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.

Cloudflare recently beat Q1 2026 earnings. Revenue up 34% year over year. EPS ahead of consensus. Full-year guidance raised. Then, in the same breath, they announced 1,100 layoffs, 20% of the company. CEO Matthew Prince’s explanation: “The way we work at Cloudflare has fundamentally changed.”

Block did the same thing. Beat guidance, raised outlook, cut 4,000+ jobs. Both framed it as architecting for the AI era.

This is not a contradiction. This is the new math boards are running. And if you’re a CIO who hasn’t started running it yourself, you’re behind.

The benchmark has moved

AI-native companies have quietly reset what “efficient” means for a technology organization. Midjourney generates over $500M in revenue with roughly 160 employees, over $3M per head. Anthropic hit a $14B annualized run rate in early 2026 with fewer than 3,000 employees. Across the top AI-native startups, the average revenue per employee is $3.48M, nearly twelve times the traditional SaaS benchmark of $300K.

Boards aren’t comparing you to your 2019 self anymore. They’re comparing you to Anthropic.

This is the pressure Cloudflare and Block are responding to. They’re not cutting people because the business is struggling. They’re cutting because investors have internalized a new denominator. Headcount is no longer a proxy for capacity; it’s a liability on the efficiency ratio.

For CIOs, this creates a hiring problem that looks nothing like the cloud or mobile talent gaps of the past decade. Those gaps were about volume: hire 100 cloud engineers, absorb the cost, build the capability… This one is about density; you’re not looking for 100 people. You’re looking for 10 who can deliver what 100 couldn’t, and justify $1M or more in value per seat.

Finding bodies to fill seats has never been easier. Finding people who operate at that level of leverage is a different problem entirely.

‘Acqui-hires’ are a shortcut with a hidden cost

Companies have figured out that recruiting AI-native talent one by one is too slow and that it’s faster to buy a team. Google’s acquisition of the Windsurf founders, Meta bringing in the Scale AI team, Accenture’s string of AI-focused acquisitions: these are acqui-hires dressed up as M&A. The premium on experienced AI talent is high enough, and the urgency real enough, that organizations are skipping traditional hiring loops entirely and buying their way in.

I’ve been on the other side of this. My company, MadKudu, was acquired by HG Insights specifically to bring AI-native capability into an established enterprise business. HG needed change agents who had already figured out how to build and ship in this new era, not just people who’d read about it. That’s the thesis behind most of these deals.

But there’s a cost that doesn’t show up in the acquisition price.

AI-native teams are fast because they operate with a different set of defaults: full access to tools, minimal governance layers, the ability to experiment and ship without a six-week approval cycle. That operating model is not a perk; it’s the fundamental mechanism. It’s why a team of 10 can do what an enterprise team of 100 can’t.

When you acqui-hire that team and then slot them into your existing approval processes, you’ve bought the people and killed the engine. The change agents you paid for become change-frustrated. The attrition that follows is expensive and predictable.

The harder realization: acquiring an AI-native team means accepting how they work. That requires deliberately carving out space for them to operate differently, not just tolerating it but institutionalizing it. The acquisition is an organizational change program, not just a hiring event.

The CIO’s real problem

The governance stack most enterprise organizations run was designed for a headcount world. Every tool vetting cycle, every vendor review, every security approval was calibrated assuming you were managing a large team where consistency and control were the primary objectives.

That calculus breaks when your goal is talent density. The same approval processes that protect against data leaks are now the reason your best people can’t do their best work. When it takes six weeks to approve a tool that your competitor’s team is already shipping with, you’ve traded velocity for the perception of safety.

The practical fix is structured experimentation: clear guardrails, defined boundaries, but explicit permission to try tools before deciding whether to roll them out broadly. Gating everything prevents you from ever discovering what 10x productivity looks like.

The skills inventory question is also more nuanced than it sounds. Job titles won’t tell you where the leverage is. You need to map the actual tasks within each function and assess which can be automated or augmented with AI. That’s where you find the people who, with the right tools, become your $1M/employee talent, not because you hired differently, but because you enabled better.

This is also where the build-versus-buy question gets genuinely tricky. As AI reshapes how products are built and delivered, your internal operating model — how you work, how fast you ship, how you use data — is becoming core IP. Outsourcing delivery means outsourcing the part of the organization where your competitive advantage is now being built.

Closing the gap without slowing down

The AI talent wars are not primarily a recruiting problem. They’re a rethinking of what organizations are supposed to look like.

Boards have a new benchmark. Cloudflare, Block, Amazon, Meta and others have already started restructuring to meet it, publicly, painfully, even while beating their numbers. The question for CIOs isn’t whether this pressure arrives; it’s whether you’re ahead of it or behind it when it does.

The organizations that navigate this well won’t win by outbidding competitors for a handful of elite engineers. They’ll win by designing operating systems that amplify the leverage of the talent they do have, by enabling their best people rather than constraining them, and by treating AI fluency as a core organizational capability rather than a niche specialization.

Talent density is the new headcount model. The sooner your governance, your tooling and your board conversations reflect that, the better positioned you’ll be when the next efficiency report lands.

This article is published as part of the Foundry Expert Contributor Network.
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In August 2025, 126 malicious packages landed in the npm registry. Even after the community caught the initial wave, 80 of these hidden backdoors remained actively listed.

That was enough. Over 86,000 downloads. Malicious code in PhantomRaven, packages running in the production systems of Fortune 500 companies worldwide. And throughout the entire window, not a single EDR/XDR alert.

This happened because the attack surface has expanded to a layer EDR/XDR was never designed to see: VS Code extensions, local MCP servers, and rogue AI coding assistants that inherit your engineers’ valid credentials to steal data at machine speed.

To eliminate this structural vulnerability, Palo Alto Networks acquired Koi, an AI-native developer security product engineered for proactive, precision enforcement. Below we compiled a 2026 CISO checklist you can use to audit your environment and see how Koi automates each defense from day one.

#1. Gain real-time visibility into shadow AI & extensions

Your existing asset management tracks binaries and installers, but it cannot see local VS Code extensions, MCP servers, or ad-hoc Python scripts running on developer endpoints. This visibility gap was recently exposed by the MaliciousCorgi campaign, where two marketplace extensions with 1.5 million combined installs silently harvested every file a developer opened. Neither triggered any detection because they were not binaries, not executables, not anything your inventory was built to flag. To counter this, Koi closes the gap by analyzing what extensions actually do after installation, exposing hidden data-harvesting channels running inside your active workspace.

#2. Distinguish between human and autonomous agent behavior 

When a rogue AI agent exfiltrates your proprietary source code, it uses a developer’s valid credentials during normal working hours, making the session look entirely legitimate to standard XDR baselines. Moving beyond static permission lists, Koi deploys behavioral profiling within the workspace runtime. By actively intercepting unauthenticated background tasks and blocking unauthorized file-system reads, it stops automated data exfiltration in real time.

#3. Establish guardrails for automated package updates on endpoints

Developers prioritize speed, often allowing software packages to auto-update on their endpoints the moment a new version appears. Attackers weaponize this supply chain vulnerability, as seen in the May 2026 Team PCP attack where 3,800 GitHub repositories were compromised in just 36 minutes via poisoned auto-updates. Securing agentic endpoints against these rapid breaches requires behavior-based inspection within the active workspace context. Koi operates at this layer by providing safe deployment buffers that automate version cooldowns, blocking bleeding-edge updates until they are vetted. By continuously auditing process creation within the IDE runtime, Koi instantly drops unauthorized remote connections before malicious payloads can exfiltrate credentials from the endpoint.  

#4. Enforce principle of least privilege for AI agents

AI coding assistants inherit the privileges of whoever deployed them. In practice, that means read access to production databases, write access to core repositories, and access to every secret in environment files and configuration directories. To restrict this excessive access, Koi applies dynamic sandboxing directly to AI agent processes at the kernel level. It enforces a strict zero-trust boundary that segregates sensitive workspace vectors, preventing agents from pulling data outside their approved scope without interrupting developer workflows.

#5. Maintain continuous endpoint posture management

Signature-based scanning only stops known threats. Sophisticated repository attacks often arrive as functional, high-rated software that carries no known bad signature. Koi’s research into the DarkSpectre campaign found eight browser extensions, all carrying “featured” badges from Google and Microsoft, installed by over 8 million users, silently harvesting every conversation from ChatGPT, Claude, and Gemini in the background. Koi addresses this by operating upstream: scanning marketplace listings every hour, using LLM-driven code analysis to compare what software promises against what its code does, sandboxing it, and scoring the risk before it ever reaches the endpoint.

Summary

Securing the modern enterprise is no longer about patching individual gaps. As AI agents redefine the workforce, Agentic Endpoint Security (AES) is now a strategic imperative for every CISO. By establishing a mandatory control plane for the AI-native workspace, AES ensures that your organization can scale engineering velocity without ever compromising enterprise integrity. 

Ready to secure the future of your software stack? See how Koi Agentic Endpoint Security delivers complete visibility, risk scoring, and real-time prevention across every endpoint in your enterprise.

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.

In August 2025, 126 malicious packages landed in the npm registry. Even after the community caught the initial wave, 80 of these hidden backdoors remained actively listed.

That was enough. Over 86,000 downloads. Malicious code in PhantomRaven, packages running in the production systems of Fortune 500 companies worldwide. And throughout the entire window, not a single EDR/XDR alert.

This happened because the attack surface has expanded to a layer EDR/XDR was never designed to see: VS Code extensions, local MCP servers, and rogue AI coding assistants that inherit your engineers’ valid credentials to steal data at machine speed.

To eliminate this structural vulnerability, Palo Alto Networks acquired Koi, an AI-native developer security product engineered for proactive, precision enforcement. Below we compiled a 2026 CISO checklist you can use to audit your environment and see how Koi automates each defense from day one.

#1. Gain real-time visibility into shadow AI & extensions

Your existing asset management tracks binaries and installers, but it cannot see local VS Code extensions, MCP servers, or ad-hoc Python scripts running on developer endpoints. This visibility gap was recently exposed by the MaliciousCorgi campaign, where two marketplace extensions with 1.5 million combined installs silently harvested every file a developer opened. Neither triggered any detection because they were not binaries, not executables, not anything your inventory was built to flag. To counter this, Koi closes the gap by analyzing what extensions actually do after installation, exposing hidden data-harvesting channels running inside your active workspace.

#2. Distinguish between human and autonomous agent behavior 

When a rogue AI agent exfiltrates your proprietary source code, it uses a developer’s valid credentials during normal working hours, making the session look entirely legitimate to standard XDR baselines. Moving beyond static permission lists, Koi deploys behavioral profiling within the workspace runtime. By actively intercepting unauthenticated background tasks and blocking unauthorized file-system reads, it stops automated data exfiltration in real time.

#3. Establish guardrails for automated package updates on endpoints

Developers prioritize speed, often allowing software packages to auto-update on their endpoints the moment a new version appears. Attackers weaponize this supply chain vulnerability, as seen in the May 2026 Team PCP attack where 3,800 GitHub repositories were compromised in just 36 minutes via poisoned auto-updates. Securing agentic endpoints against these rapid breaches requires behavior-based inspection within the active workspace context. Koi operates at this layer by providing safe deployment buffers that automate version cooldowns, blocking bleeding-edge updates until they are vetted. By continuously auditing process creation within the IDE runtime, Koi instantly drops unauthorized remote connections before malicious payloads can exfiltrate credentials from the endpoint.  

#4. Enforce principle of least privilege for AI agents

AI coding assistants inherit the privileges of whoever deployed them. In practice, that means read access to production databases, write access to core repositories, and access to every secret in environment files and configuration directories. To restrict this excessive access, Koi applies dynamic sandboxing directly to AI agent processes at the kernel level. It enforces a strict zero-trust boundary that segregates sensitive workspace vectors, preventing agents from pulling data outside their approved scope without interrupting developer workflows.

#5. Maintain continuous endpoint posture management

Signature-based scanning only stops known threats. Sophisticated repository attacks often arrive as functional, high-rated software that carries no known bad signature. Koi’s research into the DarkSpectre campaign found eight browser extensions, all carrying “featured” badges from Google and Microsoft, installed by over 8 million users, silently harvesting every conversation from ChatGPT, Claude, and Gemini in the background. Koi addresses this by operating upstream: scanning marketplace listings every hour, using LLM-driven code analysis to compare what software promises against what its code does, sandboxing it, and scoring the risk before it ever reaches the endpoint.

Summary

Securing the modern enterprise is no longer about patching individual gaps. As AI agents redefine the workforce, Agentic Endpoint Security (AES) is now a strategic imperative for every CISO. By establishing a mandatory control plane for the AI-native workspace, AES ensures that your organization can scale engineering velocity without ever compromising enterprise integrity. 

Ready to secure the future of your software stack? See how Koi Agentic Endpoint Security delivers complete visibility, risk scoring, and real-time prevention across every endpoint in your enterprise.

Cisco and AMD have expanded their partnership with a new package of hardware and security software that’s designed to help enterprise customers protect, deploy, and manage distributed AI resources.

During AMD’s Advancing AI event this week, Cisco’s president and chief product officer Jeetu Patel took to the stage during AMD CEO Lisa Su’s keynote to talk about how AI inference will be widely distributed and will require an architectural stack of software and tools that Cisco and AMD are partnering to develop.

The joint architecture combines AMD’s compact, high-performance Ryzen AI Halo hardware and a variety of Cisco networking, observability, governance, and security technologies. “AMD provides the deskside/local AI platform. At the foundation is AMD Ryzen AI Halo hardware, an isolated agent sandbox and the services needed for local-first inferencing, including model routing and token limits via AMD’s Semantic Router and local inference on Lemonade,” wrote Cisco’s Yash Sheth, senior director, engineering and research, in a blog post about the new package.

Ryzen AI Halo (pictured below) is designed to support local AI inference on an AI PC using its CPU, GPU, and XDNA neural processing unit (NPU), according to AMD. A resilient AI platform should continue delivering useful AI services even when connectivity is limited, models need to change, or workloads shift, AMD stated.

Cisco then wraps that platform in a secure harness that includes its Splunk Agent Observability plus Splunk Infrastructure Monitoring to provide full-stack observability, tracking agent behavior, tokenomics and compute operation, according to Sheth.

Cisco also brings its AI Defense for model and agent security; DefenseClaw for security policy enforcement, so guardrails are enforced directly on-device, within the agent harness; and Cisco Cloud Control offering a single pane of glass for unified policy and control, Sheth stated.

“To make deskside and local AI computing work at enterprise scale, every AI node must be treated as a secure, managed node in the enterprise network,” Sheth wrote.

“The need for token efficiency and data sovereignty is driving a new class of computing, deskside computing, with users and teams putting AI agents right by their sides,” Sheth wrote. “Inference is moving to a hybrid architecture with thousands of ambient deskside agents in an enterprise helping employees have 24×7 productivity. That’s an extraordinary opportunity. It’s also a brand-new operating challenge.”

As agentic AI moves from experimentation to real enterprise workflows, organizations need more than powerful endpoints. AI agents can run continuously and act on enterprise data, but create new requirements for network infrastructure, tokenomics, agent behavior, and security, according to a statement from AMD.

“Running more AI locally can help improve responsiveness, keep sensitive data closer to users, and reduce dependence on cloud-only approaches, but enterprises also need a way to monitor and manage these systems at scale. AMD and Cisco are addressing that gap by collaborating to pair high-performance local AI compute with the observability, governance, and control infrastructure needed for enterprises to deploy it responsibly,” AMD stated.

“By combining AMD Ryzen AI Halo systems and our broader local AI software capabilities with Cisco’s enterprise networking, observability and security technologies, we are helping customers deploy AI in a way that is performant, secure, observable and manageable at scale,” said Jack Huynh, senior vice president and general manager, computing and graphics group with AMD, in a statement.

A few other interesting statistics and trends cited in AMD CEO Su’s keynote include:

• AI adoption is accelerating across all industries, with agentic AI driving a surge in compute demand and shifting workloads from training to inference, which accounts for 60% of global AI compute capacity in 2026.
• AI is moving beyond the cloud, with edge and personal devices becoming critical for real-time, distributed intelligence.
• The AI accelerator market is projected to reach $1.4 trillion by 2030, nearly tripling previous forecasts, with GPUs expected to dominate but CPUs gaining new growth vectors due to agentic AI.
• Server CPU market is forecasted to grow over 50% to $200 billion by 2030, fueled by rapid agentic AI adoption and the need for massive CPU infrastructure.

Cloudflare recently beat Q1 2026 earnings. Revenue up 34% year over year. EPS ahead of consensus. Full-year guidance raised. Then, in the same breath, they announced 1,100 layoffs, 20% of the company. CEO Matthew Prince’s explanation: “The way we work at Cloudflare has fundamentally changed.”

Block did the same thing. Beat guidance, raised outlook, cut 4,000+ jobs. Both framed it as architecting for the AI era.

This is not a contradiction. This is the new math boards are running. And if you’re a CIO who hasn’t started running it yourself, you’re behind.

The benchmark has moved

AI-native companies have quietly reset what “efficient” means for a technology organization. Midjourney generates over $500M in revenue with roughly 160 employees, over $3M per head. Anthropic hit a $14B annualized run rate in early 2026 with fewer than 3,000 employees. Across the top AI-native startups, the average revenue per employee is $3.48M, nearly twelve times the traditional SaaS benchmark of $300K.

Boards aren’t comparing you to your 2019 self anymore. They’re comparing you to Anthropic.

This is the pressure Cloudflare and Block are responding to. They’re not cutting people because the business is struggling. They’re cutting because investors have internalized a new denominator. Headcount is no longer a proxy for capacity; it’s a liability on the efficiency ratio.

For CIOs, this creates a hiring problem that looks nothing like the cloud or mobile talent gaps of the past decade. Those gaps were about volume: hire 100 cloud engineers, absorb the cost, build the capability… This one is about density; you’re not looking for 100 people. You’re looking for 10 who can deliver what 100 couldn’t, and justify $1M or more in value per seat.

Finding bodies to fill seats has never been easier. Finding people who operate at that level of leverage is a different problem entirely.

‘Acqui-hires’ are a shortcut with a hidden cost

Companies have figured out that recruiting AI-native talent one by one is too slow and that it’s faster to buy a team. Google’s acquisition of the Windsurf founders, Meta bringing in the Scale AI team, Accenture’s string of AI-focused acquisitions: these are acqui-hires dressed up as M&A. The premium on experienced AI talent is high enough, and the urgency real enough, that organizations are skipping traditional hiring loops entirely and buying their way in.

I’ve been on the other side of this. My company, MadKudu, was acquired by HG Insights specifically to bring AI-native capability into an established enterprise business. HG needed change agents who had already figured out how to build and ship in this new era, not just people who’d read about it. That’s the thesis behind most of these deals.

But there’s a cost that doesn’t show up in the acquisition price.

AI-native teams are fast because they operate with a different set of defaults: full access to tools, minimal governance layers, the ability to experiment and ship without a six-week approval cycle. That operating model is not a perk; it’s the fundamental mechanism. It’s why a team of 10 can do what an enterprise team of 100 can’t.

When you acqui-hire that team and then slot them into your existing approval processes, you’ve bought the people and killed the engine. The change agents you paid for become change-frustrated. The attrition that follows is expensive and predictable.

The harder realization: acquiring an AI-native team means accepting how they work. That requires deliberately carving out space for them to operate differently, not just tolerating it but institutionalizing it. The acquisition is an organizational change program, not just a hiring event.

The CIO’s real problem

The governance stack most enterprise organizations run was designed for a headcount world. Every tool vetting cycle, every vendor review, every security approval was calibrated assuming you were managing a large team where consistency and control were the primary objectives.

That calculus breaks when your goal is talent density. The same approval processes that protect against data leaks are now the reason your best people can’t do their best work. When it takes six weeks to approve a tool that your competitor’s team is already shipping with, you’ve traded velocity for the perception of safety.

The practical fix is structured experimentation: clear guardrails, defined boundaries, but explicit permission to try tools before deciding whether to roll them out broadly. Gating everything prevents you from ever discovering what 10x productivity looks like.

The skills inventory question is also more nuanced than it sounds. Job titles won’t tell you where the leverage is. You need to map the actual tasks within each function and assess which can be automated or augmented with AI. That’s where you find the people who, with the right tools, become your $1M/employee talent, not because you hired differently, but because you enabled better.

This is also where the build-versus-buy question gets genuinely tricky. As AI reshapes how products are built and delivered, your internal operating model — how you work, how fast you ship, how you use data — is becoming core IP. Outsourcing delivery means outsourcing the part of the organization where your competitive advantage is now being built.

Closing the gap without slowing down

The AI talent wars are not primarily a recruiting problem. They’re a rethinking of what organizations are supposed to look like.

Boards have a new benchmark. Cloudflare, Block, Amazon, Meta and others have already started restructuring to meet it, publicly, painfully, even while beating their numbers. The question for CIOs isn’t whether this pressure arrives; it’s whether you’re ahead of it or behind it when it does.

The organizations that navigate this well won’t win by outbidding competitors for a handful of elite engineers. They’ll win by designing operating systems that amplify the leverage of the talent they do have, by enabling their best people rather than constraining them, and by treating AI fluency as a core organizational capability rather than a niche specialization.

Talent density is the new headcount model. The sooner your governance, your tooling and your board conversations reflect that, the better positioned you’ll be when the next efficiency report lands.

This article is published as part of the Foundry Expert Contributor Network.
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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.

Your CFO has a simple question. “We’re spending more on AI. What are we getting for it?” Most CIOs cannot answer it — not because AI isn’t creating value, but because the accounting systems we inherited were built before AI existed as a category of labor.

This June, the conversation shifted from token maxing to token cutting. The New York Times reported that Meta, Uber, Walmart and Amazon are capping employee AI usage. Uber blew through its 2026 AI budget in four months. Satya Nadella started framing it as human capital versus token capital.

All of that is true. None of it answers the CFO. Capping tokens is an input lever, not an output measure. And the human-versus-token framing names two sources of labor when the reality is four.

The enterprise now has 4 sources of labor

There are humans. There are humans assisted by AI. Humans are working alongside AI. And humans are managing AI. Sources two through four are all supervised machine labor at different intensities — none of them have a line item, a manager or an hourly rate. In our 2026 AI Labor Report, 78% of leaders view AI as both software and a labor force. The org chart has not caught up. Neither has the P&L.

Most enterprises are stuck at A-Level 1 with no accounting for any of it, while quietly sliding into A-Level 2. The job descriptions have not caught up. The budget has not caught up. You cannot upskill into a role that has not been named.

AI is the only category of work the modern enterprise has ever bought without a system of record for what it produced.

What you are actually running is supervised machine labor

The model does a first pass. A human makes it usable. One hundred percent of leaders we surveyed said AI work requires human review before it ships; 34% said substantial editing. That is a workforce with no manager, no hourly rate and no line on the income statement.

The accounting breaks in 3 places at once

Under GAAP: COGS if it helps produce the product, OpEx if it does work for you. The same workflow can hit all three buckets at once. A tier-one support resolution involves the human’s salary (OpEx), the AI’s tokens (COGS if support is a delivered service), and the supervisor’s review time (OpEx). Three buckets. One piece of work. No reconciliation. The token invoice arrives from Anthropic or OpenAI and gets coded to OpEx-software because that is what the bill looks like. Audit partners will be asking about this by next year.

When you call AI a tool, you book it like software. When you call it labor, you have to ask which kind and what it is producing.

The per-employee number is the wrong unit

Per-employee AI spend collapses a workforce into a per-head average. It hides the only number that matters: What AI is producing inside each workflow.

Lanai measured two teams inside the same finance organization. Same monthly prep and variance analysis. AI took the same amount of time to produce outputs of similar quality. The only variable was the model each team reached for by default — a choice nobody had made deliberately and nobody had seen until it was measured.

The gap existed for months before anyone saw it.

Faith-based budgeting — the organizational equivalent of putting money in the collection plate and hoping God handles the ROI — is what made it invisible.

AI labor orphaning

That is not a measurement problem. It is a category error. We call it AI Labor Orphaning. AI does the work. The output gets credited to the human who approved it. The token bill lands in OpEx-software. The supervision time absorbs into salaried hours nobody is auditing. Eighty-seven percent of leaders admitted AI output is sometimes or always credited entirely to the human employee. This is the last-click attribution problem of the AI era, running in reverse.

What fills the vacuum? Belief. Forty-three percent assume that if AI was involved, it contributed. Only twelve percent have a clear methodology. Seventy-nine percent are worried AI budgets will be cut because they cannot connect spend to results. The cuts are not coming because AI does not work. They are coming because nobody can prove that it did.

Capping tokens may look like responsible governance, but it is like turning off a staticky radio rather than tuning the dial. The companies cutting AI budgets in 2026 will discover in 2027 that they cut the workflows that worked alongside the ones that did not.

The real cost of AI is not the model. It is the redesign

Three layers. Most organizations only manage the first.

What to actually do

The 12% of organizations that can answer the CFO treat AI like every other category of labor — with a cost per AI Work Hour that is accounted for by a set of AI assistants, co-pilots and agents that are held accountable to performance standards. 

• Audit the four sources separately. Each A-Level has different token economics, SaaS implications and human redesign requirements.
• Find the embedded SaaS repricing before your next renewal. Pull your top 20 contracts. Ask whether AI features previously included are now priced incrementally.
• Redesign the human role at A-Level 2 before you scale it. You cannot upskill into a role that has not been named.
• Build a system of record before you build the next agent. Start with one department. Two weeks. You will find something that surprises you.
• Stop calling it a tool. Start calling it labor. The language determines the chart of accounts.

When your blended AI rate is $22 an hour, the conversation shifts from ‘we spent $340,000 on AI’ to ‘we acquired a skilled workforce at $22 an hour.’ That sentence is defensible. A vendor invoice is not.

The CIOs who will have a defensible AI story in 2027 are the ones who renamed the work in 2026. Not because technology changed. Because they finally built the accounting to see it.

Findings are drawn from the 2026 AI Labor Report, fielded by Wakefield Research with 200 senior technology leaders at US enterprises of 1,000-plus employees, March 20–April 8, 2026 (±6.9pp at 95% confidence).

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HireQuotient, an AI-native recruiting platform, today announced its integration with Paylocity (Nasdaq: PCTY), bringing AI-powered candidate sourcing and screening capabilities to Paylocity customers in manufacturing, building services, construction, healthcare and insurance, which are industries where deskless and frontline hiring has historically been underserved by AI recruiting tools.

Employers in these sectors who already use HireQuotient are seeing the impact firsthand.

“By simply putting in vetting criteria, I was able to get a very specific talent pool within a day,” said Niki Simoneaux, COO of Arc Health. “With EasySource, we can reach a huge database or filter for specific licenses and areas.”

“With technology and the ability to work remotely, we’re able to recruit nationwide outside of our footprint. This expands the pool of prospective candidates and gives us access to candidates who might not have ever applied for a position on our website’s career page,” said Jeff McGee, vice president at W3 Insurance.

“HireQuotient’s AI-native product helped my team at Alliance Building Services to cut down time to close position by more than 60%, and their team works very closely with the client to ensure adoption at scale,” said Willow Marcon, senior vice president at Alliance Building Services.

Recruiters in manufacturing, building services, construction, healthcare and insurance often spend more than two-thirds of their time on manual work and using over 10 platforms to just close a hire: sourcing a wide range of profiles, skimming resumes, finding contact information, reaching out and doing hundreds of calls and constant follow-ups. That fragmented process leads to recruiter burnout and leaves gaps in candidate data. Because most HR platforms don’t offer built-in AI native agents that work in tandem to do all it takes to close the hire, employers in these industries have faced hiring delays and platform churn.

The partnership addresses this gap by integrating HireQuotient’s EasySource platform directly into the Paylocity ecosystem. Instead of relying on rigid keyword searches, EasySource identifies strong talent pools, screens for role-specific credentials and licenses and personalizes outreach by phone and email. For mid-market companies with 500 to 1,000 employees, that translates to a 70% faster time-to-hire and an estimated $100,000 in annual savings.

The integration also closes the feedback loop for employers. By feeding post-hire data back into the recruiting system, EasySource learns from successful hires to build smarter talent pools over time, while keeping recruiters engaged longer by freeing them to focus on the human side of hiring.

Gokul Rajaram, board member at Coinbase and former board member of The Trade Desk (Nasdaq: TTD), and also known as the godfather of Google AdSense, said, “Smarthveer is one of those rare founders who picks an unglamorous, deeply underserved market and refuses to leave until it’s fixed. Frontline hiring is exactly that market. Being named to Paylocity’s elite partner network is a testament to his relentlessness and to the team he’s built. Excited for what’s ahead.”

“Paylocity maintains an elite partner network, so being named one of them is a testament to the strength of our product,” said Smarthveer Sidana, founder and CEO of HireQuotient. “By keeping that recruiting activity inside the Paylocity ecosystem, we’re helping them close a critical gap for their employers, capture revenue that previously sat outside their platform, and prevent the client churn that comes with a fragmented hiring process.”

Jim Moffatt, former global CEO of Deloitte Consulting and board partner at Greycroft VC, said, 

“This is a big milestone for Smarthveer and his team. I congratulate them on this big win. Paylocity’s large client base acts as a strong distribution, and HireQuotient’s EasySource acts as a strong product to cater to the needs of Paylocity’s clients. Paylocity is known for its highly selective approach, and I’m glad to see that after months of evaluation and extensive due diligence, they’re going live with HireQuotient’s EasySource. I feel confident in the value this partnership will create for frontline industries. I remember when Smarthveer spoke to me about it in December, and it felt very ambitious. I’m glad to see it turn to reality.”

For employers with a traditionally deskless workforce in industries where AI adoption has lagged, the integration bridges a major capability gap by allowing them to source, screen, onboard and manage payroll all in one place.

For more information, visit www.hirequotient.com.

A media kit with additional partner quotes, executive headshots and logos can be found here.

About HireQuotient

HireQuotient is an AI-native recruiting platform that automates candidate sourcing and screening for employers in manufacturing, building services, construction, healthcare, insurance and other frontline-heavy industries. Its flagship platform, EasySource, is one of a select number of recruiting technologies integrated with Paylocity’s HR and payroll ecosystem. Founded by Smarthveer Sidana, HireQuotient is headquartered in San Francisco. For more information, visit https://www.hirequotient.com/.

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Bethany Rhodes

Uproar by Moburst for HireQuotient

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Bethany Rhodes

bethany@moburst.com

AI-powered professional services automation provider Certinia has acquired Moonnox, an AI-native automation platform created for the sector. It extends Certinia’s system of action, Veda, offering a new suite of AI agents that automate administration, project management and project deliverables, the company said.

The acquisition will add real-time native context capture across applications such as Salesforce, G-Suite, Microsoft 365, Jira, Confluence, Zoom and others, and, unlike point agents, it “spans the full arc from proposal to delivery to renewal, so nothing has to be rebuilt, re-mapped, or re-trusted as work moves from sales to delivery to customer success,” Certinia said.

New capabilities in Veda include automatic creation of proposal responses and statements of work, conversion of high-level business requirements into actionable blueprints, providing an on-demand virtual assistant to manage day-to-day tasks, performing scope scans and otherwise monitoring projects to proactively manage risk, and organizing delivery data and lessons learned into a searchable knowledge base for later use.

“What excites me most is the combination,” Robert Ong, co-founder and CEO of Moonnox, now part of Certinia, said in a statement. “Moonnox’s ability to capture what happens in the room, paired with Certinia’s system of record and agentic capabilities for services operations, closes a gap neither of us could close alone. Together, we give services firms the foundation to capture that value, deliver with confidence, and navigate shifting their operating models into the future.”

Thomas Randall, research director at Info-Tech Research Group, said it’s a smart move for Certinia to acquire Moonnox. “The current solution is quite complex to use. What Moonnox offers for Certinia is a way for non-technical staff to navigate their system of record across unstructured data silos. I expect to see an increase in satisfaction for the user experience.”

However, Jason Andersen, VP and principal analyst at Moor Insights & Strategy, still has questions.

“While this acquisition might increase service planning and delivery efficiencies in the short term, the real question will be how this combination will help firms with the fundamental shift AI and agents have thrust upon the services industry,” he pointed out. ”Unfortunately for services firms, agentic technologies have already reset client perceptions on internal work capacity, velocity, and cost. So, the proof point I’d like to see is beyond margin improvement and towards business transformation.”

Major AI labs are unleashing forward-deployed engineers (FDEs) to try and grab enterprise customers. Large consultancies are dishing out tokens and assembling armies of consultants — both human and agent — to do the same.

But smaller firms are in the mix now, as well. AI is helping 28Stone Consulting, a New York-based, 230-person technology consultancy for capital markets, punch above its weight against larger rivals in the rush to deliver FDEs.

In this Q&A, Thomas Dolan and Frank Erickson, founders of 28Stone, argue that agentic AI isn’t a one-size-fits-all solution in vertical markets; success takes discipline, deep domain expertise, and human involvement to mitigate risk.

Many enterprises continue to struggle with the use of AI agents, which is consultancies are stepping in to get projects off the ground. 28Stone is among those that have published blueprints and methodologies on the development and delivery of agentic AI workflows with humans in the loop.

Computerworld spoke with both founding partners about why companies are still stumbling with agentic AI rollouts, and what a disciplined delivery process actually looks like.

After 15 years of delivering software for capital markets firms, is ‘AI-first’ a real distinction or just positioning?

Dolan: “We’re not shying away from being AI-forward. What needs to shine through is AI done intelligently — not stuff you get by buying some tokens for somebody on the trading desk. We’re an AI-first firm.”

Erickson: “And it’s temporary. At some point, AI is going to be synonymous with software development.

“The whole idea of an AI SDLC (software development lifecycle) versus an SDLC is going to be one and the same, a lot like cloud computing today. To not include AI in your strategy, you’d look like a COBOL vendor.”

What does agentic AI delivery look like?

Dolan: “We’ve got several AI initiatives delivering a pure agentic approach. We’ve doubled down on the human expertise wrapper in the SDLC. That doesn’t mean sacrificing any of the benefits of the AI models — quite the opposite.

“You don’t achieve anywhere near the same level of value from applying AI without keeping that expertise — industry, functional and technical — throughout the process.”

Where do humans stay in the loop once agents are doing the work?

Dolan: “We’re believers in starting with requirements discovery. Someone who knows the analytical nuances of a good business analyst is critically important; shaping a product owner’s business information through a markup file that can be fed into a BA agent, then treating the output as if it came from a very fast junior BA. Only then is the story complete.

“The developer takes that story, transforms it into the most efficient input, then owns the output, because they’re accountable for that code. A developer should own the code on both the input and output side.

“Your product owner, who knows the business, that’s great. But expecting them to interact with an agent and output enterprise code is ridiculous. It’s not a great plan.“

Why not just put one do-everything person in charge of AI and agents?

Erickson: “Every analyst, programmer or software engineer isn’t a great requirements analyst. And a great domain analyst with some technical background won’t know if the agent’s code is garbage, maintainable, performant.

“It’s unrealistic to expect one individual to have that breadth across domain, software engineering, testing, deployment. Clients ask all the time, and we push back: ‘Great, if you can find that guy, they’re few and far between.’ To deliver at the enterprise level, you need the human expertise, at depth.“

Dolan: “There’s system speed and latency, important in parts of finance. Then there’s speed of delivery, because other areas evolve quickly and time-to-market is critical.

“Our human wrapper may at first pass come across as a little slowed down. Maybe it is. But [Erickson] has a good analogy about one of the dangers of AI: you can end up going really fast in the wrong direction. By the time you look up, you’re way off base and have to backtrack.“

What about AI in your sector do you think is overhyped?

Dolan: “The hype around the ease of use of AI and the democratization of enterprise software delivery — that ‘anybody could do it now, it’s all being done by machines’ — is another idea that could prove costly in the long run.

“This do-it-yourself reaction is dangerous for clients, and for trust in the overall AI benefit, which is real. We compare it to the beginning of offshoring 20, 30 years ago: a golden idea that was going to cure everything. A lot of firms did it thoughtlessly, thinking it’s just labor arbitrage, and it almost inevitably failed. That all-or-nothing mentality missed that offshoring is an amazing way of getting better value for your dollar, but it has to be done thoughtfully, so the delivery process — the thing that ties it all together — stays unsevered.

“We’re seeing that now. I’ve heard, ‘We’ll just push a button, the machine’s building the system.’ The machine is not building the system. It might be writing the code, the story, running the tests.

The system is built by a team of engineers you bring in and trust. My fear is that people will say, ‘We don’t need this vendor or this technology team. I’ve got a product team. They might not be able to code at all, but they know the business,’ and it fails dramatically. 

“Then people say, ‘We played with AI, it’s not ready yet,’ and throw it all away. One of the best things we can do is ensure clients know the benefit is real.“

Erickson: “The hype can be summed up in a single phrase: vibe coding. That has done AI a massive disservice, because there’s a huge difference between vibe coding and enterprise software development, and some of the loudest proponents of AI are too latched on to it. In our industry, the only way to succeed would be a stable of unicorns. It just doesn’t scale. I get perturbed when our people internally refer to AI tooling as vibe coding; if they think that’s what they’re doing, they’re misunderstood.“

When you engage clients at different levels of AI maturity, how do you get them to a understand what works?

Dolan: “95% of our take on an agentic approach is in line with everyone else’s, but that 5% matters, especially in requirements discovery, in who’s giving the requirements and how they’re thought of. It can set you up for dramatic errors, given the speed at which you’re moving.

“There’s a dangerous human tendency we’re seeing among clients to try and cut corners at the start of a project and — in lieu of having deep, expert driven discovery sessions — just summarize what they may want using AI.

“We would hope our clients are collaborative, everyone understanding it’s early days. If a client insists on doing something we feel strongly against, like a product owner completely owning everything right up to code generation, that’s an issue we have to either push back strongly on or step out of the accountability for.“

AI body shops — LLM providers and giant consultancies — are emerging to help enterprises deploy AI. Does that model work?

Dolan: “Whether you’re partnering with an LLM or with an AI-first, generic software provider — ‘Hey, we’re not industry guys, but we know AI delivery’ — you end up, if you’re a bank or a broker-dealer, saying: ‘All right, we know our business, these guys know the AI side of it. What could go wrong? Put us together and we’ll have quality engineering.’

“The problem is what you miss: the know-how of putting industry and technical expertise together and actually delivering financial services systems. The people working at the generic delivery firms, whether an AI-only firm or a body shop somewhere, don’t have that capability.“

Does AI change the economics for smaller consultancies like yours competing against the big firms, and does it cut both ways?

Dolan: “Over our 15 years pre-AI, there were two recurring reasons we’d lose a project. One: ‘We’d love to work with you guys, given your subject matter expertise, but the costs just aren’t there compared to my budgets. I’m being forced to go to a body shop or an [offshore] delivery center.’ The other side of that coin: ‘We love your capabilities, but you’re a firm of 230 people and I need 300, 400 people.’

“AI changes the options for clients. You don’t have to sacrifice the niche vendor who knows your space just because you need a larger team or a cost target. AI levels the playing field and should allow smaller firms to compete with the larger, big-box generic firms, the Accentures of the world.“

Erickson: “It redefines what scale means. You can look at velocity as a measure of your cost to deliver, not a rate card. Scale can’t be defined in terms of headcount anymore. It’s got to be defined in terms of output.

“There’s a threat in it, too. If you’re an Accenture with hundreds of thousands of low-cost software engineers, how do you train all those people? I feel for them. But for us, a couple hundred people with a specific domain focus, it’s a huge opportunity.“

How has the profile of the people you and others hire changed with this agentic process?

Erickson: “You’re still looking for people with strong engineering and design backgrounds, and communication skills, because they interact across the software development lifecycle more than in the past.

“Many take too much joy in typing out perfect code. Sorry, I don’t need you writing for-loops and classes anymore. I need you reviewing them, understanding them, operating at a higher level. That’s a different kind of person: an engineer, not a programmer or a coder. On the [business analyst] side it’s similar: people took great pride in detailed user stories covering every path. Now it’s conversations, prompts, reviewing output — less doing, more interacting.

“More than ever, they have to be interested in the domain. They can’t just be, ‘I want to learn everything there is to know about Java.’ That’s too narrow. They don’t have to be an expert; they have to be interested. In our case, capital markets is a specific niche. The biggest challenge is getting familiar with the tools — finding time, while delivering for customers, to ramp up and make the mistakes you need to without jeopardizing projects.“

What about governance? Who’s keeping AI delivery and its costs under control?

Erickson: “This is evolving rapidly. People aren’t sure how to put governance around this. The most obvious is financial governance. People are starting to get hefty bills. One of our clients spent a million dollars on tokens over the last eight weeks alone. Sticker shock. The token-maxing policies are starting to show their flaws. It’s wild west still: learn on the fly, then figure out what needs to be governed.“

Are CIOs actually opening their wallets? And when they do, what’s the smarter way to invest?

Erickson: “There’s still a lot of caution. Forecasts keep going down on how long something should take. So: ‘I could wait three months and maybe still get it delivered by the same date someone’s promising me now, but for half the price. I’m going to wait and see when equilibrium is met.’ We haven’t seen the wallets open up like crazy — it’s slow adoption.“

Dolan: “One of our clients is looking at it from a productivity-boost perspective: instead of doing the same for less, I can do much more for the same. AI lets clients pull the trigger on things they wouldn’t have in the past — projects that might not have been approved pre-AI, where the costs have come down to a point that’s palatable with the business.“

Erickson: “And that’s the story we’re hoping to hear more of. There isn’t a huge cost anymore to exploring a business opportunity. The time and money that would have gone to a return-on-investment study could be spent on a proof-of-concept with AI, and the project done a few weeks later. Maybe [there’s] a hint of things to come, where decisions start being made quicker. 

“There’s a little fear on our side, though: a lot of tiny little projects is tough for a consulting business.“

For diversified enterprises, few operating model questions are as persistent or polarizing as centralization versus decentralization. Decentralization promises speed, ownership, and local responsiveness. Centralization promises efficiency, standardization, and leverage. Both can be right. Both can be wrong. The challenge is that many organizations end up with both models operating at once, without enough clarity about why.

The result of fragmented systems, duplicated capabilities, inconsistent data, rising IT spend, and a complexity tax that compounds over time is familiar to many CIOs. What starts as autonomy can become architectural sprawl. What starts as enterprise leverage can become bureaucracy. And as companies modernize core platforms, integrate data, and scale capabilities like AI, the tension becomes harder to ignore.

Paul Krebs has lived that tension from multiple vantage points. Most recently as CIO and chief transformation officer at Koch Industries, and previously a technology and transformation leader at The Coca-Cola Company, he’s worked in environments where business units value autonomy, enterprise scale matters, and the wrong operating model can slow progress just as easily as the wrong technology architecture.

His conclusion isn’t that CIOs should pick a side, but they need a more intentional form of centralization, one that starts with business architecture, clarifies decision rights, and continually revisits where capabilities should sit as the organization matures.

Centralization: a design choice, not a doctrine

In diversified organizations, decentralization often starts as the default because it aligns with how the business creates value. Local businesses understand their customers, markets, regulatory environments, and operating realities, and giving them decision rights can increase speed and accountability.

In Krebs’ experience, the default model often leaned toward decentralization, he says, with the belief that optimizing for customers and markets would allow different businesses to be as responsive as possible to the specific customers and markets they served. But that logic isn’t complete. Leaders also need to ask whether there’s a compelling case where a more centralized approach can generate additional value, accelerate progress, or optimize investments.

Digital transformation created one of those moments. Krebs recalls around 2016 when Koch challenged its businesses to build multi-year digital transformation roadmaps. The ambition was there, but the capabilities to execute at the necessary pace weren’t evenly distributed. In response, the organization invested more aggressively from the center, building shared services and centers of expertise in areas such as business transformation, enterprise applications, and data and analytics.

The purpose was acceleration, not control. Centralizing those capabilities helped accelerate learnings, capability building, and their ability to deploy new solutions at scale. But the move wasn’t treated as permanent. “There was always a belief that the centralization push should be re-looked at on a regular basis, not thought of as a forever decision,” he says.

Know what belongs at the center

Over time, Krebs learned that  the capabilities most likely to remain centralized were those where scale, consistency, and risk management mattered more than local differentiation. Infrastructure, collaboration platforms, cybersecurity, cloud management, FinOps, and the help desk were natural candidates to remain shared services.

Other areas were more nuanced. Some application capabilities moved back into the businesses as local maturity increased. Many data and insights capabilities also moved closer to the business once teams had built enough muscle to own them. Meanwhile, certain emerging capabilities such as spatial technologies like AR/VR remained centralized because it didn’t yet make sense for each business to build them independently. Many companies have lived this journey as well, for example, with gen AI, which often started with a center of excellence, and then evolved into a more decentralized approach, enabling teams across the business to innovate quickly.

That distinction avoids the trap of treating the enterprise as one uniform operating model. “Both models can be successful, and both have advantages,” he says. “That’s what makes the balance so difficult.”

Centralization provides a clearer path to execution at scale and cleaner decision rights, but it requires change management and careful attention to bureaucracy. Decentralization provides ownership and speed, but it can also over index toward preference versus real differentiation, he adds, while making architecture harder to scale later.

Don’t confuse standardization with centralization

One of the most important distinctions Krebs makes is between centralization and standardization. Many organizations treat them as interchangeable, but they’re not.

“You can have a centralized team that can manage the nuances of different requirements,” Krebs says. “You can also have a centralized standard platform that can be used in a decentralized manner.”

That distinction opens up more operating model choices. A company may centralize a platform but decentralize how business teams configure or use it. It may standardize process patterns while keeping execution close to the region or business unit. It may also centralize architectural governance while allowing local teams to move quickly within defined guardrails.

This is especially important in global organizations, where regional needs are real but not always unique. Krebs advises leaders to examine whether local requirements can be made more generic and reusable. The risk is solving each local requirement as a one-off, so the better path is to understand the underlying requirement, build it in a way that can scale, and still allow local teams to execute within the standard model.

Let business architecture lead technology architecture

Few topics expose the centralization tension more clearly than ERP consolidation. Many diversified companies, particularly those shaped by acquisition, end up with dozens or hundreds of ERP instances. Some leaders push for massive consolidation. Others prefer to build integration layers on top of the existing environment.

Krebs’s starting point is neither technology nor cost. It’s business architecture. “The easiest and most effective path is when the IT or systems architecture follows and aligns to the business architecture,” he says.

If the business is truly going to operate processes separately, separate systems may be appropriate. But if the organization has numerous teams, processes, and tools, leaders need to ask whether there’s enough differentiation and value to justify that complexity.

The same logic applies to M&A. Companies can get into trouble when integration synergies are held hostage by ERP migration timelines. Instead, Krebs advises starting with the business integration strategy. Understand where the synergies are, how the business architecture should come together, and then decide whether the IT architecture needs to be fully integrated, or whether a data layer, reporting platform, or other integration approach can deliver value faster.

Make the cost of complexity visible

CIOs in decentralized companies often face a frustrating dynamic. The business wants autonomy and speed, but the same leadership team still questions why IT spend is high relative to benchmarks. Krebs says the answer starts with cost alignment and visibility.

In environments with a mix of centralized and decentralized services, Krebs saw centralized capabilities like infrastructure, help desk, and security perform well on benchmarks. More decentralized areas, such as BI, reporting, and commercial applications, often had more redundancy and higher cost.

The point isn’t to blame the business but make the economics of complexity visible. CIOs need to show how flexibility in one area may require multiple systems, data stores, or teams elsewhere. “I understand we want flexibility here,” Krebs says. “But leaders must see when that flexibility may cost the company money, and be clear on whether the value justifies it.”

That shifts the conversation from IT cost to business service economics. A single aggregate IT spend number is rarely useful in a decentralized environment. More helpful is a capability-based view that shows which areas are scaled efficiently, which are fragmented, and where the business architecture is driving the technology cost structure.

Revisit the model as maturity changes

For a new CIO entering a decentralized environment, Krebs cautions against immediately declaring that too many things need to be centralized. The better starting point is curiosity. “I would begin with just trying to understand why they’ve made the decisions they have,” he says.

From there, CIOs can engage leaders in a conversation about the target operating model, connecting business architecture to technology, data, and organizational capabilities. Once the direction is clear, he advises CIOs to work with the willing. Find the parts of the organization that already see the need for change, prove the model there, and scale from demonstrated success.

Regardless of execution, though, the right model changes over time. A low-maturity capability may benefit from centralization because the organization needs to build talent, avoid reinventing the wheel, and accelerate learning. As maturity grows, decentralization may make more sense because business teams need flexibility to adapt quickly. Once maturity is high and patterns stabilize, the organization may be ready to centralize again to leverage scale.

“Once I’ve decided I’m going to start with centralized or decentralized, you don’t necessarily need to stay in that model,” Krebs says. “You need to be continually revisiting the operating model as your organization matures and evolves.”

That may be the heart of smart centralization. It rejects the false permanence of operating model decisions, and recognizes that autonomy and scale are both valuable, but in different places, at different times, for different reasons.

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.

I have sat in a lot of ERP training sessions over the years. Some were excellent. Most were not. And the ones that failed share a pattern I have come to recognize almost immediately: a vendor trainer at the front of the room, working through the same slide deck they use for every client, at the same pace, with the same examples, regardless of who is sitting in the chairs.

In one room, you might have a warehouse supervisor who has never used enterprise software, a finance manager with 20 years of system experience and a department coordinator somewhere in between. The vendor trainer covers the same material with all of them. Everyone gets a certificate at the end. Almost nobody is prepared to do their job in the new system when go-live arrives.

I want to be clear about something before I go further. In my previous CIO article, I wrote about why organizations should stop blaming their ERP vendor when implementations fail. That argument still stands. But the training problem is a choice the organization makes. Most organizations spend less than 5% of their total ERP project budget on training and then hand that underfunded responsibility to the vendor. The vendor delivers what they were contracted to deliver. The gap between what got delivered and what the organization actually needed is not the vendor’s fault. It is a decision the organization made, often without fully understanding its consequences.

After 25 years of leading enterprise software implementations and based on the doctoral research I conducted studying ERP implementations in small businesses, I am convinced that the organizations that get training right share one thing in common: they build the expertise internally rather than importing it.

Why vendor training falls short

Vendor trainers know the software. That is not in question. What they do not know is your business: your processes, your workflows, your data, your terminology, your culture and the specific ways your organization will use the system once it is live.

That gap matters more than most organizations realize. Research consistently shows that inadequate training is one of the primary drivers of ERP implementation failure, not because training did not happen, but because the training that happened did not connect the system to the work. Employees left those sessions knowing what buttons to click without understanding why those buttons mattered to their specific job.

Generic training has a structural problem: it is optimized for coverage, not relevance. The goal is to ensure every employee has seen every feature. The result is that employees spend significant time learning functionality that does not apply to their role, while the functionality that does apply gets the same shallow treatment as everything else.

A finance manager sitting through a session on shop floor production tracking is not learning anything she will use. A warehouse supervisor learning about financial journal entries is in the same position. Both leave the session technically trained. Neither leaves prepared.

There is also a timing problem. Vendor training typically happens in a compressed window before go-live, delivered as a series of sessions rather than a progression. Research on learning retention suggests that training delivered weeks before it is needed, without reinforcement or practice, is largely forgotten by the time employees need to apply it. The result is a go-live day where everyone attended training and almost nobody feels ready.

What internal expertise looks like in practice

In my doctoral research, I interviewed six IT managers from small businesses who had each led successful ERP implementations. Five of the six identified role-based, department-specific training as essential to their outcome. What distinguished their approach was not that they spent more on training. It was that they built the training capability inside the organization rather than contracting it out.

The approach that worked most consistently was identifying one person from each affected department early in the implementation, before configuration even began. That person became the departmental expert: involved in design decisions, consulted on how their team’s processes mapped to the new system and ultimately responsible for either delivering training to their colleagues or co-leading it alongside a formal trainer.

This is sometimes called a super user model, and the research supports its effectiveness. But what I observed in the implementations that worked goes beyond the mechanics of the model. The departmental expert brought something a vendor trainer cannot: credibility. When the warehouse supervisor learns the receiving process from someone who has worked in that warehouse, who understands the exceptions and the edge cases and the way things truly flow on a busy day, the training resonates in a way that a generic session never can.

There is also an ownership dimension that is easy to underestimate. Peer-based training led by internal super users helps employees connect system steps to their daily responsibilities in ways that outsider-led training rarely achieves. The departmental expert has skin in the game. They are going to use this system too. That shared stake changes the dynamic in the training room and sustains the support relationship long after the formal training is over.

I have seen this play out in both directions. In implementations where the organization invested in building internal expertise early, go-live day was hard but manageable. Questions went to the departmental expert, who could answer them in the language of the department. Issues surfaced quickly because someone in each area was watching for them. Adoption stabilized faster because the support was embedded in the team rather than accessible only through a help desk ticket.

In implementations where training was handed entirely to the vendor, the pattern was different. Go-live revealed gaps that training had not covered. The vendor’s support engagement was winding down. The organization had no internal expertise to draw on. Employees reverted to workarounds. The system went live but never fully took hold.

How to build internal training capability before go-live

The organizations that got this right did not wait until the training phase to think about training. They started building internal expertise at the beginning of the project. Here is what that looked like in practice.

Identify departmental experts early

Select one person from each affected department before configuration begins. Choose people who are respected by their colleagues, have a solid understanding of their department’s processes and are willing to invest extra time in the project. This is not a small ask. Make sure they and their managers understand the commitment and that the contribution is recognized.

Involve them in the implementation, not just the training

The departmental expert should participate in process design sessions, configuration reviews and user acceptance testing. By the time training begins, they should understand the system deeply enough to explain not just how it works but why specific decisions were made. That context is what makes internal training credible.

Design training around the job, not the system

Training content should be organized around realistic work scenarios specific to each department, not around the system’s feature set. The finance team trains on how to process their transactions in the new system. The warehouse team trains on how to manage their receipts and inventory. Connect every step to the work employees actually do.

Plan for post-go-live support, not just pre-go-live training

The weeks immediately after go-live are when training becomes real and when the gaps in pre-go-live preparation surface. The departmental expert should have a defined support role in that period: available to their colleagues, connected to the project team and empowered to escalate issues that need resolution. This is not a minor detail. It is where the investment in internal expertise pays its most important dividends.

None of this requires a large budget or a dedicated training function. It requires early decisions about who will own the training relationship inside each department and the organizational commitment to support those people through the implementation, rather than treating training as a final phase activity.

The vendor knows the software. That knowledge is valuable and should not be wasted. But your people know your business, your processes and the way work flows on any given day. The question is not whether to use your vendor’s expertise. It is whether you are also building the internal expertise that turns a trained workforce into a prepared one. The organization that does both will not just go live. It will thrive.

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A few years ago, I was retained to conduct a digital risk review for the chief executive of a mid-sized financial services firm. The brief was standard. Assess what was publicly available about the executive, identify exposure and advise on remediation. The AI tools I used completed the substantive reconnaissance in under ten minutes.

What came back was a synthesized profile. Board memberships and the dates they started. A pattern of public commentary that revealed which policy positions the executive held strongly and which ones he would likely bend on under pressure. A philanthropic interest that explained which causes he would respond to if someone framed an ask around them. None of this information was sensitive in isolation. But assembled into a single, queryable narrative, it was something an attacker could use immediately.

What I was looking at was a publicly accessible query to a general-purpose AI tool. And that is the problem most executive protection programs have not yet confronted. The reconnaissance phase for a targeted social engineering attack now takes minutes, not days, and the inputs required are trivial.

AI-aggregated executive data has become an attack surface. Most security programs have not yet adapted to it.

The reconnaissance phase has effectively collapsed

Traditional OSINT work against an executive target required skill and patience. A competent analyst could build a useful profile over several days by working through search engines, corporate filings, social platforms and archived media. That work was a meaningful barrier. It took time and it required judgment about which sources to trust. It also left trails if the attacker was careless.

AI aggregation removes all three constraints.

The speed advantage is obvious but it is not the most important change. The more significant shift is synthesis. A search engine returns documents. An AI tool returns a coherent narrative with inferred relationships and interpreted significance. When I query a major AI platform for a senior executive by name, I get a structured account of their career arc, their professional relationships, their areas of visible influence and frequently their personal interests, relationships and public-facing affiliations.

The MGM Resorts incident reported in 2023 illustrated the principle at scale. Attackers reportedly identified an MGM executive on LinkedIn, used that public profile information to impersonate them in a call to the IT help desk and obtained access credentials within minutes. The OSINT required was minimal and the manipulation was straightforward. What AI tools have done since is make that kind of reconnaissance faster, more complete and available to actors who lack the manual tradecraft to run it themselves.

As the Verizon Data Breach Investigations Report consistently documents, the human element is present in the majority of confirmed breaches, and social engineering remains one of the most reliable initial access vectors.

The accessible nature of AI tools is also expanding the threat population. Attacks that previously required a skilled analyst to design now require only a motivated actor with internet access. That changes the volume and targeting calculus. Executives who were previously too obscure to justify a sophisticated manual attack are now viable targets for anyone with a grievance and a query box.

What should CIOs and CISOs do about it?

The instinct in many organizations is to route anything involving an executive’s public profile to the comms or PR function. That instinct made sense when the risk was reputational. It no longer covers the exposure.

What follows is how I advise clients to structure this work.

Monitor regularly

The starting point is establishing visibility into what AI tools are actually returning about your executive population. Not a one-time audit conducted during a board meeting and forgotten. The profiles shift continuously as new content is indexed, old content is reweighted and the models are updated.

Assign ownership to run structured queries across the major platforms, including ChatGPT, Gemini, Perplexity and the Microsoft Copilot stack, on a regular cadence. Document what you find and track changes. Treat the output the same way you would treat a vulnerability scan as something to be prioritized and acted upon.

Reduce the available attack surface

Work with each executive to identify content that expands their AI-indexed profile without serving any legitimate business purpose. This includes legacy conference bios that contain personal details, social posts that reveal schedule patterns or family context and board announcements that, in aggregate, map an executive’s full professional network. For some of this content, removal is possible and worth pursuing with a targeted effort.

The more important conversation is around future behavior. Executives who habitually overshare on LinkedIn or in conference panels need to understand, concretely, what that sharing enables.

Family member exposure is a consistent blind spot. An attacker who cannot pressure an executive directly may look for leverage through a spouse, a sibling or a child. Executives rarely consider their family members’ public digital footprint as part of their own security posture. It is.

Shape the narrative where reduction isn’t possible

Public company executives, board members with mandatory disclosure obligations and individuals whose public profiles are central to their organizations’ credibility cannot simply go dark.

The objective shifts from reduction to shaping in these cases. The goal is to ensure that what AI tools synthesize from the indexed content is professionally bound and does not inadvertently surface high-value pretext material. This is a joint exercise between security and communications, with security defining risk boundaries and communications executing the strategy.

Train executives on what their own profile looks like

The most effective single intervention I have seen in executive briefings is also the simplest. Open a browser and query an AI platform on the executive in the room. Let them see the output. The reaction is consistent. They are surprised by the synthesis, uncomfortable with specific details that surface and immediately more engaged with the rest of the conversation than they were before.

Abstract threat briefings about social engineering risks rarely land with senior leaders who feel they understand their own security position. Demonstrated evidence of their AI-mediated profile lands every time. As covered in the context of executive-targeted attacks, awareness is a prerequisite for the behavior change that makes protection programs effective.

Integrate this into the executive protection program

This work belongs alongside endpoint security, credential management and physical protection in a unified executive protection program. When it remains a communications function, it lacks the reporting structure, budget authority and operational discipline that security work requires.

Assign an owner with a security mandate. Include AI exposure in the risk register. Report on it at the same cadence as other executive protection metrics. The organizations that have done this well have not created a separate program for it. They have extended an existing one.

What effective executive protection programs now include

The organizations that have integrated AI exposure into their executive protection work share a few characteristics that distinguish them from those still treating it as a communications edge case.

• They treat the executive’s public information footprint as a managed attack surface with a named accountable party. Someone is responsible for it, the same way someone is responsible for endpoint patching or identity governance.
• They include AI-assisted reconnaissance as a starting condition in red team exercises. Before any social engineering simulation begins, the red team runs the same queries an attacker would run. The pretext they design is based on what those queries return.
• Their executive protection briefings include an AI profile review as a standing agenda point. Physical security considerations, credential exposure and public information risk are reviewed together because they are connected. An attacker who knows an executive’s schedule from their public-facing content can time a credential reset attempt or a vishing call with equal precision.

The executive I reviewed several years ago had no idea what his AI-indexed profile contained or what it enabled. Most of the executives I work with today are in the same position. By the time you finish reading this, it is likely those queries have already been run on someone in your organization. The question is whether your program is positioned to detect it and respond in time.

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Security experts are calling on enterprises to revise their vulnerability management strategies and move towards “just in time” patching in response the increased pace of vulnerability exploitation.

Attackers are turning to AI to increase the rate of vulnerability exploitation and supply chain compromise so that traditional forms of vulnerability management are no longer keeping pace.

Muhammad Yahya Patel, vCISO and cybersecurity advisor for EMEA at managed security services vendor Huntress, recently told CSO that “organizations need to shift their vulnerability management program to a risk-based, continuous [approach], tied to real-time exploitation intelligence — not scheduled patch cycles that leave exploitation windows wide open for days and weeks.”

Wild frontier

Frontier AI tools such as Claude Mythos have signaled a structural shift for cybersecurity, readily surfacing vulnerabilities at a huge scale — a development that, as government security assurance organizations such as the UK’s National Cyber Security Centre point out, is likely to lead to a surge in patches.

“Most organizations already struggle to fix known issues quickly, so a spike in AI-driven discovery could easily overwhelm teams and widen the gap between finding problems and fixing them,” Andrew Woodford, CTO at network security vendor Titania, tells CSO. “In many ways, this just exposes a problem that’s already there.”

Shane Fry, CTO at cybersecurity vendor RunSafe Security, argues that patching as a security strategy has been in crisis for years, and AI-accelerated vulnerability discovery has simply pushed it over the edge.

Some experts contend that virtual patching — a technique that involves blocking exploit attempts at a security layer rather than fixing vulnerable code — represents a sound mitigation strategy, but Fry has reservations about the approach.

“While virtual patching will play a role going forward, its effectiveness is limited and leaves security teams chasing a gap they will never be able to close,” Fry says.

Instead, security teams need to shift toward mitigation-first approaches that make it impossible for attackers to exploit bugs in software.

“Removing entire classes of exploits upfront takes the heat out of the patch gap, and allows patching to become strategic rather than reactive,” Fry argues.

‘Assume Autonomy’

The conventional patch management model was designed around a world where vulnerability discovery happened at human speed: A human researcher finds a flaw, reports it, a CVE gets assigned, vendors ship a fix, enterprises test and deploy it — a process that can take weeks.

AI-powered vulnerability discovery blows this model out of the water.

“If offensive AI can identify, validate, and exploit vulnerabilities without human authorization, a 43-day median patch time, as noted in Verizon’s DBIR, is the least of your problems,” argues Rik Ferguson, vice president of security intelligence at Forescout. “An AI system doesn’t wait for a proof-of-concept to circulate on GitHub or a CVSS score to land in a dashboard. It finds the flaw, confirms exploitability, and moves.”

Ferguson advocates a change of approach toward what he describes as “Assume Autonomy.”

“The question is what compensating controls you put in place between discovery and remediation, and how you constrain what an attacker can do with access they’ve already acquired,” Ferguson explains.

Just-in-time patching fits in with this philosophy and is a desirable goal but may be difficult to achieve in practice especially for the many enterprises that struggle with asset management.

“Just-in-time patching is sound in principle: prioritize and deploy fixes as exploitation intelligence emerges rather than waiting for the scheduled window,” Ferguson says. “But achieving it has some real-world requirements: continuous asset visibility, knowing precisely what you have, where it is, and what its current exposure status is.”

For example, Ferguson adds, “you can’t patch just-in-time against a vulnerability in a device you didn’t know was on your network.”

Virtual patching

Gunter Ollmann, CTO at pen testing as a service firm Cobalt, notes that just-in-time patching makes sense if and when a patch is available — but that’s not always possible.

“The major problem lies in the discovery of new vulnerabilities in code or systems that the business has no rights or capabilities to fix themselves, and they have a dependence upon third parties to develop the fix or patch — and are therefore subject to external SLA [service level agreement] turnarounds,” Ollmann explains.

In such cases, enterprises will need to deploy virtual patches capable of blocking or deflecting the exploitation vectors of the vulnerable system.

“Businesses are in desperate need of quickly deciphering a new vulnerability and dynamically creating an appropriate blocking rule — or rules — for their layered defenses,” Ollmann says.

Virtual patching may mitigate security threats particularly in operational technology (OT) and IoT environments where applying a vendor patch to a running production system risks unplanned downtime or safety system interruption but only serves as a stop gap, Ferguson tells CSO.

“A network-layer control that blocks exploitation of a known flaw, while you work through the testing and deployment cycle for the actual fix, is a compensating control,” notes Ferguson, who warns that virtual patches come with multiple drawbacks.

“Virtual patches require accurate detection signatures, they don’t remediate the underlying vulnerability, and they can create a false sense of closure that delays proper patching indefinitely,” Ferguson argues. “The risk is that temporary becomes permanent. The underlying vulnerability stays open, and the virtual patch becomes the reason nobody revisits it.”

Just-in-time risk reduction

Douglas McKee, director of vulnerability intelligence at Rapid7, advocates what he describes as just-in-time risk reduction rather than just-in-time patching because of the practical difficulties with the latter.

“In the real world, especially in OT, medical devices, and business-critical systems, you can’t always patch the second a CVE drops,” McKee argues. “You still need testing, maintenance windows, rollback plans, and someone who actually owns the asset. However, the old monthly scan, report, and remediation cycle will not survive this pace.”

Tips for modernizing vulnerability management

The enterprise attack surface has expanded significantly of late, and patch management models haven’t kept up. In response, security leaders’ vulnerability management strategies have to become more of a continuous monitoring function, not a triage and remediation process.

Modernizing enterprise approaches to vulnerability management involves “real-time exploitation intelligence integrated into prioritization, compensating controls deployed at discovery rather than at patch release, and visibility across the full asset estate that conventional patch management tools were never designed to cover,” Ferguson says.

Rapid7’s McKee stresses that security teams need to separate “known vulnerable” from “actually reachable and exploitable in my environment.”

This process can be achieved through a combination of asset inventory, internet exposure mapping, KEV tracking, vulnerability intelligence, ownership, and emergency change paths.

“Prioritization based on risk factors like public exposure, known exploitation, automation potential, and technical impact is key,” McKee concludes.

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.”