioral analytics to conduct real-time threat detection; if any anomalous activity is flagged on the network, automated alerts are triggered and incident response workflows will commence.

SIEM tools also help when it comes to building out compliance reports and maintaining audit readiness. The IT departments inside these enormous football stadiums require a host of different compliance reporting capabilities, including PCI-DSS for stadium payment systems, SOC 2 compliance for third-party vendors handling fan data, ticketing and other operations, as well as GDPR compliance for loyalty programs, identity verification, WiFi registration and any biometric data captured within the stadium.

Key steps that stadium IT personnel should take during the World Cup

Firstly, a Zero Trust environment should be maintained inside all the stadiums. The 2026 World Cup contains far more integrated technologies than ever before. Today’s in-stadium technologies are borderline futuristic; referees wear body cameras, and there is even motion sensors embedded inside all World Cup game balls. Given this ultra-high-tech environment, all users, APIs and devices need to be continuously authenticated and treated as hostile-by-default.

Secondly, in such a high-stakes, highly integrated environment, real-time monitoring and centralized visibility is crucial. With centralized visibility across the network, IT personnel can effectively conduct deep traffic flow analyses, identifying which devices are attempting to communicate with which systems. This way, all lateral movement attempts can be identified, and any fan-device that tries to reach a payment or operational technology segment can be flagged.

Thirdly, IT teams should conduct incident simulations. Given the complex environment of broadcasting infrastructure, digital ticketing systems, POS, WiFi and commercial cellular networks, it is vital that IT personnel test their incident response processes to ensure they avoid service disruptions and prevent data leaks during matches.

The bottom line: The 2026 World Cup stadiums require robust cybersecurity solutions

From a cybersecurity perspective, the 2026 World Cup is a unique event. With matches taking place across sixteen different cities in three different countries (not to mention the currently heightened geopolitical tensions), there is a strong potential for state-backed cybercriminals and hacktivist groups to target stadium infrastructure.
It is vital that stadium IT personnel are equipped with adequate cyber solutions, including robust SIEM, IAM, patch management and network management tools. There’s no reason to give bad actors a free kick.

This article is published as part of the Foundry Expert Contributor Network.
Want to join?

Artificial intelligence is changing how cybersecurity teams work, with security professionals spending more time validating AI-generated recommendations and deciding when to trust AI outputs, according to new research from ISC2.

The ISC2 survey of 856 cybersecurity professionals found that 65% spent more time deciding when to trust or act on AI-generated recommendations during the past year, while 63% reported spending more time reviewing or validating AI outputs. The study also found that AI-related mistakes remain common. Nearly nine in 10 respondents (89%) said they have experienced AI recommendations that led to incorrect outcomes, and 50% said human decision-makers are ultimately held accountable when those mistakes occur.

“AI is not replacing cybersecurity professionals; it is changing what the profession requires of them,” ISC2 CEO Scott Beale said in a statement. “As AI takes on more repetitive tasks, cybersecurity roles are shifting toward higher-value work, from asking the right questions to validating findings, interpreting outputs, and applying human judgment.”

The ISC2 survey also revealed that:

• 48% said AI has reduced workplace stress.
• 32% said it has increased stress.
• 62% cited overreliance on AI recommendations as a top concern.
• 61% cited undetected errors that could scale rapidly across systems as a top concern.

Many cybersecurity professionals are expected to act on AI-generated outputs without fully understanding how those outputs were produced, ISC2 found. Nearly a quarter of respondents (24%) said they are often or very often expected to act on AI-generated outputs without fully understanding how those outputs were produced. Still, respondents emphasized the importance of governance and oversight. About 80% said governance frameworks, understanding when to trust AI outputs, and knowing when to override recommendations are critical for effective AI use.

Views on AI’s effect on cybersecurity careers are mixed: More than half of respondents (56%) said AI has reduced the need for entry-level positions, but 53% said AI is creating new types of entry-level roles. Nearly half (48%) said AI has made them more optimistic about their long-term cybersecurity careers.

Despite increased AI adoption, respondents said foundational cybersecurity skills remain important. Nearly two-thirds (62%) said AI has not reduced the need for core cybersecurity skills. Respondents also cited determining when to trust AI outputs (82%), understanding when to override recommendations (80%), and establishing governance frameworks (80%) as critical factors for effective AI use.

“This evolution is not limited to entry-level roles. It changes how work is distributed across security teams, making continued investment in governance, validation practices, mentoring, and skills development essential at every level,” Beale said.

Venture-backed firms are buying up the support, finance-ops and managed-services providers enterprises rely on and re-platforming them around AI agents — and the renewal that follows arrives priced per outcome, sold as your advantage. The acquisition-built structure and unproven stability create governance and continuity risks your vendor process isn’t sized for. Here’s how to keep the leverage on your side of the table.

The first time an AI-native services pitch crossed my desk, I nearly signed it. The savings were real, the agents demoed cleanly and the pricing was the kind procurement loves — per resolved case, not per seat. What I almost missed was who got to define the word “resolved.” On an earlier outsourced-support arrangement, back in my public-sector days, the vendor’s reported resolution rate looked excellent right up until we pulled the reopen numbers ourselves. Auto-closed tickets had been counted as wins. Users had quietly stopped logging issues at all. The dashboard was green; the service was not.

That gap — between the number on the contract and what your users actually live with — is the whole game now, and it is about to scale across your portfolio. Gartner’s 2026 CIO and Technology Executive Survey found only 17% of organizations have deployed AI agents, but more than 60% expect to within two years — the steepest adoption curve of any emerging technology it tracks. The providers running your services are moving first, and the contracts are changing faster than most of us can govern them.

The agents underneath these pitches are also nowhere near as reliable in production as they look in the room. Gartner expects more than 40% of agentic AI projects to be canceled by the end of 2027 on cost, unclear value and weak risk controls, and reckons only about 130 of the thousands of self-described agentic vendors are the real thing — the rest are “agent washing” old chatbots and RPA. Treat any headline resolution rate the way you treat a vendor’s own uptime stats: Marketing, until you have seen it run on accounts like yours.

What’s behind the pitch

The challenger’s economics aren’t magic. They come from a reshaping of the services market: Venture-backed firms buying up fragmented, labor-heavy providers — support desks, contact centers, AP and finance ops, slices of managed IT — and re-platforming them around agents. Many of the companies pitching you are not one company at all but several acquired shops stitched onto a shared AI layer. That barely comes up in the sales meeting. It matters enormously once you are the customer.

The direction is independently corroborated, not vendor hype. Everest Group reports outcome-based pricing in business-process services moving from pilots to scaled adoption as AI makes outcomes measurable enough to contract on, with the binding constraint now governance and verified baselines. HFS Research tracks the same “services-to-software” shift across consulting, IT, and operations providers. Here is the part worth holding onto: Most enterprises are early, so you have a little time. But the first vendors to reprice you this way will be the small, single-source ones in the long tail of your portfolio — which, if your stack looks anything like mine, is most of it.

Don’t assume the incumbent is the safe choice

And don’t kid yourself that renewing with the familiar name keeps you clear of this. The big integrators are pulling labor out of their own delivery just as fast — Accenture cut tens of thousands of roles and rehired against an AI-skills filter — and rewriting deals around a share of savings instead of time and materials. Outcome pricing is becoming the default everywhere. There is no version of this where you sit it out.

Two risks your vendor process won’t catch

The first is governance, and the roll-up structure makes it worse than the usual AI-vendor worry. The company you are contracting with isn’t one system. It is several acquired firms with different data practices and security postures, with an AI layer dropped on top at speed. Your customer records, invoices and support transcripts flow into agents whose decisions you often can’t trace, across entities that were never built to one standard. The numbers here aren’t comforting: IBM’s 2025 Cost of a Data Breach Report found 63% of breached organizations had no AI governance policy at all, and 97% of those that suffered an AI-related breach lacked basic AI access controls — AI adoption, IBM concluded, is outpacing both security and governance. When an agent botches a dispute or misroutes regulated data, the regulator and the customer come looking for you, not the platform. And here is the organizational trap: The savings line is what your CFO signs; the provenance question is the one your audit committee won’t ask until after the incident. Nobody raises it for you.

The second is whether the provider will still be standing in three years. These platforms are new, built by acquisition, venture-funded and not one has run through a full contract term or a real downturn. With Gartner expecting more than 40% of agentic AI projects to be canceled by 2027, putting core operations on a young, agent-dependent vendor is a single point of failure dressed up as innovation. Write the exit and data-portability terms before you sign — while you still have leverage to.

Six questions for the renewal

When the challenger shows up — or your incumbent reprices to match — these are the questions I would put on the table. They come down to one thing: Making sure you, not the vendor, own the number.

1. Definition, baseline, guardrails. Make “resolved” mean what your users experience, measured against a baseline you have captured yourself, and tie it to metrics you own — first-contact resolution, reopen rate, time-to-resolution. Expect procurement to resist because pinning this down slows the deal. Hold the line; it is the whole ballgame.
2. Real agent, or agent washing. Gartner reckons only a sliver of self-described agentic vendors are the genuine article. Make them prove it: Production resolution on accounts like yours, and the human-escalation rate sitting behind that number. Not a demo.
3. Auditability, as a gate. SOC 2 at minimum, increasingly ISO 42001 or NIST AI RMF alignment, plus model cards, decision logs and an incident-response plan they have actually tested. If they can’t show how data is walled off between their acquired entities, or how an agent’s decision gets traced, they aren’t ready for anything regulated. This belongs in the shortlist criteria, not the post-mortem.
4. Where autonomy stops. Decide which actions an agent can take alone and which need a human, how it hands off with context and who is accountable when it acts on its own. Put names against it before go-live.
5. The exit. An embedded agent platform gets stickier than the staffed incumbent it replaced, faster than you would think. Lock down data portability, knowledge-base ownership and a way out while you are still the one with leverage.
6. Capacity, not just cost. The best outcome here often isn’t a smaller bill. It is the demand that your old service levels were quietly turning away. Nobody answered the tickets. The cases that aged out. Ask what fixing that is worth before you optimize purely for headcount.

The move

Outcome pricing is where this lands, and on balance, that is progress. But in the near term, it hands the advantage to whoever can measure the outcome — and in most shops, that isn’t the buyer. The edge isn’t picking the cleverest challenger or the safest incumbent. It is being able to hold any of them to a result, on your numbers. Look again at why Gartner thinks so many of these projects die: Not the technology — cost, fuzzy value, weak controls. Our side of the table. So, start there. Take one high-volume, measurable workflow, pilot it against a baseline you own, instrument it with your own metrics, and treat the muscle you build doing that as the real deliverable. Get it right and the pricing model stops mattering. Skip it, and you have just agreed to pay for someone else’s definition of done.

This article is published as part of the Foundry Expert Contributor Network.
Want to join?

I attended several major technology conferences in 2025 where the first AI agents embedded in enterprise SaaS platforms were announced. Some of these agents showed promise and a glimpse into the future of work, while others looked like natural language extensions of a platform’s existing functionality.  

At the end of 2025, Anthropic and OpenAI launched new AI models and code-generating capabilities. More developers tried vibe coding, and some platforms launched spec-driven development capabilities. By February 2026, even The New York Times reported that the AI disruption had arrived, noting that code generators were building “apps that may be flawed, but credible.”

Wall Street investors took notice of the code-generation improvements and other disruptive factors, driving a selloff in SaaS stocks, now referred to as the “SaaSpocalypse.” Part of their concern stemmed from the belief that CIOs would use AI to write software that would replace SaaS solutions.

AI innovations from SaaS and solution providers

But I thought differently and wrote a response in my article asking whether AI is the end of SaaS as we know it. CIOs might use AI to accelerate application modernization, but I doubt they would replace their ERP, CRM, and even smaller SaaS point solutions by building them.

Instead, I believed it would be SaaS companies that would take the most advantage of AI code-generation capabilities.

This hypothesis drove me to attend nine conferences this spring to see how SaaS companies were launching AI agents and defining a new future of work. I wrote eight articles on what CIOs need to know about data management, agile organizations, marketing, ERPs, critical process management, and other evolutions to plan for in the AI era.

Now, looking across all nine conferences, I can draw some conclusions about how AI agents are shaping the future of work. Here are my learnings and what CIOs need to consider when evaluating and deploying AI agents in the workplace.

Agentic, human-in-the-middle, or augmenting human?

SaaS companies have very distinct perspectives on the future of work, including the extent to which humans will play which roles and whether and how quickly we’ll see agentic, fully automated work.

For example, Atlassian proclaimed, “step into the future of human-AI collaboration,” while SAP unveiled “the autonomous enterprise.” Snowflake aimed to “make AI real for business,” while Appian targeted “serious AI built on process.”

These vendors’ marketers had to decide whether to lead with AI, people, or business in their messaging, but so must CIOs as they contemplate their AI strategies and how to get employees to fully adopt AI agents.

Some CIOs see a fully automated agentic AI as the future, with human-in-the-middle as a transitional phase as departments build trust in AI agents’ decision-making and automation capabilities.

Other CIOs see AI more as a tool that delivers productivity improvements by augmenting human decision-making capabilities. Many of these CIOs see human augmentation as essential to supporting critical thinking, innovation, and creativity.

Deloitte’s State of AI Report, published in January, provides a benchmark. It states that 36% of IT leaders expect at least 10% of their jobs to be fully automated in the next year, and 82% expect to reach that benchmark in three years.

Many organizations will have a mix of AI agents, choosing automation where reliability at scale is possible, but opting for human augmentation in operationally critical or customer-facing domains. But how CIOs position AI agents is not only an operational strategy; it’s also a cultural statement that shapes employees’ embrace of AI and whether detractors vocalize job-loss fears.

In the short term, it will also weigh in on which AI agents to use from different partners and which areas to build in-house.

Many options to test and deploy AI agents

Many solution providers are demonstrating significantly more AI agents this year. For example, SAP went from 40 Joule Agents in 2025 to over 200 in 2026. Three technology capabilities are fueling this significant growth:

• Adobe, Appian, Boomi, Cisco, Domo, Salesforce, SAP, Snowflake, and others offer data fabrics and data-pipeline capabilities to connect data sources outside the primary workflows supported by their platforms. Appian, Pega, Quickbase, and SAP also centralize business process automation, an important starting point for developing AI agents.  
• MCP servers enable integration and communication between AI agents and are used to facilitate multistep agentic workflows. Virtually all the companies announcing major investments in AI agents are also announcing MCP integration capabilities and related partnerships.
• Solution providers are not just using AI code-generating capabilities; many are launching their own AI agent development tools. The first beneficiaries of these development tools are the solution providers themselves and their integration partners, who use them to accelerate the development of AI agents and make them available to customers.

The result is that CIOs will have many options about which agents to test, but will have to dedicate analysts to understand the capability, cost, and compliance trade-offs. Additionally, expect AI agent capabilities to evolve significantly over the next few years, so CIOs should continuously revisit their decisions regarding deployed AI agents, focusing on performance, benefits, and ROI.

CIOs should also watch for signs of shadow AI and employee confusion about which AI agents to experiment with on different platforms. The AI strategy should include a transparent, defined process for selecting, reviewing, evaluating, procuring, deploying, driving adoption, monitoring, and collecting end-user feedback around AI agents.

AI development capabilities for engineers and citizen builders

The apparent ease-of-use of AI code generators may lead some engineering teams to build AI agents rather than buy them from SaaS providers. But CIOs should quickly realize that coding is just one step in developing AI agents, and that aggressively pursuing a build strategy can lead to AI debt and increased AI costs.

DevOps teams can code AI agents using tools such as Claude, Codex, Lovable, and Replit — a do-it-yourself approach. Some SaaS companies are providing an alternative, with AI agent development tools that leverage the data, infrastructure, and governance baked into their platforms. Many of these development tools offer flexibility, allowing developer teams to select AI models and development environments.

Examples of new and enhanced AI development tools I saw at conferences this quarter include:

• Appian Composer and Agent Studio
• Atlassian Rovo Dev
• Boomi Companion
• Cisco Cloud Control Studio
• Domo App Catalyst
• Pega Infinity Studio
• Quickbase Pave
• Snowflake CoCo
• SAP Joule Studio.

I also reviewed Nutanix Agentic AI, a platform-as-a-service for accelerating the deployment of agentic AI workloads, and Adobe Firefly AI Assistant for creatives.

These development tools can target different audiences. Some look like low-code development tools targeted at software developers, whereas others are no-code and enable citizen developers, i.e., businesspeople, to develop applications and agents. Additionally, some of these tools support spec-driven development and generate artifacts such as product requirement documents (PRDs), data models, and testing capabilities.

Before commissioning AI development for apps and agents, CIOs should sponsor proofs of technical, data, modeling, security, and governance capabilities.

The context layer powering AI agents

Between AI agents and the enterprise’s intelligence, including structured data sources, defined business processes, and agent interactions (both human-to-agent and agent-to-agent), lies an evolving “context layer.”

This layer refers to the enterprise knowledge that AI agents draw on when evaluating signals and recommending or taking actions. Context may include a knowledge graph, a semantic layer, cleansed document repositories, and other knowledge bases.

The context layer, skills, tools, out-of-the-box agents, and governance capabilities are some areas to review where solution providers differentiate. Some examples: 

• Many support the Open Semantic Interchange, and some brand their context layers, such as the Atlassian Teamwork Graph, Boomi Knowledge Hub, and the SAP Knowledge Graph.
• Some are branding their guardrails, such as Adobe’s AI Brand Intelligence, Appian’s Private AI, and Quickbase AI Control Center.
• To manage AI agents at scale, some are extending the notion of data catalogs and other governance tools to the AI domain with products such as Boomi Connect, SAP AI Agent Hub, and Snowflake Horizon Catalog.

CIOs should recognize that while solution providers will compete on capabilities, the real “secret sauce” of the context layer lies in the company’s trusted data, well-defined business processes, and employee adoption of AI agents.

Conversational user experiences and coworkers

AI agents use the context layer, but also tap into skills, which encode the procedures they can follow, and tools, which prescribe the actions they can take. Before AI agents are ready to pilot, their governance, including permissions, approval gates, and other guardrails, must be defined. Other capabilities to look for when defining AI agents include orchestration, testing evals, and observability.

In 2025, many solution providers bolted on AI agents to their existing user experiences. This year, many solution providers showcased new conversational user experiences that employees can use instead of traditional ones built with forms, flows, reports, and static dashboards. Conversational user experiences are where AI agents and people come together, whether it’s human-in-the-middle or human augmentation.

Solution providers also grouped their AI agents into assistants or coworkers. For example, Adobe CX Coworker illustrates human augmentation, helping marketers manage campaigns with prompts and monitor their performance. SAP launched Joule Assistants across several business functions, including finance, human capital, supply chain, and customer experience. Other assistants, such as Appian AI Copilot, Atlassian Rovo, Cisco AI Assistant, Nutanix NIVA, and Snowflake CoWork, offer AI-first user experiences to assist different end-user types.

CIOs should demo these AI-first user experiences to glimpse the future of work.

Developers are already getting used to these experiences through code generators and vibe coding tools. Now, similar capabilities are being tailored across all business functions. CIOs should ramp up their change management programs to accelerate the adoption of these AI capabilities.

Solution providers are showcasing AI capabilities that can help CIOs reshape their businesses. But in Q2, there were only a few examples of how AI can help CIOs drive growth, evolve business models, or embed AI into customer-facing products. I expect to see a wave of further AI innovations that will go beyond productivity improvements and efficiencies and help CIOs pursue growth-driving digital transformation strategies.  

Sacolick travelled to conferences mentioned in this article as a guest of Adobe, Appian, Atlassian, Domo, Nutanix, SAP, and Snowflake. In addition, he was hired by Quickbase to speak at its conference.

Since the advent of generative AI, the use of AI in business has shifted from something we should do to something we must do to survive. Many companies are now working to utilize AI with the aim of improving productivity and creating value.

Here, I would like to pose a question to you all once again: “What is the fundamental factor that determines AI performance?”

Is it the AI model? Is it the AI tool? Or is it the AI agent?

Of course, I believe all of these are important. However, if we look at the long-term perspective, the competition among multiple companies to improve AI model performance will eventually level off, and we will eventually reach a point where every AI model is amazing!

In that context, what I believe is the most important factor influencing AI performance is the data accumulated by companies that connects to their unique strengths.

For example, if asked, “What do plants need to grow?” I would say “good water and light.”

Similarly, if asked, “What do people need to thrive?” I would say, “Kind words.”

Finally, “What does AI need to thrive?” The answer is “good data.”

I believe that the extent to which companies can genuinely understand the importance of this extremely simple principle and implement it with unwavering dedication will determine their ability to establish a competitive advantage and achieve sustainable growth.

AI is a mirror of data

As I’m sure you’re all aware, AI is by no means a magic wand. It is an entity that learns based on the data it is given and makes inferences within that scope. In other words, AI’s output depends heavily on the quality of its input data; one could say that AI is a mirror of data.

• If you feed it inaccurate data, it will return inaccurate results (i.e., garbage in, garbage out)
• If you feed it biased data, it will make biased judgments
• Insufficient data yields only shallow insights and suggestions

In this way, AI is not smart but rather faithful to the data. Based on this premise, it becomes clear that the essence of AI utilization lies not in which tools to use, but in what kind of high-quality data to prepare and how to utilize it.

What is good data?

So, what exactly is good data?

It goes without saying that data is useless if it is merely abundant in quantity, but on the other hand, what specific qualities must good data possess?

Generally speaking, good data possesses at least the following elements.

• Accuracy: Data containing many errors or noise will skew conclusions, no matter how advanced the analysis. It is important to minimize sensor errors, input mistakes and duplicates.
• Completeness: Are any required fields missing, and are there too many missing values? For example, if customer data is missing information such as age, region or gender, it becomes difficult to perform meaningful analysis.
• Consistency: Is data with the same meaning mixed in different formats (e.g., date formats, units, variations in notation)? This is particularly important for system integration and long-term data.
• Timeliness: No matter how accurate it is, data that is too old may not be useful for decision-making. Whether real-time data is required or historical data is sufficient depends on the use case, but it is important that the data has the appropriate freshness for the purpose.
• Relevance: If there is a large amount of data unrelated to the analysis objective, it becomes noise and leads to incorrect judgments. It is necessary to clearly define what the data is used for and ensure the data is appropriate for that purpose.
• Reliability: The data’s source and collection method must be clear, ensuring reliability and reproducibility. Data with an unknown source or that is a black box cannot be verified later.

In summary, good data is data that is accurate, has few gaps, is consistent in meaning and notation, is collected at the appropriate time, is suitable for the purpose and comes from a reliable source.

Only when the quality of this good data is guaranteed can AI produce valuable outputs. Conversely, introducing AI with unorganized data will not yield the expected results. Many complaints, such as “We implemented AI but it’s unusable” or “The AI’s accuracy isn’t improving stem from data issues.”

Data does not organize itself naturally

The key point here is that good data does not arise naturally. On the contrary, if left unattended, data will inevitably deteriorate.

• Rules become inconsistent depending on who entered the data and when
• Multiple instances of data with the same meaning exist
• Outdated data is scattered and left unattended
• Data becomes siloed by department

These conditions are likely common in many companies.

Below is an overview of our company’s data management framework.

Broadly speaking, it consists of data governance — covering roles and structures, risk management and evaluation — and data management, which encompasses data utilization cycle management and data utilization support services. Within this framework, data utilization cycle management involves:

• Needs management: We clarify the purpose and needs by asking, “What is the data being used for?” and “For whom, and in what way, does this data create value?”
• Collection: We gather the necessary data based on the defined objectives. We design the process to determine what data is required (internal/external), the level of detail and frequency of collection, and how to ensure data quality.
• Processing: We enhance the quality and prepare the data for use. This includes cleansing (correcting errors and missing values), standardizing formats, deduplicating and integrating data, processing structured and unstructured data separately, and assigning business and operational meaning to the data.
• Storage: We ensure the data is available to the right people at the right time. This involves storing data in databases or data lakes, implementing security and access controls, and managing metadata (ensuring the data is clearly identifiable).
• Utilization: This is the most critical step. The purpose of data is not merely analysis but driving action. We generate value from the data through visualization (dashboards), analysis (statistical processing, BI, AutoML, AI) and integration into business operations (automation and decision support).
• Disposal: We properly dispose of data that is no longer needed. Simply holding data can itself pose risks, such as managing retention periods, complying with laws and governance requirements, and mitigating security risks. That is why the principle of not holding data that is not used is so important.

Data management is not a one-time effort; it is an ongoing initiative that requires continuous maintenance and improvement.

The CIO must embed data management as a system within the organization and continue to implement it until it becomes firmly established.

Data management is not just the IT department’s job

Another important point is that data management is not just the IT department’s job.

Data is fundamentally generated within day-to-day operations on the front lines. Therefore:

• Who determines the meaning and definition of data
• How should input rules be standardized?
• How do we ensure data quality?

are, in essence, operational issues, business issues and management issues.

The latest Digital Skills Standard ver. 2.0, published by the Ministry of Economy, Trade and Industry in April 2026, defines the following three roles within the data management category:

• Data steward: Based on business domain knowledge, this role is responsible for operations aimed at ensuring data quality, reliability and security, as well as for promoting the adoption and establishment of data management within business divisions and frontline organizations, and for fostering data utilization. In short, they are the data quality manager and data utilization promoter.
• Data engineer: This role involves understanding the current state of data and supporting the organization’s continuous data utilization through data preparation and preprocessing in processes such as collection, integration, processing and provision, as well as the design and implementation of data pipelines. In essence, they are the implementers and operators who drive data.
• Data architect: This role involves taking a bird’s-eye view of the data structure, flow and utilization methods across the entire organization and business. By designing and continuously reviewing data architecture that encompasses the entire data lifecycle in alignment with business strategy, they ensure the successful integration of company-wide data utilization and governance—essentially serving as the overall designer of data.

The CIO is not merely responsible for establishing data storage and analysis infrastructure; they are also tasked with appropriately assigning personnel to these three roles within the company and establishing cross-departmental, company-wide tools and rules to connect data with management, business operations and daily tasks.

Ultimately, the success of data utilization depends on organizational culture

On the other hand, no matter how much progress is made in staffing, infrastructure, tools and rulemaking, data will not be utilized unless there is an organizational culture that actively drives management, business and operations based on data.

• The purpose of data entry is not understood
• Data is optimized solely for the department’s own operations
• Decision-making based on data is not valued

In such a situation, no matter how well the systems are set up, they will become mere formalities.

In contrast, in organizations where data utilization is advanced:

• Discussions are based on data
• Formulate hypotheses and verify them with data
• And continuously improve based on data

These actions occur naturally.

In other words, the essence of data management ultimately lies in creating an organizational culture that assumes the effective use of data.

Data management cannot be achieved overnight. That is precisely why it is important to start small and build on your successes.

• Organize data for specific tasks and achieve results through the use of AI
• Rolling out successful practices
• Gradually Expand the Scope

By repeating this cycle, the importance of data will permeate the entire organization.

The role expected of a CIO in the AI era

In the AI era, the role expected of a CIO has changed significantly.

Traditionally:

• Ensuring the stable operation of systems
• And optimizing costs

However, moving forward:

• We will view data as an asset and maximize its value
• Developing the data infrastructure, tools and rules that underpin AI adoption, and advancing personnel allocation and development
• And fostering an organizational culture that embraces data utilization —roles that are more directly linked to business management

In other words, the CIO must evolve into the person responsible for creating value from data.

Data is the source of competitive advantage

In the coming era, the use of AI will be a given. What will set companies apart is not whether they use AI, but what data they possess.

Data is the accumulation of a company’s past strengths and the source of future value creation. And its quality is determined by daily operations and the nature of the organization.

• AI grows by being fed good data
• And companies grow through that AI

Taking this simple principle as our starting point, we must place data management at the core of our business strategy. Isn’t that the shortest route to sustainable growth in the AI era?

CIOs are called upon to lead the way in making this a reality.

This article is published as part of the Foundry Expert Contributor Network.
Want to join?

If you’re a software developer or architect, you might wonder if programming certifications are still worth the effort, especially in the era of rapid AI-driven evolution. The short answer is, it depends.

“Certifications are shifting from a checkbox to a compass. They’re less about proving you memorized syntax and more about proving you can architect systems, instruct AI coding assistants, and solve problems end-to-end,” says Faizel Khan, lead AI engineer at Landing Point, an executive search and recruiting firm.

“In the AI era, fewer students will get trained on the job, which means they have to train themselves,” Khan says. “Certifications—especially architectural ones like AWS, Kubernetes, Terraform—are still the clearest path to do that.”

Pros and cons of programming certifications

It’s not all black and white when it comes to deciding whether to pursue programming certifications. The effort involves both pros and cons.

“In terms of pros, certifications concretely demonstrate that you have a skillset at a documented level,” says Chris Riccio, vice president of engineering at Uplevel, an engineering optimization system provider. “They also show that you’ve put in the time and effort to learn, study, and prepare.”

Programming certifications are “a useful way to validate foundational skills and show that someone understands core concepts,” says Greg Fuller, vice president of Skillsoft’s training provider, Codecademy. “They’re especially helpful for people entering the field or shifting from adjacent roles.”

Certifications offer a structured path to demonstrate proficiency, and they can confirm your ability to build and deploy in various environments, Fuller says.

These types of certifications often demonstrate baseline proficiency and continuous learning, says Reshmi Ramachandran, head of partnerships and GTM strategy for Cprime, a consultancy. “These are often key indications of proficiency for companies looking to filter large candidate pools,” she says.

Certifications really do two things, Khan adds. “First, they force you to learn by doing,” he says. “If you’re taking AWS Solutions Architect or Terraform, you don’t pass by guessing—you plan, build, and test systems. That practice matters. Second, they act as a public signal. Think of it like a micro-degree. You’re not just saying, ‘I know cloud.’ You’re showing you’ve crossed a bar that thousands of other engineers recognize.”

But there are cons, too. “In tech, employers don’t just want credentials, they want proof you can deliver,” says Kevin Miller, CTO at IFS, a maker of factory automation software. “Programming certifications can be a valuable indicator of your baseline knowledge and competencies, especially if you’re early in your career or pivoting into tech, but their importance is dwindling.”

AI tools that can generate, debug, and optimize code are already performing tasks once done by entry-level developers, “which means fewer traditional programming roles are available,” Miller says. “As a result, the job market is becoming more competitive, and certifications aren’t seen as the noteworthy achievement they once were.”

What’s more, not all certifications carry the same weight, Riccio says. “Some may reflect only familiarity rather than true expertise,” he says. “Certifications also often measure ‘book knowledge’ rather than practical experience, and they don’t always map clearly to the requirements of a specific role.”

Programming certifications “can be a helpful signal, especially for confirming baseline knowledge in areas like cloud, security, or devops, but they’re not the full picture,” says Morgan Watts, vice president of IT at 8×8, a contact center platform developer.

“I’m more interested in a candidate’s attitude and aptitude: what problems they’ve solved, what they’ve built, and how they’ve approached challenges,” Watts says. “Certifications can show commitment and discipline, and they’re especially useful in highly specialized roles. But I’m cautious when someone presents a laundry list of certifications with little evidence of real-world application.”

A certification without experience doesn’t carry much weight, Watts says, and over-certification can sometimes signal the wrong focus. “Ultimately, it’s the ability to apply knowledge, collaborate, and adapt that sets great developers apart,” he says.

Finally, certifications can age fast, Khan says. “Tech stacks evolve and a badge from two years ago may already feel dusty,” he says. “And some certifications are paper-thin—multiple-choice exams that don’t prove you can debug production at 2 a.m. So, the risk is you collect badges but still can’t ship.”

Which certifications will get you noticed?

Despite the drawbacks, certifications are still very much in demand, and some carry more weight than others.

The most in-demand certifications are typically platform-based—Amazon Web Services (AWS), Google Cloud Platform (GCP), Microsoft Azure, and others, Riccio says. “Many of these platforms provide managed services that integrate with existing systems or serve as the glue between them,” he says. “Today’s engineering teams aren’t just building standalone systems in isolation; they’re using other systems to store data, orchestrate business workflows, and connect applications.”

A certification that demonstrates the ability to build solutions on these platforms can put a development professional ahead of the competition, Riccio says.

“The certifications I see in highest demand tend to reflect the evolving tech landscape,” Watts says. “Cloud certifications from AWS, Azure, and GCP are incredibly valuable, especially as distributed systems become the norm.”

Also in demand are certifications for devops and CI/CD tools including Kubernetes, Docker, and Jenkins, Watts says, “because deployment automation and reliability are critical at scale. Also, with AI reshaping development, we’re seeing growing interest in certifications around machine learning, data science, and AI model integration. These certifications stand out because they align directly with the skills that teams need to move faster and more intelligently.”

On the AI front, certifications in TensorFlow and other machine learning platforms are gaining traction as organizations look to embed AI across the development process, Watts says. “These are the certifications that align closely with where modern engineering is headed—scalable, secure, and AI-enabled,” he says.

And then there are cybersecurity credentials that continue to be in high demand. Security certifications, such as CompTIA Security+ or Certified Ethical Hacker, “have become essential as every company faces increasing cyber threats and compliance requirements,” Miller says.

“Core programming certifications are still a bit niche, but the adjacent skills, like those that help developers deploy, secure, and scale their code, are driving demand,” Fuller says. “Companies want developers who understand the full lifecycle, not just how to write code.”

Also see: The best Java certifications for software developers.

Certifications in the hiring process

Experts are clear that programming certifications alone will not get you the job. But they do play a role in the hiring process.

“The information technology world is characterized by rapid and continuous evolution, including the skills and knowledge required to work in the field,” says Diane Rafferty, managing director of the National Technology Group at Atrium, a global talent solutions and extended workforce management firm.

“Certifications not only prove that you have the skills and knowledge needed, but they also show employers that you’re invested in your education and career growth,” Rafferty says. “They can give you a competitive edge when looking for a job, as many companies now require candidates to have them.”

Certifications are one part of the hiring equation, “but never the only part,” Watts says. “They help validate that a candidate has taken the time to build foundational knowledge, and that’s a good sign. But I put more weight on how a person thinks, solves problems, and contributes to the team. I look for people who are curious and proactive, who are learning because they want to, not just because a course told them to.”

Certifications can also play a valuable role in retention, Watts says. “I encourage team members to pursue growth, and when they invest in their own development, the whole organization benefits,” he says. “But again, it’s that balance of knowledge, attitude, and applied experience that really moves the needle.”

Certifications “may allow you to breeze through the initial résumé screening process, potentially getting you to the next stage faster,” Riccio says. “At a minimum, they will set your profile apart from the rest of the pack. They also demonstrate that you’ve reached a baseline level of expertise, allowing hiring managers to quickly evaluate whether you have the skills for the role.”

Employers today “care far less about whether someone has passed an exam and far more about whether they can apply knowledge effectively in real-world situations, leverage AI tools, and solve complex problems,” Miller says. “A certification might get someone an interview, but being able to demonstrate problem-solving skills, teamwork, and adaptability will really make them stand out.”

Popular programming certifications

The following certifications consistently rose to the top in my conversations with tech leaders and hiring managers.

AWS Certified Developer—Associate

Showcases skills and knowledge in developing, optimizing, packaging, and deploying applications, using CI/CD workflows, and identifying and resolving application issues, according to AWS. This certification is said to be a good starting point on the AWS certification journey for professionals in IT or cloud developer job roles.

Azure Developer Associate

This certificate from Microsoft is intended for developers participating in all phases of cloud development, including design, deployment, maintenance, and monitoring. The course teaches developers how to create end-to-end solutions in Microsoft Azure, using the Microsoft Learn Sandbox environment to access Azure resources and services.

Certified Kubernetes Application Developer (CKAD)

This certification was created by the Linux Foundation and Cloud Native Computing Foundation. It demonstrates that candidates can design, build, and deploy cloud-native applications for Kubernetes.

Certified Secure Software Lifecycle Professional (CSSLP)

This certification, from ISC2, focuses on secure software development practices. It recognizes leading application security skills and demonstrates advanced technical skills and knowledge needed for authentication, authorization, and auditing throughout the software development lifecycle.

Databricks Certified Machine Learning Professional

Professionals learn about the latest data and AI techniques and how they can use the Databricks Data Intelligence Platform to build a variety of solutions across data engineering, data warehousing, data science, and AI.

Professional Cloud Architect

This certification from Google assesses the ability to design and plan a cloud solution architecture, manage and provision the cloud solution infrastructure, design for security and compliance, analyze and optimize technical and business processes manage implementations of cloud architecture, and ensure solution and operations reliability.

Terraform Associate

This certification from HashiCorp is for cloud engineers specializing in operations, IT, or development who know the basic concepts and skills associated with Terraform. It validates foundational skills in using Terraform for infrastructure as code development.

A portmanteau of “development” and “operations,” devops emerged as a way of bringing together two previously separate groups responsible for the building and deploying of software.

In the old world, developers (devs) typically wrote code before throwing it over to the system administrators (operations, or ops) to deploy and integrate that code. But as the industry shifted towards agile development and cloud-native computing, many organizations reoriented around modern, cloud-native practices in the pursuit of faster, better releases.

This required a new way to perform these key functions in a more streamlined, efficient, and cohesive way, one where the old frustrations of disconnected dev and ops functions would be eliminated. With two groups working together, developers can rapidly roll out small code enhancements via continuous integration and delivery rather than spending years on “big bang” product releases.

Devops was born at cloud-native companies like Facebook, Netflix, Spotify, and Amazon; but it’s become one of the defining technology industry trends of the past decade, primarily because it bridges so many of the changes that have shaped modern software development.

As agile development and cloud-native computing have become ubiquitous, devops has enabled the entire industry to speed up its software development cycles. Thus, devops has now thoroughly infiltrated the enterprise, especially in organizations that rely on software to run their business, such as banks, airlines, and retailers. And it’s spawned a host of other “ops” practices, some of which we’ll touch on here.[JF1] 

Devops practices

Devops requires a shift in mindset from both sides of the dev and ops divide. Development teams should focus on learning and adopting agile processes, standardizing platforms, and helping drive operational efficiencies. Operations teams must now focus on improving stability and velocity, while also reducing costs by working hand in hand with the developer team.

Broadly speaking, these teams need to all speak a common language and there needs to be a shared goal and understanding of each other’s key skills for devops to thrive.

More specifically, engineers Damon Edwards and John Willis created the CALMS model to bring together what are commonly understood to be the key principles of devops:

• Culture: One that embraces agile methodologies and is open to change, constant improvement, and accountability for the end-to-end quality of software.
• Automation: Automating away toil is a key goal for any devops team.
• Lean: Ensuring the smooth flow of software through key steps as quickly as possible.
• Measurement: You can’t improve what you don’t measure. Devops pushes for a culture of constant measurement and feedback that can be used to improve and pivot as required, on the fly.
• Sharing: Knowledge sharing across an organization is a key tenet of devops.

“Who could go back to the old way of trying to figure out how to get your laptop environment looking the same as the production environment? All these things make it so clear that there’s a better way to work. I think it’s very tough to turn back once you’ve done things like continuous integration, like continuous delivery. Once you’ve experienced it, it’s really tough to go back to the old way of doing things,” Kim told InfoWorld.

What is a devops engineer?

Naturally, the emergence of devops has spawned a whole new set of job titles, most prominent of which is the catch-all devops engineer.

Generally speaking, this role is the natural evolution of the system administrator — but in a world where developers and ops work in close tandem to deliver better software. This person should have a blend of programming and system administrator skills so that he or she can effectively bridge those two sides of the team.

That bridging of the two sides requires strong social skills more than technical. As Kim put it, “one of the most important skills, abilities, traits needed in these pioneering rebellions — using devops to overthrow the ancient powerful order, who are very happy to do things the way they have for 30 to 40 years — are the cross-functional skills to be able to reach across the table to their business counterparts and help solve problems.”

This person, or team of people, will also have to be a born optimizer, tasked with continually improving the speed and quality of software delivery from the team, be that through better practices, removing bottlenecks, or applying automation to smooth out software delivery.

The good news is that these skills are valuable to the enterprise. Salaries for this set of job titles have risen steadily over the years, with 95% of devops practitioners making more than $75,000 a year in salary in 2020 in the United States. In Europe and the UK, where salaries are lower across the board, 71% made more than $50,000 a year in 2020, up from 67% in 2019.

Key devops tools

While devops is at its heart a cultural shift, a set of tools has emerged to help organizations adopt devops practices.

This stack typically includes infrastructure as code, configuration management, collaboration, version control, continuous integration and delivery (CI/CD), deployment automation, testing, and monitoring tools.

Here are some of the tools/categories that are increasingly relevant in 2025, and what is changing:

• CI/CD and delivery automation: Traditional tools like Jenkins remain in many stacks, but newer orchestration tools and CLI-driven or GitOps-centric platforms are growing in importance (e.g. ArgoCD, Flux, Tekton). Also, platforms that integrate more tightly with monitoring, secrets management, drift detection, and policy enforcement are gaining traction.
• Security, compliance, and devsecops tooling: Security tools are increasingly integrated into devops pipelines. Expect to see more use of static analysis (SAST), dynamic testing (DAST), dependency and supply chain scanning (SCA), secret management, and policy as code. The push is toward embedding security earlier and bridging gaps between dev, security, and machine learning teams. (InfoWorld:)
• AI  and automation augmentation: AI-assisted tools are increasingly part of tooling stacks: auto-suggestions in CI/CD, anomaly detection, predictive scaling, intelligent test suite selection, and more. The hope is that these tools will reduce manual interventions and improve reliability. Tools that are “AI ready”—that is, they integrate well with AI or have mature built-in automation or assistance—increasingly stand out from the pack.

Devops challenges

Even as devops becomes more widely adopted, there remain real obstacles that can slow progress or limit impact. One major challenge is the persistent skills gap. The modern devops engineer (or team) is expected to master not just source control, CI/CD, and scripting, but also cloud architecture, infrastructure as code, security best practices, observability, and strong cross-team communication. In many organizations these capabilities are uneven: some teams excel, others lag behind. A 2024 survey showed that while 83% of developers report participating in devops activities, using multiple CI/CD tools was correlated with worse performance — a sign that complexity without deep expertise can backfire.

Toolchain fragmentation and complexity is a related issue. Devops toolchains have sprouted into a sometimes bewildering array of packages and techniques to master: version control, CI build/test, security scanning, artifact management, monitoring, observability, deployment, secret management, and more.

The more tools you have, the more difficult it becomes to integrate them cleanly, manage their versions, ensure compatibility, and avoid duplicated effort. Organizations often get stuck with “tool sprawl” — tools chosen by different teams, legacy systems, or overlapping functionalities — which introduce friction, maintenance burden, and sometimes vulnerabilities.

Finally, although devops has spread far and wide, there is still cultural resistance and alignment. Devops isn’t just about tools and processes; it’s about collaboration, shared responsibility, and continuous feedback. Teams rooted in traditional silos (dev vs ops, or security separate) may resist changes to roles and workflows. Leadership support, communication of shared goals, trust, and allowance for continuous learning are all necessary.

Many CIOs focus too much on tools or implementation first, rather than organizational culture and behaviors; but without addressing culture, even the best tools or processes may not yield the hoped-for velocity, quality, or reliability. Organizations that succeed here tend to have proactive strategies: dedicated training programs, mentorship, internal “guilds,” pairing junior and senior engineers, and making sure leadership supports ongoing learning rather than one-off bootcamps.

Why do devops?

Whoever you ask will tell you that devops is a major culture shift for organizations, so why go through that pain at all?

Devops aims to combine the formerly conflicting aims of developers and system administrators. Under its principles, all software development aims to meet business demands, add functionality, and improve the usability of applications while also ensuring those applications are stable, secure, and reliable. Done right, this improves the velocity and quality of your output, while also improving the lives of those working on these outcomes.

Does devops save money — or add cost?

Devops teams are recognizing that speed and agility are only part of success — unchecked cloud bills and waste undermine long-term sustainability. Waste in devops often comes in the form of “devops debt”— idle cloud capacity, dead code, or false-positive security alerts—which was called a “hidden tax on innovation” in recent Java-environment studies.

 Embedding finops practices can help fight these costs. Teams should shift left on cost: estimating costs when spinning up new environments, resizing instances, and scaling down unused resources before they become runaway expenses.

How to start with devops

There are lots of resources for help getting started with devops, including Kim’s own Devops Handbook, or you can enlist the help of external consultants. But you have to be methodical and focus on your people more than on the tools and technology you will eventually use if you want to ensure lasting buy-in across the business.

A proven route to achieving this is a “land and expand” strategy, where a small group starts by mapping key value streams and identifying a single product team or workload for trialing devops practices. If this team is successful in proving the value of the shift, you will likely start to get interest from other teams and from senior leadership.

If you are at the start of your devops journey, however, make sure you are prepared for the disruption a change like this can have on your organization, and keep your eye on the prize of building better, faster, stronger software.

---

More on devops:

• Devops debt: The hidden tax on innovation
• 10 big devops mistakes and how to avoid them
• Smarter devops: How to avoid deployment horrors

Almost every company has a board or executive AI mandate. Vendors are rolling out agentic AI platforms. The pressure to move is intense.

But the reality on the ground looks different. Eighty-three percent of organizations say data quality is their top AI challenge, and 74% struggle to demonstrate ROI, according to Lopez Research. And only 21% report having a mature governance model for AI agents, per Deloitte’s 2026 State of Enterprise AI report.

“Agentic AI is real, and vendors’ offerings are very real, too,” says Boris Evelson, vice president and principal analyst at Forrester. “However, most enterprises are still not ready to adopt at scale.”

Dave Hilborn, who leads West Monroe’s Organization, People & Change practice, frames it as a race with three arrows moving forward — one representing AI and tech evolution, one representing organizations and people, and one representing data. “The AI arrow is far out ahead,” he says. “That delta is the readiness gap.”

The gap isn’t the technology. It’s the foundational work most organizations haven’t done: data readiness, operating models, governance, skills, and culture. The companies making progress aren’t waiting for vendors to solve these problems. They’re tackling the unglamorous work themselves.

AI doesn’t tolerate ambiguity

AI readiness can be framed across six levels — from data foundation at the base to reinvented business experiences at the top, says Afshean Talasaz, former CIO at Colonial Pipeline and now an executive advisor. One of the key areas that doesn’t always get the attention it needs is the operating model.

“The technology playbooks of the past don’t work in the AI world,” Talasaz says. “Those areas were able to tolerate more ambiguity between business and tech teams. AI doesn’t tolerate the same level of ambiguity. It needs clarity.”

That demands a different kind of partnership between IT and the business. AI systems learn from data — records and measurements of what’s actually happening in the business — and then operate within business processes. Unlike traditional software, which is built based on user requirements, AI is sandwiched between the business that produces the data and the business that consumes the outputs.

“AI is requiring IT and business teams to work more closely together, to be clearer about what AI will and will not do — that really close partnership is crucial,” Talasaz says. “It’s not something that will always naturally evolve. It requires a lot of intentionality about how teams need to work together to deliver outcomes.”

The AI questions CIOs must answer aren’t just technical. Do we have the right operating model? Have we balanced governance and standard operating procedures within the model? Have we organized teams appropriately? All this must be designed within the context of what the business actually needs.

Too many organizations are bolting AI onto existing processes without redefining roles or workflows, Forrester’s Evelson. “Organizations can either incrementally enhance existing workflows by augmenting capabilities with AI or pursue a more transformative approach by redesigning the process end-to-end.”

The companies getting value are doing the latter.

Data debt comes due

Data readiness remains the most common barrier to scaling AI. “We’ve never fixed this data quality problem in most organizations,” says Maribel Lopez, founder and principal analyst at Lopez Research, “and it comes back to haunt a company in spades as they move to AI.”

At Levi Strauss, the foundational work came first. “If you think about the Levi’s business, it’s quite complex — 100 countries, over 3,000 stores, multiple business models,” says Jason Gowans, the company’s chief digital and technology officer. “You can imagine the complexity of gathering all that data to understand how the business is performing. The idea of this single source of truth — that’s been the biggest thing.”

Levi’s now has more than 1,100 standard operating procedures that govern how work gets done on top of SAP. “That’s fertile material to feed to LLMs on how work gets done,” Gowans says.The results are tangible: partner onboarding that once took three to six months to set up EDI exchanges now takes days.

At contract manufacturing company Jabil, Chase Christensen, segment CIO, took a similar path. “We had to get everyone to understand where the source data resides, put tech in place so consumption is easier, and drive ownership around data and decision rights — so 140,000 employees don’t feel empowered to create their own data sources that fall out of line.”

The data challenge goes beyond quality, Evelson notes. Most organizations’ data isn’t AI-ready; it hasn’t been prepared for how AI systems consume and learn from information. “Data is siloed, poorly governed, and hard to discover, integrate, and trust,” he says.

Forrester research shows that 45% of data and analytics decision-makers were adopting vector databases in 2025, and 53% were adopting graph databases — investments that signal recognition of how much data architecture needs to evolve. The firm recommends a balanced approach: roughly 48% of AI spending on foundations such as data management and engineering, and 52% on consumption, including analytics, governance, and applications.

But even as organizations work to prepare existing data, AI is creating new challenges. Users leveraging AI tools are generating new forms of data and information that never make it into corporate databases, West Monroe’s Hilborn notes.

“There are explosions of new data, content, and insights being created on the periphery of these data lakes,” he says. “The challenge is how do you capture that and leverage it.”

Who’s sponsoring this?

Even when data is in order, many AI initiatives stall due to how they’re sponsored and funded.

“Enterprise data, analytics, and AI programs succeed when business CxOs sponsor them because they are accountable for business outcomes, not just technology delivery,” Forrester’s Evelson says. “IT-led initiatives often become siloed or tool-centric, whereas business sponsorship ensures alignment to enterprise strategy, prioritization of end-to-end use cases, and a focus on decisions and actions rather than insights alone.”

Too often, AI is still treated as a series of disconnected use cases rather than a sustained, multi-year investment. Evelson calls this the “use case trap” — organizations overindex on individual projects and miss the enterprise-wide compounding impact. That leads to fragmented priorities, inconsistent adoption, and difficulty demonstrating ROI.

Leadership readiness is a distinct layer of AI preparedness, Talasaz says. “Are leaders prepared to provide a vision of reinvented business experiences that become the north star?” he asks. “Leadership teams, at various levels of the organization, need to articulate what a reinvented business looks like so teams have the direction and support to build differentiating capabilities.”

Levi’s offers a counterexample. AI is a CEO priority there. At the last quarterly offsite, the execs were building agents. “When you’re committed to upskilling the workforce, you’re better served to answer how to rewire processes with AI at the core,” Gowans says. “It starts at the top. It has to be an exec priority.”

Fear, literacy, and two types of AI

Technical talent is only part of the equation. Organizations also need to address change management.

“We saw it with the AI boom — fear about jobs, not knowing what AI did,” says Jabil’s Christensen. “The key is demystifying AI. We doubled down and focused on AI literacy. We want everyone to understand how it was put together, and that removed a lot of that fear. That’s been the biggest hurdle.”

Different types of AI require different skills and governance, Talasaz says. “General use focuses on productivity on the desktop,” he says. “Integrated AI — industrial-capable AI embedded within core business processes — requires different skills, capabilities, and governance.”

For desktop AI, training and guardrails help employees be successful — what Talasaz calls “bumpers,” like in bowling. Organizations need to help employees through reskilling and guidance. “You have tools in a toolbox,” he says. “It’s important to know when to use a power tool versus when you need a screwdriver.”

But for integrated AI embedded in core processes, the stakes are higher. “Business leaders responsible for business outcomes based on AI-driven processes need to be fully aware of both the benefits and risks that come along with using these tools,” Talasaz says.

That distinction matters for governance, too. Lower-, medium-, and high-risk AI use cases may require different ways of working and different risk management approaches. “Deploying AI in potentially high-risk or high-cost areas of the business requires a higher level of rigor,” Talasaz says. “That’s different than building something that helps write my emails.”

From POC to production

Perhaps the biggest readiness gap is the transition from proof of concept to production. “It requires such a different approach,” Talasaz says. “A successful proof of concept can create a lot of excitement, but when teams are unprepared to build and scale, it can create the potential to over-promise and under-deliver.”

The operating model that works for experimentation doesn’t work for production at scale. Proofs of concept are designed to demonstrate the efficacy of ideas and the underlying technology. But building, scaling, and sustaining technology in the business requires operating models, standards, roles, and skills that many organizations haven’t developed. Intentionally designed operating models reduce the cost of learning, improve execution, and increase delivery velocity, says Talasaz.

But there’s no one-size-fits-all answer. “A business that needs to build capabilities in a marketplace moving very fast requires one kind of operating model,” Talasaz says. “A business that can take longer to develop business capabilities and adapt to market changes can choose a different operating model. It’s important to design ways of working tailored to what the business needs and the speed at which the business needs to leverage technology to be successful.”

Jabil is navigating this journey as part of its move to SAP’s cloud ERP through RISE, scaling from $29 billion to $34 billion in revenue while keeping selling, general, and administrative (SG&A) expenses relatively flat — in part by layering generative AI onto predictive analytics capabilities built over years.

“We started years ago with computer vision to drive product quality,” Christensen says. “As gen AI blew up, we took the predictive analytics we had built over the years and imbued them with gen AI. We’ve implemented the basics, and now we’re looking for complex scenarios.”

Governance built in, not bolted on

Governance is often treated as a policy document or committee. It should be embedded in the operating model itself, Talasaz argues.

“The operating model doesn’t always get the attention it needs,” he says. “Policies and committees are useful, but they should handle larger enterprise risks. Most of the governance should be embedded in the operating model to ensure you’re getting outcomes you want.”

That might mean peer review built into the development process, bias checks before deployment, or clear escalation paths for high-risk use cases. When governance is separate from the operating model, it tends to slow things down. When it’s integrated, it becomes how work naturally gets done, says Talasaz.

Governance at the agent level matters, too, Levi’s Gowans says. “Know what agents have been deployed, who authored them, and who’s responsible,” he says, noting that the company has established a registry to understand what agents it has operating within its networks.

The challenges of AI governance are unique, Lopez of Lopez Research says. “Very few people have the governance stack required to say they did the right things with AI,” she says. “Non-human identity and access control is totally different and, frankly, evolving so quickly that no one knows what to do.”

The challenge is ultimately a trade-off, Forrester’s Evelson says. “Push agentic AI capabilities too far, and you risk creating a governance and compliance nightmare,” he says. “Tighten controls too aggressively, and you stifle innovation. Best practices for striking the right balance are still being discovered.”

It takes a team

The AI readiness gap isn’t about technology — it’s about the work organizations have been deferring for years. Data quality. Operating models. Executive sponsorship. Skills and culture. Governance embedded in process.

“Once you progress from everyone using Copilot to putting agents in production, then you realize the need for business context,” Gowans of Levi Strauss says.

It’s a shared journey requiring all teams to understand what’s required, Talasaz says. “It involves helping people understand what it takes from all sides — the technology itself, the operating model, the skills and talents needed — but also working with business leaders on the art of the possible,” he says. “Helping them understand both the benefits and the responsibility of deploying this tech.”

A colleague of his calls AI “the ultimate executive team sport.”

“It requires people to do it well and manage it,” Talasaz says.

AI tools today enable faster processes, leaner operations and lower costs, making efficiency wins the new baseline. However, for many businesses, the strategy stops at those first wins.

This has created a growing leadership blind spot: Once you achieve AI ROI, how do you make the most of it? If there is no clear reinvestment strategy, AI gains burn out quickly and disappear into the business without meaningfully compounding their value.

For CIOs, the next challenge is not just proving AI can make the business more efficient but deciding how those gains can build a stronger company and sustain growth over the long term.

Start by investing in a crystal ball

One of the smartest ways to reinvest AI gains is to improve how the business evaluates what is worth building in the first place.

Leaders who chase “cool” use cases without defining the business impact or path to ROI upfront often end up with systems that drain funds without creating compounding returns. Instead, a clear reinvestment strategy uses AI to assess the strongest use cases before scaling up.

AI tools today can help teams move from idea to prototype to impact analysis much faster than before. That makes it easier to identify which projects have a credible path to ROI and which ones can be filed away. Access to these quick insights allows businesses to test whether a use case has real value before committing larger engineering or model costs.

This is especially crucial right now as AI is becoming more costly as businesses scale it. What looked inexpensive in early pilots can become far pricier once it is embedded in day-to-day work and as AI providers tokenize and meter its use. The more central AI becomes, the more intentional leaders need to be about where it is used, what it actually returns and how to reinvest those gains.

Not every workflow belongs in the same model. Not every task needs an agent. As AI vendors mature and monetization models evolve, the businesses that will win will be the ones that make those distinctions early, reinvest accordingly and keep building ahead of customer needs rather than reacting to them. Not every workflow belongs in the same model. Not every task needs an agent.

Cycle ROI gains back into tooling

Once AI activations start to show dividends, it’s time to reinvest in stronger tooling. This should include new AI tools that continue to advance the business, as well as continued investment in what has already worked. That compounding effect is ultimately what separates businesses that sustain AI-driven growth from those that plateau after early wins.

I’ve seen firsthand the benefits of investing in new tools that make AI more usable, repeatable and valuable in workflows. For example, automated product management tools enable rapid prototyping and product rationalization. Decision intelligence platforms can help teams simulate scenarios. Customer behavior modeling tools can help predict churn and shift customer demand patterns. These advanced solutions can help teams move from an idea to a working concept in days instead of months.

Smart reinvestment is about building the right technical mix for the outcomes the business needs, rather than funding more AI for its own sake. To maximize impact, start with tooling for governance and upskilling.

1. (Re)invest in governance

As AI usage spreads and matures across teams, products and functions, a strategic policy framework becomes all the more vital. CIOs should work to reinforce the governance foundations already in place so they can support broader adoption, rather than rebuilding new policy from scratch each time AI usage expands. This means reinvesting in shared standards, oversight mechanisms and supporting roles that make governance more durable and practical over time.

Without doubling down on governance, businesses risk creating siloed, disconnected pockets of experimentation. Those pockets quickly become expensive to monitor and difficult to secure, creating further risk to consistency, compliance and trust. The consequence is often wasted spend as experiments stall or overlap, or outcomes that are too fragmented to scale.

When businesses keep governance investment at the center of their reinvestment strategy, it becomes a force multiplier. It reduces duplication across teams, creates more commonality across products and makes it easier to expand AI use without increasing fragmentation or risk.

2. Empower employees to grow

Smart tools only create real value when people are equipped to use them well. That is why reinvestment should go beyond technology alone.

As AI tools become more powerful and accurate, the skills barrier to building something useful is dropping. Employees can get much closer to a viable concept much faster with AI, but that only works if businesses create learning pathways, academies and practical enablement that help teams use these tools well.

Smarter tooling can help product, operations and technology teams collaborate with fewer layers between idea and execution. As employees build new skills, they can stay closer to a single initiative from start to finish. That reduces handoffs, empowers employees to learn new skills and offers a more direct path from the original idea to the final result.

Let AI ROI fund your fight against siloes

Over the next few years, the businesses that pull ahead are not simply going to be the ones with the most AI pilots or the biggest efficiency gains. They will be the ones that invest AI ROI in bridging what has long been disconnected: systems, teams, workflows and ecosystems.

In telecom, for example, AI is already creating savings inside billing operations and other back-office work tied to the BSS layer. The smart move for telcos is not to stop at those savings, but to reinvest them in connecting their BSS and OSS, where fragmentation and siloes have long slowed telcos down.

Think about what that means in practice: instead of billing, service configuration and network operations functioning as separate systems with separate handoffs, AI can help orchestrate them. That makes it easier to move from order to activation to support with less internal friction, better visibility and fewer breakdowns between what was sold and what is actually delivered.

For the customer, that means a broadband outage, plan change or installation appointment is handled as one connected journey rather than a chain of handoffs. The outcome is a more connected operating model that makes the customer experience feel far less complex.

The same logic applies across industries. In banking, a customer with a mortgage, checking account and credit card at the same institution is often still treated as three separate relationships – because the underlying systems do not communicate. AI orchestration can change that, giving banks a unified view of the customer and employees the context to act on it.

Not using AI to do the same work faster, but using AI dividends to build a business that works better. That is what smart investment looks like.

ROI is just the start

AI can absolutely free up capital. That, however, is only the first chapter.

The bigger story is what leaders choose to do next: reinvest in better tooling, more consistent governance, smarter workforce enablement and operating models built to connect across silos. The payoff will be a more resilient, agile business ready for what’s next.

This article is published as part of the Foundry Expert Contributor Network.
Want to join?

While many technology companies have slowed hiring or even launched significant layoffs, that doesn’t mean job opportunities have dried up for software developers. In fact, skilled developers—particularly those with knowledge of AI—are in demand in other industries.

The key to success for developers looking to snatch up these roles is to be well-prepared to meet the needs of potential employers in a variety of sectors.

“The demand for developers in non-tech sectors is real and growing, but the roles look different from what you’d find at a software company,” says Pragati Awasthi, assistant teaching professor of AI and data science at Drexel University.

“Across all these sectors, the common thread is that software is no longer a support function; it is embedded in core operations,” Awasthi says. “The developer in these environments is often the person translating domain-specific business problems into technical solutions, which requires a different profile than a pure product engineer at a tech firm.”

Opportunity knocks

The tech industry has long been a mainstay as far as employing software developers. But as these businesses trim staffs in efforts to cut expenses, that has impacted the hiring landscape. Even as the tech sector scales back, however, companies in industries such as financial services/fintech, healthcare/healthtech, retail/ecommerce, and manufacturing are looking to acquire programming talent.

“The unifying factor is data complexity,” Awasthi says. “These industries generate large volumes of sensitive, regulated, or operationally critical data, and they need developers who can build and maintain systems that handle it responsibly.”

While recruiting firm Summit Search Group has placed developers in roles with technology companies, “it is just as common to recruit them for roles outside this niche,” says Matt Erhard, managing partner at the company. “There are actually a fairly wide variety of roles available for developers in industries beyond tech,” Erhard says.

For example, in financial services Summit Search Group has seen significant hiring for back-end and data engineers who can build and maintain fraud detection systems, digital banking platforms, and regulatory tools, Erhard says. In healthcare, companies are hiring developers to build AI-driven diagnostics platforms and patient portals, or to work with systems that manage electronic health records, he says.

In manufacturing and industrial companies, developers are needed for systems integration and embedded software related to predictive maintenance, Internet of Things (IoT) systems, and smart factories. And in retail and ecommerce, there’s strong demand for full-stack developers and data developers who can handle logistics systems, omni-channel platforms, and personalization engines, Erhard says.

“One significant function where we’ve been placing developer talent lately is in developing business systems and internal applications,” Erhard says. These roles often have titles such as systems engineer or application developer, and professionals are hired to handle tasks such as customizing customer relationship management (CRM) or enterprise resource planning (ERP) platforms, building workflow automation tools or modernizing legacy systems, he says.

Other core functions for which Summit Search Group has placed a lot of developers include data, analytics, and AI-enablement. “That could be directly involved with data engineering or in building tools like reporting systems and ETL [extract, transform, load] pipelines,” Erhard says.

The firm also has handled searches for developers who can build and maintain customer-facing products for banking, healthcare, and retail companies, such as mobile apps or digital platforms customers can use to interact with companies.

Randstad Digital, a provider of global technology talent, sees demand for roles including web developers, system developers, and app developers. “These professionals would work on anything from customer-facing platforms to internal tools,” says Michael Morris, global head of platform and talent at the company. “Non-tech companies are also often hiring roles like software architecture and devops to help scale existing technology. These involve being more ingrained in the business, like building a supply chain system for a retailer, rather than creating individual tech products like you would at a technology company.”

Prep for success

To increases the chances of success at landing developer jobs outside of the tech industry, development professionals would be wise to follow some good practices.

Boost AI skills

One best practice is to boost skills in using AI-powered tools and get familiar with all things AI.

“Get fluent with AI-assisted development and its limits,” Awasthi says. “This is not optional. Organizations across every sector expect developers to use AI coding tools productively. But the more durable skill is knowing when AI output is wrong, incomplete, or unsuitable for a regulated context. That critical evaluation capacity is what non-tech employers are increasingly trying to hire.”

AI does not necessarily replace the need for human developers so much as it changes the skills profile for those roles, Erhard says. “The biggest difference in recent years is that AI literacy is now a non-negotiable,” he says. “At minimum, developers today need to understand concepts like prompt engineering and how to use AI tools to improve their efficiency.”

One thing many job candidates don’t expect is that the rise of AI has also increased the importance of high-level skills such as problem framing, system design, and cross-functional communication,” Erhard says. “Essentially, if something is related to development but too complex or nuanced for an AI to handle effectively, then the demand is high for human developers who have that expertise,” he says.

Candidates who land roles consistently have experience building AI-augmented workflows along with standard coding skills, Erhard says. “Employers increasingly expect to hire developers who can leverage AI, so demonstrating this experience on your résumé can be very beneficial,” he says.

Gain domain knowledge

Summit Search Group is seeing high demand for developers with deep domain knowledge in an organization’s specific industry. “So, for instance, if someone is both an experienced developer and has expertise in healthcare compliance, or financial regulations, then those candidates tend to be very sought after,” Erhard says.

Domain fluency is an underrated skill, Awasthi says. “A developer who understands healthcare compliance, financial regulation, or manufacturing process logic is significantly harder to replace than one who only writes clean code,” she says. “AI can generate boilerplate. It cannot navigate a HIPAA audit or explain a model’s output to a compliance officer.”

Development professionals should “pick an industry and learn it seriously; not just the technology stack but the regulatory environment, the business model, and the actual problems practitioners face,” Awasthi says. “A developer who has read about HIPAA, or spent time understanding credit risk, is immediately more valuable in those hiring contexts.”

It’s also vital to demonstrate real-world, practical application of skills, not just credentials. “The strongest candidates have projects in their portfolio that directly tie to and solve real business problems,” Erhard says.

Acquire soft skills

And then there are the soft skills that are becoming more of a differentiator than they were in the past. As AI handles more routine coding, human developers are expected to make more architectural decisions and collaborate across departments, Erhard says. “Strong communication and problem-solving skills are critical for many of the developer roles that we’re filling today,” he says.

While technical skills are still relevant for developers using and managing AI tools, “they also need to develop the skill of ‘deeper thinking’ and learn how to think one step ahead,” Morris says. “This includes skills like system design mastery—understanding the macro view and learning how microservices, databases, and third-party APIs interact securely and efficiently.”

They also should become deeply fluent in the AI coding tools commonly used in their particular industry, with a strong understanding of how to prompt them for optimal output, Morris says. Product context awareness is also useful. “AI doesn’t know what the customer wants, but you do,” Morris says. “Understanding the business problem and the end-user experience is a requirement for being able to guide LLMs.”

Master debugging and incident response

Developers looking to break into non-tech sectors also should develop skills in debugging and incident response, Morris says. “Complex systems with multiple AI agents can, and will, fail, which means companies need humans to trace logic flaws to get the system back on track,” he says. “A mastery of root-cause analysis is a critical skill.”

“Security, compliance, and reliability are very important in non-tech industries like finance and healthcare,” says Rohit Agarwal, co-founder of Zenius, a remote hiring company. “So employers want developers who also know regulatory environments well.”

Network and keep learning

To successfully pivot from jobs at tech companies, “continuous learning, upskilling, and building hybrid skills that combine technical and business knowledge are essential,” Morris says. “With the right preparation, tech professionals can adapt and continue to thrive in meaningful, dynamic careers.”

It’s also a good idea to join talent communities in fields of interest and “engage with other members in conversations that increase your knowledge through the collective intelligence of the community,” Morris says. “Take advantage of AI skilling opportunities relevant for your role, or better yet, where you want to go next. Experiment with the technology either on your own or through structured programs.” Ultimately, be curious and proactive, he says.

“I’d also recommend developers not to ignore referrals, direct outreach, and industry-specific communities during job search,” Agarwal says. “There are often a lot more opportunities available than the ones posted online.”

A product manager on my team recently asked me where we were seeing the most issues across the engineering team. Instead of guessing, I had an engineering lead point Claude at our Jira via an MCP connector and look at the bug patterns himself.

One team had a wildly disproportionate share of tickets — about 50% of their sprint time was spent on “bugs,” versus roughly 25% for everyone else. The headline number suggested a quality problem.

It wasn’t. When we layered in the context around those tickets, almost none of them were bugs. They were manual workarounds for a missing product capability: customers asking us, one request at a time, to restore items they had accidentally deleted. Not shipping an item restore feature was burning roughly 1.5 engineers’ worth of capacity. I went back to our product team and said, “Build this, and you reclaim a person and a half.”

The analysis took 45 minutes. It was only possible because our data was already organized, tagged by team, connected to contributors, accessible through MCP and protected by role-based access. None of that is “AI.” All of it is the layer underneath AI that almost nobody invests in first. That’s probably because the investment is unglamorous: updating data dictionaries, access controls, team taxonomies, system-to-system mappings. Most of the work has been the same for twenty years. AI just raised the cost of skipping it.

The intelligence underneath the models

I keep coming back to the value of context data layers as a CTO in the middle of an AI rollout. I have started calling that value proposition contextual intelligence because I haven’t found a better name. Anthropic’s engineering team has been calling this kind of work “context engineering” since late 2025, and CIO ran its own feature on the term shortly after. Whether you describe it as contextual intelligence or context engineering, it’s the part of the stack where the actual programming work still lives.

If business logic is your company’s official org chart, then contextual intelligence is knowing who actually gets things done, how decisions are actually made and what the unwritten rules are. One is theory. The other is reality.

Most enterprise systems capture the theory. The systems that capture how work actually happens — what people do, how teams operate, where decisions get stuck — are rarer and harder to build. And modern LLMs, it turns out, are useless without both.

I learned this the hard way at a recent company hackathon. Nine engineering teams, one prompt: make our operational dataset more usable through AI. My team built persona-based chatbots (CFO, CIO, sales manager) on top of an MCP server backed by Postgres and our enrichment data. Other teams built dashboard generators, Looker conversational analytics and workflow agents.

The initial demos all had the same problem. Claude could talk to our data, but the answers were either generic or confidently wrong. The CFO persona would happily report a “spend trend” that quietly conflated two distinct cost categories across two different tables. The CIO persona would answer questions about team productivity, but the averages across roles should never have been aggregated. The sales manager persona returned answers that were technically correct against the schema and completely wrong against the business. The raw data was rich. The context layer around it didn’t exist yet. Chatting with raw data is not an AI product. It’s a demo.

One of my senior engineers spent the second day ripping out the agent’s direct database connection. He stopped trying to prompt-engineer the LLM to understand our business and instead codified that logic into the data pipeline. Working backward from the failed CFO answers, he mapped out the implicit knowledge an experienced controller relies on: Explicitly defining which legacy tables actually represent ‘spend,’ writing the rules for currency normalization and hardcoding our fiscal time windows. He built a series of semantic SQL views to enforce these rules and restricted the MCP server to exposing only this curated layer. When we pointed the same model at those same questions, it returned completely different answers. They were specific, evidence-based and grounded in our actual business reality. The model didn’t get smarter. The engineering beneath it did.

The same pattern shows up everywhere I look right now

McKinsey keeps publishing that software development tops enterprise AI use cases, with companies reporting 30–50% productivity gains in pilots. The pilot numbers are real. They rarely translate to top- or bottom-line impact in production. Our own company data tells the same story: Between Q1 2025 and Q1 2026, our total AI tool usage grew by 328% (over 4x). Over that same period, PR throughput grew by just 49%.

That gap — adoption way up, outcomes inching along — is the context gap. Plug a generic agent into raw, uninterpreted data, and it will act inefficiently at best, harmfully at worst. An agent optimizing sales without your customer segmentation or product hierarchy will confidently recommend the wrong thing. Anthropic framed the shift directly: building with language models is becoming “less about finding the right words and phrases for your prompts, and more about answering the broader question of what context configuration is most likely to generate our model’s desired behavior.” That second question — what context configuration  — is the entire game. Most organizations are still answering the first one.

Where the work actually lives

A growing number of CTOs I talk to are shifting their AI investments accordingly. Less attention on the model. More on the layer between the model and the data.

When peers ask me what that actually looks like day-to-day, I tell them I give every engineering role the same mandate: the LLM should never see raw, uncontextualized data.

In practice, that breaks down to three pieces of work, none of them glamorous.

The first is semantic middleware. We need code that transforms raw data into business-meaningful signals before it ever reaches the model. Our feature stores hold things like “employee code velocity on critical-path features,” not “X logged 50 Git commits.” The work of figuring out what “critical-path” means in our product, in our org, on this team is the work. It does not get cheaper because the model has gotten better.

The second is multi-agent design. Instead of one omniscient orchestrator, we run smaller agents scoped to specific domains, each with rules that catch the failure modes the main model is known for. We pair them with RAG that retrieves precomputed insights, with their rules attached, rather than raw documents. Validation checkpoints sit between steps and flag suggestions that violate known constraints, such as averaging productivity across completely different job functions. The guardrails are not there to be clever. They are there because we already watched the model make those exact mistakes.

The third is evaluation that takes business logic seriously. When I look at a model, general benchmark accuracy is the least interesting number. I want to know whether it respects our constraints and integrates cleanly with our existing architecture. That sometimes means fine-tuning our patterns, sometimes constitutional approaches to embed principles, sometimes hybrid systems where deterministic rules sit alongside the probabilistic ones. The throughline is the same: validate against reality, not against the benchmark.

Why this matters now

The reason this matters more now than it did six months ago is that adoption is moving faster than measurement, let alone integration. Model Evaluation & Threat Research’s (METR) developer productivity work tells the story in a way they didn’t intend. In early 2025, they ran a controlled study and found AI tools slowed experienced open-source developers by 19%. When they tried to repeat the study in late 2025, the experiment broke. Thirty to fifty percent of developers refused to submit tasks under the no-AI condition. They wouldn’t accept working without their tools. METR is now redesigning the study because the original methodology no longer holds up against how developers actually work. That’s how fast adoption moved. But I’d be willing to bet the organizational scaffolding required to convert that adoption into outcomes — context layers, workflow redesign, retraining around new tools — moved nowhere near as fast.

Get ahead with context

The teams I’ve seen succeed with AI built the context layer first. The teams I’ve seen struggle eventually built in context anyway, just at higher cost and with more scar tissue. Raw data is the new currency. But raw data without a context layer is cash sitting in a vault. It cannot act on anything. The difference between insight and noise is a layer of code that understands what your data means.

That layer is the work. It is where the next decade of competitive advantage will sit. And in my experience, the organizations that build it first are the ones that will actually get the productivity gains the rest of the market keeps promising.

This article is published as part of the Foundry Expert Contributor Network.
Want to join?

Tech job postings rose for the sixth straight month in June, as employers are increasingly on the hunt for qualified talent to support major technological initiatives, according to the most recent CompTIA Tech Jobs Report.

“June’s employment data suggests that employers are ramping up their technology investments and hiring the talent needed to support them,” said Seth Robinson, vice president for industry research at CompTIA, in a press release. “Even as some tech companies announce layoffs, employers in other industries are accelerating digital transformation initiatives and moving from AI experimentation to implementation.”

Data from Dice supports this growth, with its report showing AI/ML job title postings up 173% year over year from Q1 2025 to Q1 2026 — roles that typically offer median salaries 22% higher than the overall IT market. Hiring for jobs in data analysis (10%), cybersecurity (4%), and IT support (1%) have also seen increases. Tech postings in the finance and banking industry are up 47%, with manufacturing IT jobs up by 27% and IT roles in the insurance, aerospace, and defense sectors up by 23%, pointing to growth for IT across a range of sectors.

Software development jobs, however, are still on the decline from last year, down 22% from 2025, according to Dice, but it looks like that trend may soon shift. Data from CompTIA shows that software developer and software engineer job postings were the top roles for June (49k postings), followed by systems engineer (36k), tech support specialist (27k), data analyst (21k), and DevOps engineer (19k).

The catalyst for sparking a boost in software development hiring can be attributed to the release of Claude Code in late February 2025, according to data from the Indeed Hiring Lab. Since Claude Code’s release, Indeed has tracked a rise of 15% for software development job postings in the US, despite overall job postings declining by 7%. The report highlights how this rebound comes after a “low starting point,” so while the increases seem high, it’s because they had a long way to go.

Despite this increase in job postings, software development jobs still hover somewhere around 27.5% below their pre-pandemic levels, while overall job postings have remained relatively stable since 2020. It’s also important to note that 71% of the increase of software development job postings between May 2025 and May 2026 has been for senior-level roles, with 37% mentioning AI in the title.

This indicates that there is merit to the concerns around how AI automation is reshaping the IT leadership pipeline. IT leaders will need to remain mindful of how AI impacts hiring — and avoid leaving entry-level workers in the dust as hiring rebounds.

New research from Indeed Hiring Lab found that the number of U.S. job titles referencing AI has more than tripled since 2022, growing from 264 to 822 by the first quarter of 2026.

The research division of the global job search platform also discovered that nearly two-thirds (63%) of those AI-related job titles in the U.S. now appear in roles outside traditional technology occupations, in fields such as healthcare, education, marketing, logistics, and management.

“AI is no longer just a tech occupation story,” Indeed researchers wrote, noting that employers are increasingly incorporating AI skills into roles that historically had little connection to software development or data science. “The number of AI-touched jobs has risen across every market we track.”

Separately, according to CompTIA’s latest Tech Jobs Report, employers posted more than 280,000 new technology job postings in June, marking the sixth consecutive month of growth. Active technology job postings approached 600,000, while employment in tech occupations increased by 47,000 positions. The unemployment rate for tech occupations fell to 2.9%, compared with the national unemployment rate of 4.2%, according to CompTIA.

“June’s employment data suggests that employers are ramping up their technology investments and hiring the talent needed to support them,” said Seth Robinson, vice president for industry research at CompTIA, in a statement. “Even as some tech companies announce layoffs, employers in other industries are accelerating digital transformation initiatives and moving from AI experimentation to implementation.”

CompTIA’s report notes that new tech job postings increased for the sixth consecutive month amid what it described as a “broad-based movement toward reinvigorating digital transformation activities and clarifying AI strategies.”

Demand remains strongest for traditional IT roles. Software developers and engineers led all technology job postings in June with nearly 50,000 openings, followed by systems engineers, tech support specialists, data analysts, and DevOps engineers. Professional, scientific, and technical services firms generated the highest volume of technology job postings, followed by administrative services, manufacturing, information and media, and financial services organizations, according to CompTIA.

Employers are increasingly “writing AI into job titles for a wide range of roles—not just software and data jobs,” according to Indeed’s report. The reports show continued demand for technology workers while AI-related job titles become more common outside the traditional tech sector.

It is lap 47 of 57.

Before the race began, the team had already processed gigabytes of race data, simulations, tire models, weather forecasts, competitor tendencies and scenario plans. But on the pit wall, there is tension.

The race leader’s tires are degrading faster than predicted. A rival has just pitted for fresh tires and is closing the gap by three-tenths of a second per lap. The lead may not hold. In short, the race is not going to plan.

A strategist now has only seconds to synthesize live telemetry, competitor data, weather projections, tire inventory, track position and race simulations into one call that could determine the outcome.

They do not have those seconds because they are simply fast. They have them because the entire system behind the decision was designed that way: the data architecture, simulation models, communication protocols, decision rights, scenario playbooks and feedback loops all work together to compress complexity into a clear decision window.

What if this is not just a racing story? What if it is also a blueprint for how the best enterprises will operate in the AI era?

This builds on a broader shift I’ve described as the intent-driven future of work, where enterprise work begins less with navigating systems and more with expressing outcomes, context and intent.

The AI advantage will not belong to companies with the most tools. It will belong to companies that redesign how work senses, decides, acts and learns.

AI isn’t just a faster engine

The popular story about Formula 1 is usually about speed or the quality of the driver. The fastest car with the most powerful engine with the driver with the quickest reflexes will win. But anyone who follows the sport closely knows that raw speed is only the starting point.

Every car on the track is fast. Speed gets you into the race. It does not guarantee you a win.

The teams that win consistently do so because of the quality of the system surrounding the car. They connect telemetry, simulations, strategy, engineering, pit operations, driver judgment and real-time learning into one high-performance operating model.

Every part of that operating model matters. But the best individual part alone does not win the race.

Enterprise AI strategy is at risk of making the same mistake that would keep an F1 team stuck in the middle of the pack: investing heavily in the engine while underinvesting in the entire race system.

I see enterprising investing in more copilots, more agents, more dashboards, more tools and ultimately more automation. 

The AI systems perform their tasks at unprecedented speed. But the business outcomes do not change. In many ways, AI is becoming a new operating system of work not because it replaces every application, but because it changes how intent, context, workflow and execution come together.

That is the gap many organizations are now facing. They have access to powerful AI capabilities, but they have not yet redesigned the operating model around those capabilities. The result is faster individual task execution inside disconnected systems, fragmented workflows and unclear accountability. In fact, a recent McKinsey report found that 88% use AI but two-thirds haven’t scaled it.

The next phase of AI value will not come from simply adding more AI tools. It will come from redesigning how the enterprise senses, decides, acts and learns.

The enterprise has too many disconnected signals

Most enterprises do not suffer from a lack of signals. In fact, they are everywhere across the business.

Customer intent signals, campaign performance data, product usage patterns, sales activity, support interactions, contract information, financial indicators, employee sentiment, security events and operational metrics already exist throughout an organization.

The problem is signal fragmentation.

The average knowledge worker has become the integration layer of the enterprise. They move between CRM, marketing automation, analytics dashboards, spreadsheets, collaboration tools, support systems, workflow platforms and financial reports. Then they manually assemble context that no single system provides.

They do this to answer questions that should take seconds, not hours.

• Which customer needs attention?
• Which opportunity is at risk?
• Which process is slowing down execution?
• Which signal should trigger action?
• Which decision needs human judgment?

In Formula 1 terms, this would be like a pit crew strategist having to call five different team members to gather tire degradation data, track conditions, competitor lap times, fuel load, weather forecasts and pit stop windows before making a race-defining call.

The data exists. But the latency in accessing, interpreting and acting on it makes it less valuable at the moment of decision.

That is the signal-to-action gap. And closing that gap is one of the most important opportunities in enterprise AI.

The new operating model: Sense, decide, act, learn

The AI-native enterprise needs to operate more like a Formula 1 team: continuously sensing, deciding, acting and learning.

• Sense is the foundation. It means connecting the right signals across systems, workflows, customers, employees and operations into a layer that AI can reason across. This is not just reporting on the past. It is creating the ability to understand what is happening now and anticipate what is likely to happen next.
• Decide is where AI intelligence and human judgment come together. AI can surface context, detect patterns, model options and recommend actions. Humans bring business judgment, ethical reasoning, organizational context and accountability. The quality of this partnership depends on the quality of the signals and context available to both.
• Act is where intelligence turns into execution. The goal is not another recommendation sitting in a dashboard. The goal is a workflow that triggers the right action, with the right controls, at the right time.
• Learn is where the operating model becomes a competitive advantage. Every action should generate feedback. Every outcome should improve the next recommendation. Every workflow should become smarter over time.

In Formula 1, every lap creates learning. Tire wear, track temperature, driver feedback, competitor movement and weather changes continuously reshape strategy.

The enterprise needs the same kind of learning loop.

Semantic intelligence is the missing layer

To close the signal-to-action gap, enterprises need more than data integration. They need semantic intelligence.

Semantic intelligence is what helps AI understand enterprise meaning. It connects business language, customer context, workflow relationships, policies, roles, systems and outcomes so AI can reason across the business, not just retrieve information from systems.

A customer health score is not just a number. Its meaning depends on product usage, renewal timing, support history, stakeholder engagement, commercial value, sentiment, implementation milestones and prior interventions.

A delayed workflow is not just a status update. It may signal unclear ownership, missing approvals, poor handoffs, missing context, poor data quality or a decision that needs escalation.

A sales opportunity at risk is not just a CRM field. It may reflect adoption gaps, customer sentiment, usage decline, executive sponsor changes, pricing friction, support issues or service delivery risk.

Without semantic intelligence, AI can summarize what happened. With semantic intelligence, AI can understand what matters, why it matters, who needs to act and what action is most likely to improve the outcome.

This is where enterprise AI value compounds. Foundation models will become broadly available. The model itself will not be the moat. The moat will be enterprise context, semantic intelligence, workflow intelligence, governance and learning loops.

Redesign work before automating it

There is a warning in the Formula 1 analogy that deserves attention: adding more power to a poorly designed system does not make it high performing.

The same is true for enterprise AI. Adding AI to a broken workflow does not fix the workflow. It just compounds the dysfunction.

If the data is fragmented, AI will produce incomplete recommendations confidently. If governance is disconnected from execution, AI can scale risk as quickly as it scales productivity.

The question teams ask shouldn’t be, “Where can we insert AI into this existing process?”

The better question is, “If we were designing this work from scratch, knowing what AI now makes possible, how should it operate?”

This pushes leaders to clarify where work starts, what signals matter, which decisions should be automated, where human judgment is required, what controls must be embedded, how outcomes should be measured and how the system should learn.

This is where CIOs, CTOs and technology leaders have an expanded role. AI transformation is no longer only about deploying technology. It is about redesigning how the enterprise works.

Context becomes the differentiator

In a world where every enterprise can access powerful models, context becomes the differentiator.

The winning organizations will not be the ones with the most AI tools. They will be the ones with the strongest enterprise context and the clearest path from signal to action.

That context includes customer history, product usage, workflow patterns, decision history, business rules, governance standards, risk boundaries, organizational knowledge and outcome feedback.

It also includes knowing what happened after a decision was made. Did the action improve retention? Did it accelerate a deal? Did it reduce cycle time? Did it improve customer experience? Did it create risk? Did it scale?

Without that feedback, AI remains a recommendation layer. With it, AI becomes part of a learning operating model.

This is why the most important AI investments are not always the most visible ones. Data quality, identity, access, governance, workflow integration, observability, semantic models, feedback loops and change management may not sound as exciting as the latest AI agent. But they are what allow AI to create durable enterprise value.

The CIO as architect of the race system

The CIO’s role is evolving from technology operator to architect of the enterprise race system.

That means connecting strategy, workflows, data, platforms, governance, security, talent and execution into an operating model that can move faster without losing control. The CIO’s job is no longer just to provide platforms. It is to design the conditions where intelligence can move safely and effectively through the enterprise with the right context, controls, accountability and feedback loops.

Business teams need the ability to experiment and innovate. But they need to do so within clear standards for data access, identity, security, privacy, model usage, auditability, human oversight and business accountability.

This is the balance every enterprise needs to strike: speed with control.

The future is federated innovation with centralized guardrails. It is an enterprise operating model where more people can create value with AI, but within a trusted architecture that protects the company, the customer and the quality of decisions.

The companies that pull ahead in the next decade will not be the ones that deployed AI first or assembled the largest portfolio of tools.

They will be the ones who built the enterprise equivalent of a winning Formula 1 race system: a connected operating model.

In Formula 1, the gap between the team that wins the championship and the team that finishes fourth is often measured in tenths of a second per lap. Compounded over a race distance, those tenths become decisive.

The same dynamic is emerging in enterprise AI.

This article is published as part of the Foundry Expert Contributor Network.
Want to join?

Historically, humans have solved their toughest tasks by creating tools capable of withstanding greater strain to undertake the job or augment their abilities. From levers to steam engines and beyond, the structural evolution of machines is almost as remarkable as their ability to improve operational cultures.

In recent times, we have seen machines attain their highest structural complexity, productivity, and best aesthetics yet. The most relevant new technologies today focus on creating high-throughput physical machines and software that ‘thinks’, and, more futuristically, a fusion of both.

From moving machines to intelligent humanoids

Evolving from ‘moving machines’ capable of handling repetitive tasks to intelligent machines is a century-long goal for robotics. The rapid growth in this sector over the past half-decade, with a $218 billion projection for 2031, is driven by expectations that advancements in AI will extend to robotics and expedite the development of intelligent robots.

Current prototypes are robots capable of taking initiatives or executing tasks more efficiently with less supervision. These have been applied in agriculture, industrial-grade production, and healthcare.

Humanoid robots have attracted the most attention due to the excitement around their potential as near-human machines and the signals their development sends for the future of human-machine coexistence.

Tech leaders around the world are contributing to advancing physical AI with optimism about the impact of robotics on humanity.

Physical AI is a department of artificial intelligence specializing in developing AI algorithms and models for locomotive systems. This includes every kind of robot and, at a more advanced level, humans.

Recent developments in this field include Figure AI’s humanoid robot deployments and Tether’s investment in the NEURA, leading the fundraising of up to $1.4 billion in one of the largest robotics and physical AI investment rounds on record.

Physical AI researchers are exploring future-proof strategies to develop intelligent, safe humanoid robots that can collaborate with humans, undertake humanly impossible tasks, and handle routine tasks more efficiently.

The strongest case for AI-powered humanoid robots is that they complement human power. A World Economic Forum (WEF) report projects shifts in work roles. Humanoid robots increase the workforce, take over repetitive, strenuous, and boring roles, allowing humans to pursue more interesting career paths.

This way, superintelligent humanoid robots will lead sector-wide transformations beyond current imagination and uniquely transform the world.

In theory, it creates the ideal conditions for an improved global economy and a higher quality of life. This theory is challenged by the dystopian vision of a machine-dominated world in which humans become less relevant. However, history suggests otherwise. Every major wave of automation, from the industrial revolution to the rise of computers, initially sparked fears of human redundancy. Yet each ultimately created more opportunities than it eliminated.

Super-human advancements with physical AI

In ideal operations, physical AI will serve as a lever for humans as well. While progress in robotics is loudest, efforts to directly augment human abilities with physical AI are also yielding remarkable results. Brain-computer interfaces can now accurately decode speech in paralyzed and speech-impaired individuals through intracortical implants that detect brain activity. And this is only a ‘start’. Projections from leaders in this space give insight into the trajectory of this technology.

In a recent fireside chat with NEURA Robotics CEO and founder David Reger, Tether CEO Paolo Ardoino noted, “the evolution of robotics that Neura is making is going to allow testing and building of a framework[…] where the real impact is in the real world. Everything starts digital, but to see the true potential, we will see robots roaming the streets, helping people, and being part of society. It has to happen safely, it has to be transparent.”

Physical AI products designed for direct human integration are being developed differently, with a focus on ergonomics, a minimalist aesthetic, and performance. EVO, Tether’s arm leading the charge for human advancement through intelligent technologies, also shared plans for non-invasive implants that maintain high productivity and offer greater composability.

Technologies like these will allow humans to leverage high-level physical AI technologies to attain the same technical abilities as humanoid robots and outperform them by combining machine and raw human intelligence.

AI robotics in non-user-controlled infrastructures

Resource efficiency, data sovereignty, and surveillance are some of the biggest ethical considerations of Physical AI after safety and responsibility. The infrastructure line-up for Software and Physical AI relies heavily on managed systems, blurring the lines of control and governance.

Who is really in charge? The end user, developer, or proprietors of the centralized infrastructure that powers the product? The result is a product with multiple points of failure, disruptions, and most importantly, operational risks.

Physical AI solutions will be used by billions of people worldwide; they should therefore not be built on limited, slow, and centralized infrastructures. This necessitates localized or truly decentralized AI solutions. Local-first AI solutions like Tether’s QVAC also prioritize resource efficiency, since users are expected to provide the core infrastructure. QVAC is a modular, highly efficient, local-first AI platform that runs anywhere. Tether regards it as the invisible intelligence engine of the 21st century.

Open-sourcing and aligning intelligent robots for co-existence with humans

Yann LeCun, Chief AI scientist at Meta, notes that open-sourcing AI development is the answer to the most pressing ethical challenges of AI applications. According to LeCun:

“The magic of open research is that you accelerate progress by involving more people[…] the biggest danger of AI isn’t ‘bad behavior’ […] It’s that every digital interaction in our future will be mediated by AI. In that world, diverse open-source systems let users choose their own biases.”

Open-sourced (systemic decentralization) and local-first (Infrastructural decentralization) solutions are the only path to developing ethically aligned Physical AI capable of co-existing with humans as intended. A successful physical AI solution is expected to tick the check boxes of safety, resource efficiency, true user control, and tamper-proofness. To do this, it must embrace transparent development procedures and function without gatekeepers.

Learn how Tether EVO is building resilient technology built on fairness, inclusivity, and systems with zero points of failure.

Most CIOs and other IT leaders are confident in their teams’ ability to meet the coming AI challenges, but many believe business operating models and processes need an overhaul.

More than 80% of senior IT executives surveyed for the 2026 Global Leadership Technology Study from Deloitte are confident in their organizations’ ability to deploy and govern AI capabilities at scale. However, 75% think their operating models and processes must change in the next 12 to 18 months to drive greater value.

The survey results don’t signal an overconfidence from IT leaders in their teams’ ability to roll out AI, and they don’t seem to suggest a major AI choke point within the IT organization, says Anjali Shaikh, MD of Deloitte Consulting and leader of the Deloitte global CIO and US tech executive programs.

They do, however, point to a need for major changes to operating models and processes across the organization, not just within IT, to achieve better AI results, she says. And while CIOs can’t control how business workflows are designed, they have a role to play in pushing for processes that better make use of AI tools, she adds.

“What we’re hearing now is that AI isn’t just exposing those technology limitations, but enterprise design limitations,” Shaikh says. “What’s new is that technology is no longer the bottleneck, it’s the operating model and the ways of working.”

Business workflows that need to change are the manual processes that aren’t friendly to AI integration, like using spreadsheets for some accounting, or manual data entry.

CIO as advocate

CIOs can use their expertise to champion the redesign of enterprise workflows so they better align with AI tools like agents, Shaikh adds, creating opportunities for CIOs in a changing landscape.

 “The AI era is starting to demand a new type of leader,” Shaikh says. “They’re going to have to guide the organization through this change, build AI-related teams, and figure out the skill set required for not only their team, but the overall business.”

Another conclusion from the Deloitte report is that CIOs and other IT leaders are experiencing a make-or-break moment as they face major new expectations in their roles, including the ability to lead change and build AI-ready teams.

Leadership is important, Shaikh says, in that the organizations with the most successful AI deployments tend to be those with C-suite support for operating model and process change.

“It’s an investment and a commitment from the top down —  the board, CEO, and C-suite have made a commitment to saying we’re going to invest not only time, but budget, resources, and talent into helping us fundamentally shift the way we work,” she says. “When you get that alignment and commitment from the top, that sends a signal to everyone in the organization.”

Several IT leaders agree that several operating model and process changes are needed at many organizations.

Focus on the right thing

Many organizations are embracing AI by buying ChatGPT licenses and hosting lunch-and-learn sessions on prompt engineering, but they’re solving the wrong problem, says Peter-Paul Schreuder, chief cloud officer and VP of support at enterprise asset management software vendor Ultimo.

“Teaching people to use ChatGPT takes about an hour,” he says. “Teaching an organization to fundamentally rethink how work gets done takes four to five months of hard, unglamorous process work, but delivers exponentially more value.”

A major problem is that most organizations don’t understand their own workflows well enough to determine where AI could add value, he adds.

“They don’t know where the information gets stuck,” Schreuder says. “Which tasks consume disproportionate time? Where do we repeatedly reinvent the wheel because knowledge lives in someone’s head? Until you can answer those questions with specificity, no amount of AI training will matter.”

Getting the most out of AI takes a huge reset in the way businesses think how they complete their work, he adds, and if AI initiatives lack deep involvement from business operations leaders, they’re doomed to fail.

“If you can’t diagram how your work flows from intake to completion, you’re not ready for AI augmentation,” Schreuder adds. “If your list includes ‘summarize documents’ or ‘draft emails,’ you’re thinking about AI as a fancy word processor. Transformative applications are process specific.”

So some companies are clear about how they link new AI tools with workflow redesign. When financial services firm IMA Financial Group deploys a new AI capability, the company doesn’t treat it like a technology rollout, but as a transformation for how work gets done, says Megan Cullen-Meyer, its VP of data and AI. The company examines what tasks need to change, what roles need to be realigned, and what training and reskilling is needed, for instance.

With AI, IMA is automating routine, transactional work that used to burn up a lot of human time and energy.

“We talk a lot about how applying AI to old, outdated processes isn’t how we’re going to get the value we want from AI,” she says. “We’re taking a hard look at our core tasks and workflows to determine where we automate the work, where we augment work, and where we keep humans at the center.”

Setting the right foundation

The Deloitte report suggests that the IT team can’t build AI tools in a vacuum, says Luis Mesas, VP of product engineering at IT services provider Sngular. The value of AI comes when the organization develops a good governance framework to start with, can keep learning from what’s happening in its workflows, and can adjust the system without turning every improvement into a new initiative, he adds.

“This requires IT to work much closer with the business problem from the beginning,” he says. “Engineering teams can’t build AI systems in isolation and expect adoption to follow later, because they need to understand the process, the quality of the data behind it, and the risk of getting the answer wrong before they can design something that’ll hold up in day-to-day use.”

Until recently, many tech professionals viewed themselves as a special and respected worker class: highly educated, hard-working, well paid, and in demand.

“They considered themselves above unions,” says Zak Thompson, senior software engineer at Kickstarter and union steward at Kickstarter United.

Big Tech issued aspirational mission statements that motivated workers, workplaces were seen as meritocracies, and employees were encouraged to speak out if they were unhappy. If workers didn’t like where they worked, they just moved on: other employers would be falling over themselves to hire them.

How times have changed.

Now, fed up with mass layoffs, disillusioned with Big Tech’s direction, and stunned by bold management proclamations that AI will displace huge numbers of people in many tech jobs — starting with programmers — interest in unions has risen sharply among tech professionals. Workers in some organizations, including Kickstarter, have already taken the plunge.

A surge in interest

“Starting in 2022, the industry as a whole started seeing very large layoffs across the board [and] that has dramatically shifted the balance of power. I think most people in the industry have experienced that one way or another,” says Google software engineer Alan McAvinney.

But not everyone is convinced that the layoffs have changed the power dynamic. “I wouldn’t say the balance has definitively shifted… some things point to workers losing ground and others point to improvement,” says Liya Palagashvili, senior research fellow and director of the Labor Policy Project at the Mercatus Center at George Mason University.

What matters, she says, is not the size of the layoffs but worker options: how easily laid off workers can find alternative work in their field.

For McAvinney, the decision to support a union was about a culture change at his employer.

“In 2019, Google fired four people (the ‘Thanksgiving Four’) after they organized and spoke out internally against the company’s work with the anti-union firm IRI and US Customs and Border Protection. That was a big turning point for me,” he says. “Historically, we had a pretty robust culture that actually encouraged us to speak up internally.”

Google said the employees were fired for violating data security policies, but many workers believed the move was retaliatory. It became a galvanizing event that contributed to the launch of the Alphabet Workers Union (AWU) in 2021, says McAvinney, organizing chair, Alphabet Workers Union-CWA. (Alphabet is the parent company of Google.)

So far, tech worker interest in unions hasn’t translated into higher membership numbers nationally. According to the US Census Bureau’s Current Population Survey (CPS), union membership in tech occupations was about 3.5% in 2025, says Palagashvili. “While there have been some high-profile organizing efforts, they do not yet show up as a broad national increase in tech-sector unionization.”

Overall, only 10% of American workers belonged to a union in 2025, the Bureau of Labor Statistics (BLS) reported — near an all-time low — but interest in labor unions is rising. A 2025 Gallup survey found that 68% of Americans approved of unions, up from 48% in 2009. Interest is particularly strong among younger workers, the Economic Policy Institute reports, and in a 2024 online survey of 1,900 tech professionals on the career site Blind, 67% of respondents said they’d be “very likely” or “somewhat likely” to join a union if their company had one.

Nonetheless, for most tech professionals, those positive perceptions have not so far translated into widespread union membership.

Fear, uncertainty, and doubt

In the wake of mass layoffs that began in 2022, the primary driver toward tech worker unionization may well be job security.

“I think a greater concern is that their work and skills have been devalued at the same time their jobs become less secure and their wages and benefits have declined,” says Kate Bronfenbrenner, director of labor education research and senior lecturer emeritus at Cornell University’s School of Industrial and Labor Relations.

The fear that AI will displace IT workers en masse is palpable, says Google’s McAvinney. Whether the mass layoffs to date were actually driven by AI or if AI was used as a pretext, “Large numbers of people have that concern, and that is absolutely part of the interest in collective action — getting organized, joining a union, or forming a union,” he says.

The second motivator is ideological disillusionment. “Workers recruited with promises that they would be changing the world discovered that they were really building surveillance systems or military technology,” Bronfenbrenner says.

The insidious use of AI surveillance is another concern, says Bronfenbrenner. For example, Meta’s announcement that it would use AI to track US-based workers’ computer activities, including clicks, keystrokes, mouse movements, and screen snapshots to train AI agents had a dystopian feel to it. Were these workers training AI to take over their jobs, just as US workers were asked to train their lower-cost foreign replacements during the offshoring craze in the mid-2000s? (Meta later paused the tracking program after employees twice demonstrated the inadequacy of privacy protections for the collected data.)

But Bronfenbrenner argues that AI’s bigger threat may be its use as a surveillance tool to prevent organizing. “My research on surveillance in organizing campaigns found that it tripled from 11% in the early 2000s to one third in 2021,” she says.

“The deeper pattern is the same one inherent in Taylorism — management trying to know everything that’s under the worker’s cap, to monitor every step so workers have no control and no secrets,” Bronfenbrenner says. “Now they have even more technology to do it, and they can potentially replace you entirely with AI.”

Simone Robutti, an organizer with Tech Workers Coalition Global, calls the current wave of tech layoffs “a prequel to whatever AI-driven layoffs are coming.” It’s part of the trend of “lowering the cost of knowledge workers, of cognitive workers, of office workers in general — because that’s the bet on AI,” he says.

Whether employers can in fact replace workers with AI (or will be able to soon) is an open question. “If Amazon lays off a hundred workers, and ninety of them find comparable jobs within a few months, that mitigates the concern,” says Palagashvili from George Mason University. “If most of them have to sell their houses and move across the country, or end up underemployed — not just unemployed, but working in warehouses instead of at a competitor — that’s a different picture.”

So far that hasn’t been a big issue for former Google employees, says McAvinney. “It used to be that if you left Google, you could get a job anywhere in tech instantly. That’s no longer the case, but most people I talk to are still finding work in the industry,” he says.

But for those newly entering the workforce, it’s much harder to find a job. According to the Stanford HAI 2026 AI Index Report, released in April, “employment for software developers ages 22 to 25 has fallen nearly 20% from 2024.”

Successes and setbacks

While organizing can be an uphill battle and workers often face aggressive pushback from their employers, there have been a few notable successes.

In the UK, workers can join a union as individual members before their employer formally recognizes that union for collective bargaining purposes. That’s how 300 workers in Google DeepMind’s London office initially joined the Communication Workers Union. In April, 98% of the 300 CWU members voted in favor of pursuing union recognition, formally requesting that management recognize the CWU and Unite the Union as representatives for approximately 1,000 staff. (Google DeepMind disputed characterizing the action as a vote to unionize).

And in May, some 2,100 tech workers at the University of California joined the University Professional and Technical Employees union, which is affiliated with Communications Workers of America (UPTE-CWA), with 96% of the workers voting yes.

“A lot of tech workers right now are extremely concerned about job security and about their work being automated,” says Max Belasco, a business systems analyst at the UCLA School of Law and co-chair of the UCLA chapter of UPTE-CWA, Local 9119.

The CWA described the organizing initiative as “the largest tech industry organizing campaign in US history.” But it wasn’t the first.

“Between 2022 and 2024, CWA organized nearly 400 different digital media companies,” Bronfenbrenner says, including the game developer Activision and the New York Times.

Kickstarter’s 85 employees voted in 2020 to form Kickstarter United — the vote was 55% in favor — and most recently the union negotiated a contract that includes a four-day workweek, AI protections, and a minimum pay floor for 59 employees, including tech workers.

“What we ended up winning was yearly benchmarking of all employee salaries to the 60th percentile, along with yearly cost of living adjustments,” says Thompson.

But the way forward has been rocky. Shortly after the union was ratified, Kickstarter announced layoffs. The union, which is affiliated with the Office and Professional Employees International Union (OPEIU), wasn’t able to reverse that decision but did negotiate better severance terms, including four months of severance pay (versus 2 to 3 weeks for every year worked) and six months of health benefits.

When contract negotiations faltered in October 2025, the union went on strike for 42 days. By December, a new contract was ratified. Shortly thereafter, the company announced another round of layoffs that included four union leaders, one of whom had helped to negotiate the new contract. The union is currently fighting those dismissals and will be arguing its case in third-party arbitration.

The National Labor Relations Act of 1935 codified American workers’ rights to unionize and take collective action, and it established the National Labor Relations Board to protect those rights. Unfortunately for Kickstarter, NLRA enforcement under the Trump administration isn’t what it once was.

“Cases brought up for violations of the NLRA can go for months or years without ever seeing a hearing or having any sort of judgment. That gives companies more power to flagrantly ignore it,” Thompson says.

Tech firms have other weapons to dissuade employees from unionizing. Researchers from Carnegie Mellon University and Princeton University in 2025 interviewed 44 US-based tech worker-organizers, who cited additional pressure tactics including threats to withdraw venture capital funding — essentially killing venture-backed firms if employees vote to unionize — and threats of being fired that put tech workers with H-1B visas in an impossible position.

Kickstarter United is a majority union — one that has won NLRB certification. While that’s possible in smaller organizations, success in larger tech firms has been much more limited.

Alphabet is a prime example: the Alphabet Workers Union-CWA is a “pre-majority” union that lacks NLRB certification and has no formally recognized bargaining unit. Formed in 2021 with fewer than 400 members, today it represents 1,400 members, still a small fraction of Alphabet’s US-based workforce, estimated at over 100,000.

The challenge, says McAvinney, lies in trying to organize a distributed workforce of in-office, remote, and contract workers. “Large tech companies don’t split easily into discrete segments — there’s no strong geographic component to teams, and a single team is often spread across many locations,” which makes getting majority support unrealistic, he says.

But that doesn’t mean the union has had no impact. “In January, we launched our Googlers for Job Security campaign. Today we’re organizing around four demands: a guaranteed minimum severance package for everyone who’s laid off, voluntary buyouts before any mandatory layoffs, an end to GRAD quotas (GRAD being Google’s performance review system) so ratings reflect actual performance and aren’t given or changed to force a particular distribution, and the option to take severance as leave, giving workers, especially those on visas, more time on payroll,” McAvinney says.

“In response, Google did start offering voluntary exit packages,” he says. The union was also able to negotiate one contract, for Google Help workers. However, those workers aren’t actually Google employees: they’re contractors who report to Google management but work for Accenture.

The counterargument

Do unions get what they bargain for? Conservative business and labor economists say union contracts typically have rigid pay structures that restrict merit-based pay in favor of seniority-based wage increases, and that unions, as certified by the NLRB, create labor monopolies that limit worker choice and push up wages to levels detrimental to both workers and business.

The collective bargaining model is not well suited to the highly dynamic and innovation-driven tech sector, Palagashvili argues. “Firms often need to reorganize teams, redesign products, adjust roles, and redeploy talent quickly,” she says.

Collective bargaining agreements make those adjustments much more difficult by imposing uniform terms for an entire bargaining unit, regardless of individual preferences and circumstances. The contracts, she says, “are more about higher pay and less about flexibility.”

But Thompson says that hasn’t been his experience. “The thing with a union is you get to write the contract,” he says. “At Kickstarter we care about recognizing individual contributions, merit, and having a clear career progression.”

Kickstarter United pushed the company to clearly define what it takes to get a promotion, advocated for no “at will” employment, where employees can be fired any time without a stated reason; won standards for minimum pay, raises, promotions, and time off; secured AI protections; and codified a four-day work week.

Yes, some tech professionals voiced concerns about unions, such as that they stifle innovation and limit compensation for top performers, Thompson says, but “a lot of those people came around. They said ‘I was wrong. I feel way more protected, more secure, and I see the benefits.’”

Bronfenbrenner says it’s a mistake to think that tech workers are inherently different from other workers, adding that the two industries with the highest union density are entertainment and professional sports. “These are professionals with unique talents and capabilities, and they’ve organized successfully under the exclusive representation system.”

Will we see a unionized tech workforce?

If unions eventually prevail in tech, it will happen in the face of intense pressure from employers not to organize.

“The Alphabet Workers Union is a case study of the limits of the first wave of tech labor organizing,” says Robutti from the Tech Workers Coalition. “They hit a threshold beyond which they couldn’t fight the union busting anymore, and they became entrenched at that size.”

No one should expect large-scale unionization to occur overnight, Bronfenbrenner says. “The auto and steel industries weren’t organized in months. It took decades. Organizing global tech companies will take the same.”

While McAvinney acknowledges that a traditional majority union may be difficult to achieve any time soon in a company as large as Alphabet, he’s still bullish on his pre-majority union’s ability to make a difference. “Ultimately, regardless of which type of union you are, you can only win as much as you have leverage to win. Your leverage is inherently limited, but that doesn’t mean you can’t win anything,” he says.

Attitudes about unions appear to be changing rapidly. “Interest in unions is high, and I expect that will continue,” McAvinney says. “Now is an excellent time for people to start getting organized. I have seen lots of evidence of that.”

Thompson agrees. “We are seeing an uptick of people in tech reaching out, trying to get help organizing. When people have their job conditions continue to deteriorate, they are going to start organizing,” he says. “We’re definitely seeing a shift from ‘it’d be nice if we had a union’ to ‘okay, how can I actually do this now?’”

Come back next week for Part 2: How to unionize your tech workplace

The number of tech layoffs continues to tick upwards as AI investments increase, with Microsoft alone cutting around 4,800 employees, or roughly 2.1% of its workforce, this week.

The latest cutbacks are mostly in the company’s commercial sales and Xbox divisions. They follow two others in 2025 that impacted around 15,000 workers, or roughly 4% of the company’s workforce. Prior to the latest cuts, Microsoft had 220,000-plus employees.

The headcount reduction also comes just days after the announcement of Microsoft Frontier Company, an initiative that will provide embedded support for customers deploying AI projects, similar to traditional offerings from systems integrators (SIs).

Taken together, these moves seem to indicate that Microsoft is betting on its engineering expertise, rather than traditional account management, as the path to AI success.

“Microsoft had already reorganized its commercial business around AI,” said Thomas Randall, a research director at Info-Tech Research Group. “Recent layoffs are part of that ongoing context.”

Microsoft’s memo to employees

In a memo obtained by Business Insider, Microsoft EVP and chief people officer Amy Coleman said the cuts effectively reflect the tectonic shift being brought about by AI.

“The ‘why’ is this: Our business is changing because the world around it is changing,” she said. “Companies don’t get to choose whether their industry changes; they only get to choose whether they change with it.”

Customer needs, and the business models that serve them, are shifting, meaning vendors must “adjust resources and roles” so they can operate in a way that best serves their customers. However, Coleman emphasized: “Whenever possible, our priority is to place people into new roles aligned to the company’s highest priorities and greatest areas of opportunity.”

Which, today, is AI.

Seemingly contradictorily, Coleman said the cuts “build on” the Frontier Company announcement, which is “reshaping how we work and embedding our engineering experts alongside customers so we can help them accelerate their technology deployments.”

While she emphasized that the roles eliminated this week are not being replaced by AI, the technology is fundamentally changing work. Many everyday tasks are being automated, meaning “we all need to keep learning, keep building new skills, and keep adapting as the work evolves.”

Customers are undergoing the same shift and are looking to Microsoft for guidance, she noted. “We can’t do that well unless we’re doing it ourselves.”

Finally, she said the tech giant will evolve structure and priorities across the company “thoughtfully.”

“We are working on alternative solutions to job eliminations and … we will continue to invest in equipping employees with new skills, including in AI.”

What customers might expect

Redmond isn’t the only big tech company taking scalpels to staff as the industry adjusts to, and seeks to capitalize on, AI. Companies are spending billions and inking strategic partnerships with top AI labs, and these investments in some cases need to be offset with cuts because some have yet to provide tangible ROI.

For instance, Amazon has laid off 30,000 workers since last fall, while Google is rumored to be cutting employees in its cloud division. Meta, for its part, eliminated 8,000 employees, or about 10% of its total headcount, in May alone, while Oracle recently slashed 21,000.

For customers, there is a price to pay, however. In the case of Microsoft, Info-Tech’s Randall said that, with the cuts, some customers can now expect slower response times on “non-strategic asks,” particularly as accounts are consolidated under fewer reps.

That said, given its Frontier Company investments, top accounts with large AI, data, security, and cloud commitments may get “deeper technical engagement,” while ordinary licensing/support workflows may become leaner.

Further, customers can expect more hand-offs to partner-led engagements, given that Microsoft is already pushing customers toward its partners for FY27 (which began July 1, 2026) when it comes to AI, security, cloud modernization, Copilot, agents, and managed services, Randall said. The company is also offering partners higher margins for growth in certain AI workloads.

“To prepare for these shifts, customers should reflect and document their Microsoft account and support teams,” Randall advised.

By that he meant enumerating things like the support contacts, the partner contacts, and the escalation paths for issues. This information should be well-documented and shared internally, he said. The same goes for Microsoft-involved conversations having to do with any kind of commitment, such as those involve pricing assumptions, roadmap dependencies, or deployment milestones.

“This can ensure a smoother transition with a new account rep on what has already been set out for the organization,” Randall said.

Closing the gap between AI investment and ROI

Last week, Microsoft launched the $2.5 billion Frontier Company, which it said “goes beyond” SIs and Forward Deployed Engineers (FDE). The initiative will integrate thousands of the company’s own engineers directly into customer environments to help them build AI tools, and to also help customers learn essential skills so they can eventually handle projects on their own.

But customers shouldn’t think of this as what he described as “consulting-heavy, McKinsey-style engagements,” noted Info-Tech’s Randall. Pre-sales will likely become more focused on qualifying an organization’s specific processes for ongoing AI implementations, rather than on system-wide change management. As with other hyperscalers such as AWS, Microsoft is leaning into this more white-glove model to help “prevent the gap between AI investments and AI ROI from widening.”

“As such, Microsoft will likely reserve its best technical talent for accounts with strong production intent, credible budget, usable data, and clear executive sponsorship,” Randall predicted.

This article originally appeared on CIO.com.

With change a constant, cybersecurity professionals looking to improve their careers can benefit from the latest insights into employers’ needs. Data from Foote Partners on the skills and certification most in demand today may provide helpful signposts.

Analyzing more than 660 certifications as part of its 2Q 2026 “IT Skills Demand and Pay Trends Report,” Foote Partners calculated the most valuable IT security certifications to pursue right now based on two dimensions. The first, the average pay premium, measures the difference in pay between IT pros with a particular credential and those without it. The second, market value increase, measures the increase in pay gains over the past six months.

Together, average pay premium and market value increase can give cybersecurity pros a starting point in deciding which certification to pursue for more pay. Apart from considering their overall professional goals, security professionals should consider each certification’s training and exam costs, whether vendor-specific or vendor-neutral, and the lateral or vertical role opportunities it may open.

Here are the top 13 certifications paying higher premiums today in descending order.

GIAC Security Expert (GSE)

The GIAC Security Expert (GSE) portfolio certification is for security leaders wishing to prove their status as a top information security practitioner by showing they have offensive and defensive skills and hands-on practical skills. Available for more than 15 years, the GSE is considered one of the broadest and deepest cybersecurity certifications. To earn the certification, candidates must complete any six practitioner certifications and any four applied knowledge certifications.

GIAC allows candidates to customize the certification to fit their expertise and career. Candidates can also build their certification over any amount of time as along as the required certifications within the portfolio remain active. Practitioner certification exams are 2-5 hours in length, depending on the specific certification attempt, and applied knowledge certification exams are 4 hours in length.

Training fees: Some training is offered in affiliation with SANS Institute and costs $8,780.

Exam Fees: Because you need 10 certifications to achieve the GSE prices vary significantly. If you already hold a GIAC Certified Forensic Analyst (GCFA), the cost of one of the required certifications drops from $1,299 to $499. Most required certifications are priced at either $999 or $1,299 per attempt, though they can cost up to $11,190.

GIAC Security Professional (GSP)

The GIAC Security Professional (GSP) is designed to demonstrate the holder’s depth and breadth of information security knowledge. Launched approximately two years, this newer certification is the halfway point to the GSE. Customization of the certification is allowed, and to achieve it a candidate must complete any three practitioner certifications and any two applied knowledge certifications. Candidates can also build their certification over any amount of time as along as the required certifications within the portfolio remain active. Practitioner Certification exams are 2-5 hours in length, depending on the specific certification attempt, and Applied Knowledge Certification exams are 4 hours in length.

Training fees: Some training is offered in affiliation with SANS Institute and costs $8,780.

Exam Fees: Because you need five certifications to achieve the GSP prices vary significantly. If you already hold a GIAC Security Essentials (GSEC), the cost of one of the required certifications drops from $1,299 to $499. Most certifications required are priced at either $999 or $1,299 per attempt, though certification can cost up to $5,595.

Microsoft Certified Azure Cybersecurity Architect Expert

Those who earn the Microsoft Certified: Cybersecurity Architect Expert credential are able to translate a cybersecurity strategy into capabilities that protect the assets, business, and operations of an organization. Through the certification process, candidates learn to design, guide the implementation of, and maintain security solutions that follow zero-trust principles and best practices. You’ll also be able to design solutions for governance, risk, and compliance (GRC), security operations, and security posture management.​

As a prerequisite, candidate must have earned one of the following: Microsoft Certified: Azure Security Engineer Associate, Microsoft Certified: Identity and Access Administrator Associate, Microsoft Certified: Security Operations Analyst Associate certification.

Training fees: Self-paced training is available from the course’s page and free of charge. There is also an option to find an instructor-led training with pricing starting at $1,300.

Exam Fees: The exam costs $165 and Microsoft offers free practice assessments.

Certificate of Cloud Security Knowledge (CCSK)

As a certificate and not a certification — an important distinction — the Cloud Security Alliance (CSA) positions its Certificate of Cloud Security Knowledge as the foundation for future credentials and upskilling in the sector. From this perspective, the CCSK is helpful for cybersecurity analysts, compliance managers, security engineers, architects, and administrators. This vendor-neutral certificate has been recently updated and covers topics in zero trust, DevSecOps, cloud telemetry and security analytics, artificial intelligence, and more. CCSK offers a variety of training modalities, including an exam prep kit, instructor-led classes offered virtually and in person, and an online self-paced option. Candidates must score at least 80% on the exam, randomly pulling 60 multiple-choice questions from a test bank.

Training fees: Prices vary based on modality. A self-paced course and exam bundle costs $795, and online, instructor-led training begins at $995.

Exam fees: The exam costs $445, though discounts are available for corporate members, and US military veterans can take it for free.

Certified in Risk and Information Systems Control (CRISC)

Administered by ISACA, the Certified in Risk and Information Systems Control certification provides candidates with training across four domains: corporate IT governance, risk assessment, risk response and reporting, and technology and security. CRISC is ideal for candidates who want to enhance and optimize business resilience and risk management across their organization. The exam consists of 150 questions across the four domains. Since ISACA began offering CRISC in 2010, more than 23,000 people have obtained the certification. ISACA claims 52% of certificate holders experienced on-the-job improvement, and CRISC is the “4th top-paying certification worldwide.” To qualify for CRISC, candidates must adhere to a code of professional ethics and have three years of work experience in risk assessment and risk response and reporting. On passing the exam, candidates must submit 20 CPE credits annually and 120 continuing professional education (CPE) hours every three years to maintain their CRISC.

Training fees: ISACA offers three resources: an online review course, $895; a review manual in print or digital, $139; and an annual subscription to a 833-question test bank, $399. Discounts are available for ISACA members.

Exam fees: $575, ISACA members; $760 for non-members; plus $50 application fee.

Certified Information Systems Auditor (CISA)

The Information Systems Audit and Control Association (ISACA)’s CISA is geared toward IT auditors who wish to upskill or earn a pay boost. According to ISACA, 70% of CISA holders report on-the-job improvement, and another 22% receive a raise. The course covers five domains: information systems auditing, implementation, and operations; protection of information assets; and IT governance. The four-hour exam consists of 150 multiple-choice questions, and candidates must earn 450 on ISACA’s scaled scoring system, with 800 representing a perfect score. To maintain their CISA, certification holders must take 20 CPE credits annually and 120 over three years through conferences, volunteering, on-demand learning, and other methods as well as paying maintenance fee. To qualify, you must have five years of experience in IT or IS audit, control, assurance, or security. You can apply for an experience waiver for up to three years.

Training fees: ISACA offers four resources: an online review course for $895, an annual subscription to a question bank for $399, and a print or digital review manual for $139. Discounts are available for ISACA members. 

Exam fees: $575, members; $760, non-members; plus $50 application fee.

Certified Information Systems Security Professional (CISSP)

CISSP is a generalist cert from ISC2 aimed at security pros who have already established a strong track record. Advanced-level analysts interested in getting CISSP certified will need to know all the ins and outs of security and risk management, asset security, operations, security assessment and testing, and more. The CISSP certification requires five years of full-time experience in at least two of its eight domains. The exam is adaptive, ranging from 100 to 150 questions, including multiple-choice and advanced items of varying formats. Candidates need to score 700 points out of 1,000 to pass the exam.

Training fees: Online self-paced training fees start at $595 and can cost up to $1,993; online instructor-led bootcamp costs $2,880.

Exam fee: $749

Certified Secure Software Lifecycle Professional (CSSLP)

This ISC2 certification helps cyber pros build their career by training them to better incorporate security practices throughout software development phases. The CSSLP exam evaluates experience across eight domains: secure software concepts; secure software; lifecycle management; secure software requirements; secure software architecture and design; secure software implementation; secure software testing; secure software deployment, operations, maintenance; secure software supply chain. Those wishing to acquire the CSSLP must have four years of paid work experience as a software development lifecycle professional in one or more of the eight domains.

Training fees: Online self-paced training fees start at $550 and can cost up to $1,718; online instructor-led bootcamp costs $2,650.

Exam fee: $599

Check Point Certified Security Master (CCSM)

To become a Check Point Certified Security Master (CCSM) security professionals must have an active Certified Security Expert (CCSE) and mast have completed two subsequent Check Point Specialist accreditations. CCSM validates advanced expertise in configuring, deploying, and troubleshooting Check Point solutions. Check Point certifications are valid for 24 months.

Training fees: Training for CCSE is $3,500

Exam fee: The fee for CCSE is $300

GIAC Experienced Cybersecurity Specialist (GX-CS)

The Experienced Cybersecurity Specialist (GX-CS) sits within the applied knowledge certifications with GIAC. The certification is for practitioners to show their qualifications for advanced, hands-on IT systems roles across cybersecurity. Its intent is to demonstrate the candidate can navigate evolving real-world threats. The certification covers five areas: network security analysis and tools; evaluation of Windows and Linux OS security; advanced security tools and techniques; common attacks and defenses; and implementing overall cybersecurity and information security. The GX-CS is for GSEC holders who acquired additional experience — the GSEC exam costs $999, and SANS Institute offers training for GSEC.

Training fees: There are a few related affiliate training programs provided by SANS, each costing approximately $9,000.

Exam fee: $499 for those with an active GSEC; otherwise $1,299.

OffSec Certified Professional (OSCP+)

To earn the OffSec Certified Professional certification, candidates must complete the affiliated course, PEN-200: Penetration Testing with Kali Linux, and pass the subsequent exam. The course covers 20 plus modules, including information gathering, vulnerability scanning, encryption and cryptography, Active Directory and AWS exploitation, and more. Certificate holders will have shown mastery of penetration testing methodologies ideal for new roles, such as an ethical hacker, incident responder, or threat hunter. The OSCP+ exam is entirely hands-on, and test-takers must compromise systems within a lab environment.

OffSec does not enforce any prerequisites but recommends candidates be familiar with TCP/IP networking, scripting in Bash and Python, and Linux and Windows, which they can learn through its Network Penetration Testing Essentials Learning Path.

Training and exam fees: OffSec bundles the course and exam for $1,749 and as a yearly subscription that includes access to one 200 or 300-level course, the associated labs, and two exam attempts for $2,749 annually.

OffSec Experienced Penetration Tester (OSEP)

The OffSec Experienced Penetration Tester is ideal for penetration testers and ethical hackers who need more advanced techniques to sharpen offensive skills against modern enterprise defenses. Across more than 20 modules, the certification introduces these professionals to advanced offensive techniques, EDR and AV evasion, advanced Windows offensive security and more. During the two-day proctored exam, professionals must connect to a lab environment via a VPN and compromise multiple machines within a network through several possible attack paths. To pass, professionals must achieve the objective stated within the control panel or score at least 100 points — 10 points are awarded for every flag found in a local.txt or proof.txt file. Professionals who earn their OSEP can also obtain their OSCE³ Certification to demonstrate their mastery of offensive security. They would also need to pass the exams for WEB-300: Advanced Web Attacks and Exploitation and EXP-301: Windows User Mode Exploit Development, after which the OSCE³ is automatically awarded.

While there are no formal prerequisites for OSEP, OffSec recommends candidates take the PEN-200: Penetration Testing with Kali Linux or have a strong foundation in operating systems, networking, and scripting. 

Training and exam fees: OffSec bundles the course and exam for $1,749, and as a yearly subscription that includes access to one 200 or 300-level course, the associated labs, and two exam attempts for $2,749 annually.

OffSec Exploitation Expert (OSEE)

OffSec’s Offensive Security Exploitation Expert is a vendor-specific certification, focusing on advanced Windows exploitation, with OffSec deeming it its most challenging certification. As a penetration testing course, the material dives deep into topics such as advanced heap manipulations and disarming WDEG mitigations. Certificate holders can identify problematic code in Windows operating systems and develop exploits. For the practical exam, candidates must complete a comprehensive penetration test of software and create an exploit within a lab environment — all within 72 hours. To qualify, you must have experience debugging, developing Windows exploits, and using the following technologies: WinDBG, x86_64, IDA Pro, and basic C/C++ programming. OffSec recommends completing its 300-level certifications before OSEE.

Training and exam fees: OffSec offers only instructor-led, in-person training. Enterprises should inquire for more information.

Conocido sobre todo por sus servicios como “boutique de inversión”, el banco Singular Bank, nacido en el año 2020 gracias al impulso del exconsejero delegado del Banco Santander Javier Marín Romano, después de adquirir y transformar la estructura del antiguo banco digital Self Bank, es en la actualidad una entidad con más de 400 profesionales, 17 oficinas en España y más de 180 banqueros y agentes financieros que gestionan casi 18.000 millones de euros de patrimonio de clientes.

La compañía financiera es noticia estos días por un sonado cambio de propietario: el grupo inversor estadounidense Warburg Pincus, que poseía más del 93% de las acciones, ha vendido su participación a un grupo de inversores nacionales e internacionales liderados por ING, que se convierte ahora en el principal dueño de la entidad, con 40%, mientras que el resto se diversifica entre ‘family offices’ españoles (21,5%), el propio equipo directivo y los empleados del banco (15,5%), la firma española de inversión ProA Capital (15%) y la financiera mexicana Actinver (8%). Un movimiento que, sin embargo, no afectará a la operativa de Singular Bank, que seguirá funcionando como una entidad independiente dentro del mercado español de banca privada y con una oferta de productos complementaria a la de su nuevo dueño mayoritario, ING, como confirma a CIO ESPAÑA Rubén Andrés Priego, director general de Tecnología y Operaciones de Singular Bank y miembro del equipo directivo, entrevistado por esta cabecera unos días antes de que se hiciera pública esta información. La charla gira, no obstante, en torno a otro asunto: el intenso viaje digital experimentado por el banco, y los retos y las oportunidades que se abren a la organización en un futuro próximo.

El directivo, un ejecutivo experimentado en el mundo de la banca minorista y privada y en consultoría financiera (desempeñó puestos de responsabilidad en Banco de España, Banco Popular, BBVA, Banca March y EVO Banco) aterrizó en Singular Bank en enero de 2025. En este periodo, explica, ha inyectado las últimas innovaciones de la inteligencia artificial al modelo “híbrido” con el que contaba la entidad, que combina “la excelencia digital y en automatización con una labor intrínsecamente humana”. Pero empecemos por el principio.

La IA da una vuelta de tuerca a la transformación de Singular Bank

“Singular Bank es un banco con una historia relativamente corta, de unos siete años”, rememora el CIO durante la entrevista, subrayando que, aunque la entidad nació como un banco orientado a la banca privada, a través de Self Bank dispone de una plataforma bancaria 100% digital que permite a los clientes gestionar sus inversiones y “tomar sus propias decisiones financieras”. Al aterrizar en la corporación, hace más de un año y medio, y después de mantener, dice, casi 70 reuniones con el consejo de administración, el comité de dirección y empleados de diferentes áreas del banco, el directivo vio la necesidad de “llevar a cabo un plan de transformación que no solo pasaba por modificar la plataforma tecnológica que había y que mantenía la cuenta de resultados, así como todos los canales, la plataforma de inversión, etc. sino afrontar una segunda gran ola: el uso de la inteligencia artificial en la compañía”.

La estrategia de Singular Bank en torno a la inteligencia artificial se articula en tres grandes líneas de actuación, como relata Rubén Andrés Priego. La primera consiste en impulsar el uso de la IA en el trabajo diario de los empleados. Para ello, la empresa selló un acuerdo con OpenAI, en marzo de 2025, con el fin de distribuir 50 licencias de ChatGPT entre los 400 profesionales de la entidad. La segunda línea se centra en la aplicación de la IA al ámbito de la ingeniería, bajo un enfoque de context engineering (ingeniería de contexto), con el que están transformando su modelo de desarrollo tecnológico. Y el tercer eje pasa por crear una arquitectura propia orientada a los agentes de inteligencia artificial. Sobre esta base, explica el CIO, la entidad ha lanzado Singularity, una plataforma que integra la IA de OpenAI, la visión del equipo de estrategia y el criterio del banquero para ofrecer un mejor asesoramiento al cliente, “uno de los proyectos estrella de este año”.

Esta apuesta por la IA y, en concreto, por la tecnología de OpenAI es de tal calado que la empresa creadora de ChatGPT ha seleccionado al de Singular como uno de los casos de éxito más destacados de Europa en la aplicación de IA dentro del sector financiero. “Lo que tiene Singularity de particular es que lo han creado los propios usuarios”, indica Andrés, que explica que el banco formó a los usuarios de ChatGPT para que fueran ellos los que identificaran oportunidades y propusieran nuevas aplicaciones. “Una de las grandes ventajas de la IA generativa es que ha reducido las barreras tecnológicas: hoy el valor diferencial está más en identificar el caso de uso adecuado que en la propia ejecución técnica”, añade Andrés Priego.

A partir de ahí, relata, los banqueros empezaron a detectar necesidades que requerían una infraestructura más sofisticada que una simple interfaz conversacional. “Fue entonces cuando decidimos crear un grupo de 20 banqueros que actuaron como embajadores de Singularity y participaron activamente en el diseño de la herramienta. Mantuvimos sesiones semanales para perfeccionarla y, hoy en día, los resultados son de éxito total”.

La plataforma Singularity está diseñada para facilitar el asesoramiento al cliente en las fases previas a cada reunión. La plataforma recopila información procedente de todos los sistemas del banco y se integra con Salesforce, gestores documentales, bases de conocimiento, datos de rentabilidad y catálogos de productos. Todo ese ecosistema está conectado a una red de 20 agentes de IA que analizan la información y generan recomendaciones y resultados útiles para el banquero. El objetivo, indica el CIO, es “liberar a los profesionales de tareas operativas y de bajo valor”, como la recopilación manual de información, para que puedan centrarse en lo realmente importante: “El asesoramiento personalizado al cliente”.

“Una de las grandes ventajas de la IA generativa es que ha reducido las barreras tecnológicas: hoy el valor diferencial está más en identificar el caso de uso adecuado que en la propia ejecución técnica”

La acogida de la plataforma, subraya Rubén Andrés, ha sido excelente. “Actualmente, 80 banqueros participan en el proyecto y el 90% utiliza la herramienta de forma habitual en su trabajo diario”. Este mes de julio, añade, Singularity se ampliará a los 200 banqueros que tiene la entidad. “La vamos a conectar con toda la inteligencia de inversiones y productos. Ya no va a ser solo una herramienta de recolección de información, sino que también utilizará la inteligencia del banco para perfilar el asesoramiento”.

El ROI (retorno de inversión) de este caso de uso es significativo: “Los banqueros se ahorran entre 60 y 90 minutos diarios en la preparación de reuniones”, afirma el CIO. Un ahorro que redunda en “más tiempo para pensar qué necesita el cliente y qué podemos aportarle como banco para mejorar su día a día, su vida y sus objetivos”. 

El banquero, no obstante, sigue siendo una pieza fundamental de la ecuación, según Rubén Andrés. “Es indispensable; por ejemplo, en momentos de incertidumbre, donde hay caídas de mercados y la situación se tensa, el banquero tiene la cabeza fría y pone raciocinio a las emociones del cliente, al que acompaña en las inversiones, pero siempre apoyado en unas capacidades avanzadas de análisis de datos e inteligencia”. Es más, según el CIO, “el humano es imprescindible en el mundo de la banca”, sea cual sea el modelo: “Banca masiva, mainstream digital, banca privada o un término intermedio que sería Self Bank, un banco 100% digital con capacidades avanzadas y con unos banqueros remotos que también pueden asesorar a los clientes”.

El ejecutivo cree que la banca del futuro será más humana porque es precisamente esto lo que va a diferenciar a los distintos actores entre sí. “El factor humano aumentará la calidad en el asesoramiento y en el trato al cliente. Por otro lado, también veremos redes de agentes en el entorno digital; me atrevería a decir que ya no competiremos por la experiencia de usuario (‘user experience’), sino por la del agente (‘agent experience’). Vamos a tener que generar capas de agentes que se comuniquen entre sí y generen una experiencia muy fluida para poder analizar los productos de los que disponemos, la oferta, y comparar unos con otros y saber qué encaja mejor con el cliente en función de su contexto y el del banco. Vamos a ver una revolución en banca en los próximos años de manera clara”, expone.

Gobierno, cultura y adopción, los desafíos de abrazar la inteligencia artificial

Aunque la IA trae consigo ingentes oportunidades, también abre retos de distinta índole. Preguntado por este asunto, el CIO contesta: “El mayor retorno que tiene la IA actualmente es tecnológico. Avanza cada vez más rápido y es más accesible, de hecho, cualquiera puede usar ChatGPT incluso para programar; pero el mayor reto lo hemos encontrado en materia de gobierno, cultura y adopción”.

Para subsanar el desafío del gobierno, en Singular Bank han creado un “comité de gobierno de la IA y una política que todos los empleados deben conocer y firmar antes de poder utilizar todas estas herramientas”, explica el CIO. En este comité, de carácter multidisciplinar —está formado por el equipo de Legal Compliance, el DPO (delegado de protección de datos), el equipo de seguridad y el de tecnología y operaciones— y alineado con la regulación vigente —como la IA Act (Reglamento europeo de IA) y el RGPD (Reglamento General de Protección de Datos), entre otros— se mide el riesgo que tiene cada una de las plataformas de IA antes de adoptarlas. Solo después se ponen a disposición de los empleados. Además, agrega, en los casos de uso que han desplegado, como Singularity, realizan un análisis continuo del rendimiento de los modelos para garantizar que éste se ajusta a lo buscado y, si no, se aplican ajustes a nivel tecnológico. 

Para superar el reto cultural y las barreras de adopción, pues “toda esta revolución de la IA ha generado muchos miedos a la pérdida de empleo y a ser sustituido”, el banco ha trabajado en ofrecer amplia formación sobre estas nuevas tecnologías “desde el punto de vista de hacia dónde va el mercado, pero también mirando al pasado, porque si nos hubiéramos negado a la imprenta, el ferrocarril y todas las revoluciones industriales, no estaríamos en el nivel de sofisticación actual; hemos trasladado a los empleados que esto es una revolución a la que hay que sumarse, pues gracias a ella vamos a tener mejores profesionales y un mejor servicio a nuestros clientes”. La clave, añade, ha sido hacer a los empleados “creyentes del cambio, darles formación y acompañarlos”.

El gran cambio de mentalidad con la IA, explica, es que los propios empleados pueden desarrollar sus propias soluciones tecnológicas, sin pedirlas al equipo de tecnología. “Esta es la gran revolución que estamos acometiendo”, sentencia.

El coste, en el punto de mira

Una de las grandes preocupaciones de los líderes de TI a la hora de abrazar la IA es que el coste se dispare y provoque situaciones similares a las vividas con la adopción de cloud en el pasado. “Cuando iniciamos el proyecto con OpenAI, en marzo de 2025, trabajábamos con un modelo de tarifa plana: pagábamos una licencia que cubría tanto el desarrollo de ingeniería con herramientas como Cursor como el uso de ChatGPT. Todo estaba incluido. Actualmente hemos pasado a un modelo de coste por consumo de tokens. Desde la perspectiva de negocio, no nos preocupa esta evolución, ya que la plataforma cubre con creces nuestras necesidades actuales. Además, hemos realizado proyecciones a futuro y no prevemos ningún problema significativo. Incluso en el caso de que se produjera un ligero sobrecoste, los beneficios obtenidos compensarían con creces ese incremento del gasto”, reflexiona Rubén Andrés.

Desde el punto de vista de ingeniería, añade, “el retorno también compensa el sobrecoste, ya que hemos multiplicado la productividad y las capacidades de nuestros desarrolladores”. Sí han observado, afirma el CIO, la necesidad de establecer cuotas y mecanismos de control del consumo, pues en algún caso han detectado que ciertos desarrolladores utilizaban modelos muy avanzados para tareas que no requerían ese nivel de capacidad, lo que generaba un consumo innecesario de tokens. Unas situaciones en las que modelos más sencillos habrían ofrecido el mismo resultado con un coste considerablemente menor.

El gran cambio de mentalidad con la IA, explica Rubén Andrés, es que los propios empleados pueden desarrollar sus propias soluciones tecnológicas, sin pedirlas al equipo de tecnología. “Esta es la gran revolución que estamos acometiendo”

Por todo ello, explica, el banco está aplicando los mismos principios de gestión financiera que ya utilizó con la nube: “Hemos trasladado el uso de FinOps al ámbito de la inteligencia artificial para optimizar el uso de los recursos, controlar los costes y garantizar que cada caso de uso emplee el modelo más adecuado, además de hacer un seguimiento mensual del consumo”.

Impacto en el desarrollo de software

Los desarrolladores de software son algunos de los perfiles profesionales más directamente impactados por el auge de la IA agentiva y generativa. “En el mercado en general muchos programadores ven la IA como una amenaza, pero, en realidad, esto va a hacer que se multiplique el software porque, de hecho, el mundo necesita más. Los bancos ya teníamos mucha más demanda de la que podíamos abordar. Por eso yo no veo que la IA sea un problema para los desarrolladores”.

Dicho esto, el CIO reconoce que el tipo de perfiles técnicos que busca la entidad en la actualidad ha cambiado: “Ahora buscamos perfiles técnicos senior, pues cuanta más experiencia tiene el profesional, mejor adopta la IA; también que tengan baja resistencia al cambio”. En este sentido, en el banco ya trabajan de forma que sus técnicos lideran equipos virtuales formados por los roles [agentes] de project manager, analista funcional, analista orgánico, etc. pero alimentados con el contexto y la arquitectura de la entidad. “Una iniciativa con la que te vuela la cabeza”, sentencia Rubén Andrés.

El ROI del uso de la IA asociado al área de desarrollo de ingeniería es también significativo, según el directivo. “Ahora un programador desarrolla software como si fuera un equipo de 15 desarrolladores, y lo hace a una velocidad nunca vista antes y con una calidad totalmente alineada con la requerida por el banco”.

El área de Tecnología de Singular Bank, cuyo equipo asciende a 41 personas, está abordando “una transformación completa de todos los sistemas legacy con toda esta tecnología”, recalca el CIO. “Es un catalizador del cambio”, añade.

Foco en la soberanía tecnológica

Preguntado por si le preocupa la soberanía tecnológica en el complejo contexto actual, marcado por un escenario geopolítico tensionado, Rubén Andrés es contundente: “Mucho”. Más aún, reconoce, después de ver cómo el Gobierno de Estados Unidos paralizaba recientemente el acceso a los modelos más avanzados de Anthropic (una decisión después revertida). Por ello, la filosofía del CIO es que “todo lo que construyamos permita la continuidad de negocio, es decir, el aumento de capacidades cognitivas es un ‘on top off’ de lo que son los básicos de poder llevar a cabo el asesoramiento y el resto de las tareas que hemos hecho antes de tener estos sistemas”. Además, reconoce, ya están trabajando en “desacoplar la capa agentiva. Esta pertenece a Singular Bank, es nuestra propia plataforma, a la que ponemos por encima una capa de experiencia. Ahora la capa de agentes la tenemos con tecnología de OpenAI (ChatGPT), pero se podría cambiar por cloud o una capa propia”.

Además, desvela a CIO ESPAÑA, Singular Bank está reforzando su estrategia de continuidad de negocio mediante una arquitectura multirregión en AWS que distribuye sus sistemas entre España (en Zaragoza) y el norte de Europa. El objetivo, asegura, es garantizar la resiliencia operativa y la recuperación rápida de los servicios ante incidencias graves, desde fallos técnicos hasta posibles ciberataques o eventos geopolíticos que afecten a una determinada ubicación.

Singular Bank está reforzando su estrategia de continuidad de negocio mediante una arquitectura multirregión en AWS que distribuye sus sistemas entre España (en Zaragoza) y el norte de Europa

Ya desde el punto de vista de la ciberseguridad, en la entidad están empleando la propia IA para crear ‘red teams’ virtuales que se encarguen de analizar continuamente la superficie de ataque y detectar posibles problemas y poder resolverlos antes de que los detecte un tercero. “La ciberseguridad nos preocupa mucho porque, al final, somos una infraestructura crítica y debemos ser responsables y tener control sobre lo que manejamos”.

La ventaja que aporta disponer de capacidades de análisis en ciberseguridad junto a las de un desarrollo vertiginoso facilita que “la defensa pueda abordarse de manera mucho más rápida”, reflexiona el portavoz.

Preguntado por cómo absorbe el banco toda la avalancha normativa que existe y si esta supone un freno a la innovación, el directivo también es claro: “Llevo trabajando en banca 20 años y nunca he visto la regulación como un freno para este sector. Honestamente, creo que este argumento se ha utilizado como excusa, pero la regulación es muy necesaria, garantiza que demos un servicio seguro y confiable”.

En Singular Bank, añade, “hay un equipo muy abierto a las nuevas tecnologías y a analizar la regulación desde un punto de vista más de facilitar el cambio que de bloquearlo”. “Si se adopta con sentido común y agilidad y todos los equipos del banco trabajan en pro del negocio y de la innovación esta no es un freno”.

Aumentar la escalabilidad del banco, el eje de futuro

En un futuro próximo, el equipo de Tecnología y Operaciones de Singular Bank, con Rubén Andrés al frente, proseguirá la transformación y modernización en la que está inmerso el banco. “Estamos renovando todos los sistemas legacy a través de tecnologías con IA y context engineering y, por otro lado, continuaremos trabajando la parte de agentes. Ya hemos desarrollado algunos que reemplazan o capacitan a las áreas de operaciones para poder hacer conciliaciones bancarias cada cinco minutos, mientras que antes las hacíamos cada día; esto nos permitirá aumentar la escalabilidad del banco, que es nuestro objetivo de cara a los próximos años. Y, finalmente, seguiremos apostando fuerte por las arquitecturas agentivas para poder generar experiencias de cliente mucho más naturales a través de conversaciones a través de los canales digitales”.

Respecto a la evolución futura del rol del CIO en general, el ejecutivo es plenamente consciente de la transformación que esta función ha tenido en los últimos años, ahora “más pegado al negocio”. “En la actualidad, se ha amplificado más el rol del CIO, que ya no está ligado a la continuidad, la resiliencia o la seguridad, sino es un rol de agente del cambio, impulsor de la adopción de nuevas tecnologías, formador, visionario de nuevos modelos de negocio… En definitiva, un rol, paradójicamente en esta era de la IA, cada vez más humano”.