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.

Traditional phishing techniques are in decline as a result of the disruption of the Tycoon2FA phishing-as-a-service (PHaaS) platform, Microsoft said in a new report, “Email threat landscape: Q2 2026 trends and insights”.

“Phishing volume linked to the platform fell 92% from pre-disruption averages, including QR code phishing and CAPTCHA-gated phishing both declining from their March highs,” the company wrote in the report.

The takedown reduced activity across multiple phishing categories, forcing attackers to shift to newer delivery methods.

Riding this shift in were a few notable phishing campaigns, including an automated business email compromise (BEC) campaign that reached 42,000 organizations in under three hours, and a multi-stage phishing campaign that used nested email (EML) files, calendar invitations, and a Microsoft authentication redirect to deliver malware.

To counter phishing attacks, Microsoft recommends blocking emails containing known bad URLs/ subject fields, enabling password-less authentication methods, or moving to MFA for accounts that still require passwords.

Tycoon2FA disruption sent attackers exploring

The take-down of Tycoon2FA forced its operators to abandon portions of their infrastructure and rework hosting, domain registrations, and delivery mechanisms.

“After falling 15% in March and another 22% in April, Tycoon2FA-linked phishing volume dropped 74% in May to just 1.5 million messages, then fell another 20% in June to 1.2 million, by far the lowest monthly volumes observed in at least a year,” Microsoft said.

The decline extended to QR Code lures and fake CAPTCHA pages, two phishing techniques in which Tycoon2FA accounted for 12% and 14% of industry activity in June, respectively. This indicated that the platform’s customer base had not been able to migrate to a replacement infrastructure.

But cutting off one head of the hacker hydra only gave rise to new tactics elsewhere.

The adaptation came in the form of using Microsoft Teams as a social engineering channel. Attackers established conversations to build trust before attempting credential theft or delivering malicious payloads. “Teams-based phishing volume climbed steadily throughout Q2, with the average number of detected attacks rising 19% from March to April, holding roughly flat into May (+1%), then increasing another 10% into June,” Microsoft said.

Microsoft also observed a highly automated BEC campaign that reached over 67,000 users using scripted emails, Amazon Simple Email Service (SES), and engagement tracking, alongside a separate phishing campaign targeting 107,000 users that abused Microsoft’s authentication flow and trusted cloud services, including Teams archive recording and ICS calendar invite, to disguise malware delivery behind legitimate infrastructure.

Phishing changes but the defense doesn’t

While QR Code and Captcha-based phishing attacks dropped significantly in the second quarter, business email compromise (BEC) charted jumped 121% between March and April, before dropping down again in May.

QR Code phishing represented 8.3 million attacks in June 2026, down from a peak of 18.7 million in March. Similarly, Captcha-gated phishing fell from 12 million attacks in March to 2.2 million in June.

BEC attacks hit 9 million in March, falling to 3.9 million in June.

But even as these phishing classics lost momentum and newer techniques emerged, Microsoft’s defensive advice remained rooted in the basics. It noted organizations should complement email filtering with phishing-resistant authentication such as passkeys and phishing-resistant MFA to reduce the effectiveness of credential theft campaigns.

The company also recommended strengthening Exchange Online Protection and Microsoft Defender for Office 365 with capabilities such as Safe links and Zero-hour Auto Purge (ZAP), in which malicious emails already delivered to mailboxes are removed before they are read, alongside enforcing password-less authentication methods like Windows Hello, FIDO keys, and Microsoft Authenticator.

Microsoft concluded its report with a list of indicators of compromise (IoCs) from the threats observed in the quarter to support detection efforts.

This article first appeared on CSO.

I haven’t slept much in the past few weeks. Not because of some theoretical cyber risk that keeps many executives awake, but because reality just delivered a real wake-up call to our industry — a call that every executive must answer, now.

Imagine this: A major global enterprise, a company most of us interact with indirectly every single day, wakes up to find its entire digital environment obliterated. Thousands of employees in dozens of offices and remote locations are suddenly offline. Customers are cut off, supply chains grind to a halt and regulators are notified with a chilling admission: “We have no idea when we’ll be back.”

This wasn’t ransomware. There was no negotiation, no decryption key to buy, no easy way out. It was destruction — deliberate, coordinated and geopolitically motivated — not monetary.

As a chief customer officer who’s worked with countless customers on cyberattack risks, my perspective hits a bit differently than a CISO or a CTO. I see the aftermath, not just the attack surface. I see the faces behind the tickets, the operations team locked out of their own systems, the support agent answering panicked calls at dawn. And I ask: How many organizations have actually stress-tested their response to this scenario — not a hypothetical, but this very real, lights-out event? Here’s what every leader needs to confront today:

Recovery is not just a technical exercise

The first assumption to break during a real crisis is the belief that recovery is purely technical.

Many organizations have done tabletop exercises and have a backup and recovery playbook, so they feel prepared. They can point to backup windows, retention schedules and immutability controls. The moment a true blackout happens, a different reality surfaces. The people who own the recovery steps either do not know each other, lack the authority to make decisions without supervisor approval or need guidance from offline systems.

The reality is that technical infrastructure almost always holds up better than human infrastructure. Organizations have built their recovery strategy around the assumption that someone competent will be awake, available and empowered when a cyber event happens.

Still, backups are only as good as their independence. Let’s be blunt: If your recovery infrastructure shares identity, authentication or network trust with your Microsoft tenant (such as Azure, Microsoft 365 or Teams), you don’t actually have a recovery plan; you have a false sense of one — and a liability. A recent survey found that while 90% of organizations express confidence in their ability to recover from a cyber incident, fewer than one in three ransomware victims fully recovered their data.

True resilience means immutable, air-gapped backups, untouchable by the same compromise. Anything less is an illusion. I talk to customers about their recovery plans constantly. The customers who have rehearsed all scenarios sleep soundly. Those who haven’t? They’re rolling the dice.

Most business continuity plans ignore ‘total blackout’

I’ve reviewed hundreds of business continuity plans. Almost all assume partial failures — a region, an application, a data center. But what if every system, in every country, goes dark simultaneously? That’s an entirely different playbook. If your team hasn’t run a drill for a global, simultaneous outage, you’re not prepared. The probability is low, but the cost of being unready is existential.

Connected devices, OT systems, field hardware, partner integrations — they all plug into your enterprise network. When the core collapses, it’s not just IT at risk. It’s operational technology, physical safety systems and in regulated sectors, potentially human lives. Understanding and testing those interdependencies is non-negotiable.

This is also where boards need to change the conversation. A study found that only 5% of companies have cybersecurity experts on their board of directors. Recovery time objectives (RTOs) should not be buried in technical appendices. It’s all jargon to boards. That makes translation essential. RTOs must be explained in terms of business impact. “We can recover in four hours” is a technical statement. “Every hour of downtime costs us $2.3M and creates regulatory exposure in three jurisdictions” is a board statement.

That is the level of clarity leaders need.

The most prepared organizations do not wait for an incident to educate the board. They bring the conversation forward proactively. They frame recovery in business terms: revenue, regulatory standing, customer trust and brand reputation.

The most effective framing is often simple. Show the most critical systems. Show what happens if each one is down for one hour, four hours, 24 hours and 72 hours. Show the current recovery capability against each and then show the gap.

If your board is not demanding real answers, your business continuity strategy is likely underfunded and your business is exposed. This is a risk conversation worth forcing because the consequences do not stay inside IT. They can show up in customer churn or missed revenue and ruin an organization’s reputation.

Threat intelligence must be actionable, not archived

Geopolitical attacks, hacktivist campaigns and nation-state targeting aren’t abstract threats. They are active risks, and that intelligence cannot languish in the security team’s inbox. Executive leadership must be looped in — and immediately — so gaps can be closed before they’re exploited. Too often, intelligence enters the security operations function and never reaches the teams responsible for recovery infrastructure or executive decision-making.

If a threat actor is targeting a specific class of backup agents, the team responsible for those agents needs to know now, not two weeks from now. If intelligence suggests destructive activity against a sector, recovery owners need to validate isolation, access paths and restoration procedures immediately. If geopolitical tension increases the likelihood of targeting, executive leadership needs to understand what exposure exists and what actions are being taken. The organizations that survive aren’t just the best at incident response. They’re the ones who anticipated, rehearsed and invested before the attack.

Part of investing in a recovery strategy requires closing the loop between signal and action. The most prepared organizations have already mapped their critical recovery dependencies to specific threat categories. When intelligence touches one of those categories, there is a named owner and a clear set of actions. No guessing or forwarding emails into the void is needed because the distance between the warning and the employees’ ability to do something is shortened.

Looking ahead, the conversation will continue to evolve beyond traditional cyber response. Because in an AI-enabled enterprise, the new question is whether the data within those systems can still be trusted. When AI systems make decisions based on enterprise data, the attack surface becomes the data’s accuracy. A threat actor who quietly corrupts a dataset over 90 days before a recovery event has done more damage than just downtime. They can poison the inputs driving decisions across the business.

Regardless of how AI will change threat intelligence and cyber response, these principles remain the same. Know your problem, whether structural or technological. Ensure your human infrastructure keeps pace with your technical infrastructure, with clear cross-functional ownership and the tools and knowledge to act autonomously. Communicate with your boards often — and correctly.

Let’s not wait for the next headline to ask, “Are we ready?” Have those conversations now. Test your assumptions. Close your gaps. Because in today’s threat landscape, resilience isn’t IT’s job — it’s everyone’s mandate.

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

Traditional phishing techniques are in decline as a result of the disruption of the Tycoon2FA phishing-as-a-service (PHaaS) platform, Microsoft said in a new report, “Email threat landscape: Q2 2026 trends and insights”.

“Phishing volume linked to the platform fell 92% from pre-disruption averages, including QR code phishing and CAPTCHA-gated phishing both declining from their March highs,” the company wrote in the report.

The takedown reduced activity across multiple phishing categories, forcing attackers to shift to newer delivery methods.

Riding this shift in were a few notable phishing campaigns, including an automated business email compromise (BEC) campaign that reached 42,000 organizations in under three hours, and a multi-stage phishing campaign that used nested email (EML) files, calendar invitations, and a Microsoft authentication redirect to deliver malware.

To counter phishing attacks, Microsoft recommends blocking emails containing known bad URLs/ subject fields, enabling password-less authentication methods, or moving to MFA for accounts that still require passwords.

Tycoon2FA disruption sent attackers exploring

The take-down of Tycoon2FA forced its operators to abandon portions of their infrastructure and rework hosting, domain registrations, and delivery mechanisms.

“After falling 15% in March and another 22% in April, Tycoon2FA-linked phishing volume dropped 74% in May to just 1.5 million messages, then fell another 20% in June to 1.2 million, by far the lowest monthly volumes observed in at least a year,” Microsoft said.

The decline extended to QR Code lures and fake CAPTCHA pages, two phishing techniques in which Tycoon2FA accounted for 12% and 14% of industry activity in June, respectively. This indicated that the platform’s customer base had not been able to migrate to a replacement infrastructure.

But cutting off one head of the hacker hydra only gave rise to new tactics elsewhere.

The adaptation came in the form of using Microsoft Teams as a social engineering channel. Attackers established conversations to build trust before attempting credential theft or delivering malicious payloads. “Teams-based phishing volume climbed steadily throughout Q2, with the average number of detected attacks rising 19% from March to April, holding roughly flat into May (+1%), then increasing another 10% into June,” Microsoft said.

Microsoft also observed a highly automated BEC campaign that reached over 67,000 users using scripted emails, Amazon Simple Email Service (SES), and engagement tracking, alongside a separate phishing campaign targeting 107,000 users that abused Microsoft’s authentication flow and trusted cloud services, including Teams archive recording and ICS calendar invite, to disguise malware delivery behind legitimate infrastructure.

Phishing changes but the defense doesn’t

While QR Code and Captcha-based phishing attacks dropped significantly in the second quarter, business email compromise (BEC) charted jumped 121% between March and April, before dropping down again in May.

QR Code phishing represented 8.3 million attacks in June 2026, down from a peak of 18.7 million in March. Similarly, Captcha-gated phishing fell from 12 million attacks in March to 2.2 million in June.

BEC attacks hit 9 million in March, falling to 3.9 million in June.

But even as these phishing classics lost momentum and newer techniques emerged, Microsoft’s defensive advice remained rooted in the basics. It noted organizations should complement email filtering with phishing-resistant authentication such as passkeys and phishing-resistant MFA to reduce the effectiveness of credential theft campaigns.

The company also recommended strengthening Exchange Online Protection and Microsoft Defender for Office 365 with capabilities such as Safe links and Zero-hour Auto Purge (ZAP), in which malicious emails already delivered to mailboxes are removed before they are read, alongside enforcing password-less authentication methods like Windows Hello, FIDO keys, and Microsoft Authenticator.

Microsoft concluded its report with a list of indicators of compromise (IoCs) from the threats observed in the quarter to support detection efforts.

This article first appeared on CSO.

Traditional phishing techniques are in decline as a result of the disruption of the Tycoon2FA phishing-as-a-service (PHaaS) platform, Microsoft said in a new report, “Email threat landscape: Q2 2026 trends and insights”.

“Phishing volume linked to the platform fell 92% from pre-disruption averages, including QR code phishing and CAPTCHA-gated phishing both declining from their March highs,” the company wrote in the report.

The takedown reduced activity across multiple phishing categories, forcing attackers to shift to newer delivery methods.

Riding this shift in were a few notable phishing campaigns, including an automated business email compromise (BEC) campaign that reached 42,000 organizations in under three hours, and a multi-stage phishing campaign that used nested email (EML) files, calendar invitations, and a Microsoft authentication redirect to deliver malware.

To counter phishing attacks, Microsoft recommends blocking emails containing known bad URLs/ subject fields, enabling password-less authentication methods, or moving to MFA for accounts that still require passwords.

Tycoon2FA disruption sent attackers exploring

The take-down of Tycoon2FA forced its operators to abandon portions of their infrastructure and rework hosting, domain registrations, and delivery mechanisms.

“After falling 15% in March and another 22% in April, Tycoon2FA-linked phishing volume dropped 74% in May to just 1.5 million messages, then fell another 20% in June to 1.2 million, by far the lowest monthly volumes observed in at least a year,” Microsoft said.

The decline extended to QR Code lures and fake CAPTCHA pages, two phishing techniques in which Tycoon2FA accounted for 12% and 14% of industry activity in June, respectively. This indicated that the platform’s customer base had not been able to migrate to a replacement infrastructure.

But cutting off one head of the hacker hydra only gave rise to new tactics elsewhere.

The adaptation came in the form of using Microsoft Teams as a social engineering channel. Attackers established conversations to build trust before attempting credential theft or delivering malicious payloads. “Teams-based phishing volume climbed steadily throughout Q2, with the average number of detected attacks rising 19% from March to April, holding roughly flat into May (+1%), then increasing another 10% into June,” Microsoft said.

Microsoft also observed a highly automated BEC campaign that reached over 67,000 users using scripted emails, Amazon Simple Email Service (SES), and engagement tracking, alongside a separate phishing campaign targeting 107,000 users that abused Microsoft’s authentication flow and trusted cloud services, including Teams archive recording and ICS calendar invite, to disguise malware delivery behind legitimate infrastructure.

Phishing changes but the defense doesn’t

While QR Code and Captcha-based phishing attacks dropped significantly in the second quarter, business email compromise (BEC) charted jumped 121% between March and April, before dropping down again in May.

QR Code phishing represented 8.3 million attacks in June 2026, down from a peak of 18.7 million in March. Similarly, Captcha-gated phishing fell from 12 million attacks in March to 2.2 million in June.

BEC attacks hit 9 million in March, falling to 3.9 million in June.

But even as these phishing classics lost momentum and newer techniques emerged, Microsoft’s defensive advice remained rooted in the basics. It noted organizations should complement email filtering with phishing-resistant authentication such as passkeys and phishing-resistant MFA to reduce the effectiveness of credential theft campaigns.

The company also recommended strengthening Exchange Online Protection and Microsoft Defender for Office 365 with capabilities such as Safe links and Zero-hour Auto Purge (ZAP), in which malicious emails already delivered to mailboxes are removed before they are read, alongside enforcing password-less authentication methods like Windows Hello, FIDO keys, and Microsoft Authenticator.

Microsoft concluded its report with a list of indicators of compromise (IoCs) from the threats observed in the quarter to support detection efforts.

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

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

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

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

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

A ‘persistent operator’ that acts without approval

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

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

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

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

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

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

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

A ‘planted accomplice’ that does all the work

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

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

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

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

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

FOMO exposing security gaps

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

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

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

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

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

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

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

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.

I haven’t slept much in the past few weeks. Not because of some theoretical cyber risk that keeps many executives awake, but because reality just delivered a real wake-up call to our industry — a call that every executive must answer, now.

Imagine this: A major global enterprise, a company most of us interact with indirectly every single day, wakes up to find its entire digital environment obliterated. Thousands of employees in dozens of offices and remote locations are suddenly offline. Customers are cut off, supply chains grind to a halt and regulators are notified with a chilling admission: “We have no idea when we’ll be back.”

This wasn’t ransomware. There was no negotiation, no decryption key to buy, no easy way out. It was destruction — deliberate, coordinated and geopolitically motivated — not monetary.

As a chief customer officer who’s worked with countless customers on cyberattack risks, my perspective hits a bit differently than a CISO or a CTO. I see the aftermath, not just the attack surface. I see the faces behind the tickets, the operations team locked out of their own systems, the support agent answering panicked calls at dawn. And I ask: How many organizations have actually stress-tested their response to this scenario — not a hypothetical, but this very real, lights-out event? Here’s what every leader needs to confront today:

Recovery is not just a technical exercise

The first assumption to break during a real crisis is the belief that recovery is purely technical.

Many organizations have done tabletop exercises and have a backup and recovery playbook, so they feel prepared. They can point to backup windows, retention schedules and immutability controls. The moment a true blackout happens, a different reality surfaces. The people who own the recovery steps either do not know each other, lack the authority to make decisions without supervisor approval or need guidance from offline systems.

The reality is that technical infrastructure almost always holds up better than human infrastructure. Organizations have built their recovery strategy around the assumption that someone competent will be awake, available and empowered when a cyber event happens.

Still, backups are only as good as their independence. Let’s be blunt: If your recovery infrastructure shares identity, authentication or network trust with your Microsoft tenant (such as Azure, Microsoft 365 or Teams), you don’t actually have a recovery plan; you have a false sense of one — and a liability. A recent survey found that while 90% of organizations express confidence in their ability to recover from a cyber incident, fewer than one in three ransomware victims fully recovered their data.

True resilience means immutable, air-gapped backups, untouchable by the same compromise. Anything less is an illusion. I talk to customers about their recovery plans constantly. The customers who have rehearsed all scenarios sleep soundly. Those who haven’t? They’re rolling the dice.

Most business continuity plans ignore ‘total blackout’

I’ve reviewed hundreds of business continuity plans. Almost all assume partial failures — a region, an application, a data center. But what if every system, in every country, goes dark simultaneously? That’s an entirely different playbook. If your team hasn’t run a drill for a global, simultaneous outage, you’re not prepared. The probability is low, but the cost of being unready is existential.

Connected devices, OT systems, field hardware, partner integrations — they all plug into your enterprise network. When the core collapses, it’s not just IT at risk. It’s operational technology, physical safety systems and in regulated sectors, potentially human lives. Understanding and testing those interdependencies is non-negotiable.

This is also where boards need to change the conversation. A study found that only 5% of companies have cybersecurity experts on their board of directors. Recovery time objectives (RTOs) should not be buried in technical appendices. It’s all jargon to boards. That makes translation essential. RTOs must be explained in terms of business impact. “We can recover in four hours” is a technical statement. “Every hour of downtime costs us $2.3M and creates regulatory exposure in three jurisdictions” is a board statement.

That is the level of clarity leaders need.

The most prepared organizations do not wait for an incident to educate the board. They bring the conversation forward proactively. They frame recovery in business terms: revenue, regulatory standing, customer trust and brand reputation.

The most effective framing is often simple. Show the most critical systems. Show what happens if each one is down for one hour, four hours, 24 hours and 72 hours. Show the current recovery capability against each and then show the gap.

If your board is not demanding real answers, your business continuity strategy is likely underfunded and your business is exposed. This is a risk conversation worth forcing because the consequences do not stay inside IT. They can show up in customer churn or missed revenue and ruin an organization’s reputation.

Threat intelligence must be actionable, not archived

Geopolitical attacks, hacktivist campaigns and nation-state targeting aren’t abstract threats. They are active risks, and that intelligence cannot languish in the security team’s inbox. Executive leadership must be looped in — and immediately — so gaps can be closed before they’re exploited. Too often, intelligence enters the security operations function and never reaches the teams responsible for recovery infrastructure or executive decision-making.

If a threat actor is targeting a specific class of backup agents, the team responsible for those agents needs to know now, not two weeks from now. If intelligence suggests destructive activity against a sector, recovery owners need to validate isolation, access paths and restoration procedures immediately. If geopolitical tension increases the likelihood of targeting, executive leadership needs to understand what exposure exists and what actions are being taken. The organizations that survive aren’t just the best at incident response. They’re the ones who anticipated, rehearsed and invested before the attack.

Part of investing in a recovery strategy requires closing the loop between signal and action. The most prepared organizations have already mapped their critical recovery dependencies to specific threat categories. When intelligence touches one of those categories, there is a named owner and a clear set of actions. No guessing or forwarding emails into the void is needed because the distance between the warning and the employees’ ability to do something is shortened.

Looking ahead, the conversation will continue to evolve beyond traditional cyber response. Because in an AI-enabled enterprise, the new question is whether the data within those systems can still be trusted. When AI systems make decisions based on enterprise data, the attack surface becomes the data’s accuracy. A threat actor who quietly corrupts a dataset over 90 days before a recovery event has done more damage than just downtime. They can poison the inputs driving decisions across the business.

Regardless of how AI will change threat intelligence and cyber response, these principles remain the same. Know your problem, whether structural or technological. Ensure your human infrastructure keeps pace with your technical infrastructure, with clear cross-functional ownership and the tools and knowledge to act autonomously. Communicate with your boards often — and correctly.

Let’s not wait for the next headline to ask, “Are we ready?” Have those conversations now. Test your assumptions. Close your gaps. Because in today’s threat landscape, resilience isn’t IT’s job — it’s everyone’s mandate.

This article is published as part of the Foundry Expert Contributor Network.
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Business email and collaboration suite Zimbra has received a major security update that fixes several critical issues that could allow attackers to execute malicious code on the server or in users’ browsers.

Available in commercial and open-source editions, Zimbra Collaboration Suite is a self-hosted Microsoft Exchange alternative that is popular with businesses, government entities, and educational institutions, which has made it a target for attackers in the past.

Version 10.1.20 released this week includes patches for nine vulnerabilities, including a permanent fix for a critical flaw announced in June in the SNMP monitoring component that could be exploited to inject commands when notifications are enabled.

The release also fixes four cross-site scripting (XSS) vulnerabilities in the Classic Web Client that could allow attackers to execute malicious scripts when users view emails in the web interface. For example, one vulnerability can be triggered through specially crafted attachment filenames and another when users render an attachment.

XSS vulnerabilities are dangerous because they execute scripts in the user’s browser in the context of the page. That gives rogue code the same privileges as the user, enabling it to perform malicious actions, exfiltrate data, or even leak session cookies.

In 2025, an XSS vulnerability in the calendar import feature of the Zimbra Classic Web Client (CVE-2025-27915) was exploited in attacks targeting Brazilian military personnel. Many other Zimbra vulnerabilities have been exploited over the years, sometimes as zero-days, especially by Russian state-sponsored APT groups, such as Fancy Bear (APT28), Cozy Bear (APT29), and Winter Vivern (TA473).

A recent incident involved a Russian threat group targeting a Ukrainian critical infrastructure agency in March using specifically crafted emails that exploited a known Zimbra stored XXS vulnerability (CVE-2025-66376).

Another vulnerability fixed in the newly released Zimbra 10.1.20 could allow authenticated attackers to bypass email forwarding restrictions. Adding email forwarding rules to a compromised account is a common way to achieve persistence and continuously exfiltrate emails from a mailbox over an extended period of time, even if the account password is changed.

The release also fixes a security issue related to access controls in the EWS extension, an authorization issue in mailbox delegation, and a server-side request forgery (SSRF) vulnerability in the Nextcloud integration. Nextcloud is another self-hosted collaboration suite that is popular with government and public institutions that don’t want to rely on public clouds.

It’s worth noting that this is the second Zimbra security update this month. Version 10.1.19, released on July 7, patched another unspecified security issue in the Classic Web Client that could run malicious code when specially crafted emails were opened by users.

Zimbra owner Synacor strongly advises customers to upgrade to the latest available version as soon as possible to keep their environments secure. While none of these flaws had zero-day status, hacker groups have a history for quickly adopting known Zimbra exploits.

Es würde mich nicht wundern, wenn Ihnen dieses Szenario bekannt vorkommt. Denn Situationen wie diese sind keine Ausnahme. Laut der Trovarit-Studie „ERP in der Praxis 2024/25“, für die über 1.700 DACH-Unternehmen befragt wurden, nimmt eine typische ERP-Einführung im Mittelstand zwischen zehn und dreizehn Monaten in Anspruch. Parallel werden in mehr als der Hälfte der Projekte Zeit- oder Budgetziele nicht eingehalten. Die Implementierungskosten pro Nutzer liegen demnach bei durchschnittlich 5.917 Euro, wobei Schulung, Datenmigration und Anpassung 50 bis 70 Prozent der Gesamtkosten ausmachen – nicht die Lizenz.

Für einen Konzern mit 800 Mitarbeitern, SAP-Legacy und drei Produktionsstandorten sind das keine Schreckenszahlen. Für einen Immobilienmakler mit 40 Mitarbeitern, der seine Angebote automatisiert versenden will oder einen Steuerberater, der Mandantenpost automatisch klassifizieren möchte, durchaus. Diese Mittelständler brauchen kein ERP-Projekt, sondern im Wesentlichen drei funktionierende Workflows.

Das Low-Code-Versprechen – und die KMU-Praxis

Low-Code– und KI-Automatisierungsplattformen wie n8n oder Make.com versprechen, die Lücke zwischen ERP-Großprojekt und Stillstand aufzulösen. Dabei ist anzumerken, dass beide Plattformen keinen Ersatz für ein ERP darstellen: Sie substituieren keine Stücklisten-Verwaltung, keine mehrstufige Fertigungsplanung und keine IFRS-Konsolidierung. Dafür ist weiterhin ein ERP nötig.

Aber Plattformen wie die beiden genannten stellen Verbindungen zwischen Systemen her, die keine native Schnittstelle haben. Entscheidungen automatisieren, die nach klaren Regeln getroffen werden und Workflows bauen, die bisher über E-Mail, Excel-Export und Telefonanruf abgewickelt werden – das sind für den deutschen Mittelstand oft genau die Prozesse, die täglich die meiste Zeit kosten und zugleich selten im ERP verortet sind. Vielmehr sitzen diese in Outlook-Ordnern, in freigegebenen Tabellenblättern und in den Routinen von Mitarbeitern, die „das schon immer so machen”.

Das KfW-Mittelstandspanel vom Februar 2026 (PDF) stellt fest, dass 20 Prozent der mittelständischen Unternehmen in Deutschland KI einsetzen – fünfmal mehr als noch im Jahr 2018. Bei Unternehmen mit über 50 Mitarbeitern liegt die Quote bereits bei 36 Prozent. KI ist im Mittelstand also angekommen. Die Frage ist nur, in welcher Form. Hier hilft ein Blick auf die KPMG-Studie „Digitalisierung im Rechnungswesen 2025/2026“. Demnach:

• setzen 53 Prozent der Befragten KI in der Buchhaltung ein oder befinden sich gerade in der Einführungsphase.
• berichten 37 Prozent dadurch von sofortigen Zeiteinsparungen bei transaktionalen Prozessen.

Was diese Studien allerdings nicht zeigen, ist der Implementierungsaufwand. Und genau hier liegt der große Unterschied zwischen dem KI-Einsatz per Low-Code und einem ERP-Einführungsprojekt. Das verdeutlichen die folgenden drei Anwendungsfälle für KMU im Bereich KI und Low-Code. Diese sind dazu geeignet, innerhalb von Wochen (statt Monaten oder Jahren) Ergebnisse zu liefern:

• Rechnungseingang: PDF-Rechnungen oder XRechnung-Dokumente entgegennehmen, relevante Felder per KI extrahieren und gegen ERP-Stammdaten prüfen oder vorkontierte Buchungsvorschläge generieren – was früher ein Buchhalter täglich in Stunden manuell erledigte, kann heute als Hintergrundprozess laufen. Dabei werden Fehler weiterhin von Menschen überprüft, statt automatisch in die Buchung zu laufen. Die typische Implementierungszeit hierfür liegt (in überschaubaren Setups) bei zwei bis vier Wochen.
• Angebotsmanagement: Eine Kundenanfrage geht per E-Mail ein, KI extrahiert den Leistungsumfang, prüft gegen Preislisten, generiert einen Angebotsentwurf und leitet ihn zur Freigabe an einen Sachbearbeiter weiter. Der Kunde bekommt sein Angebot schneller, die Bearbeitung kostet weniger Zeit. Dabei ist kein ERP-Modul beteiligt – und es fällt keine siebenstellige Projektsumme an.
• Mandantenpost bei Steuerberatern: Eingehende Dokumente werden nach Typ und Dringlichkeit klassifiziert und Zuständigkeiten zugewiesen. Was in Kanzleien mit hohem Postaufkommen täglich zu Engpässen führt, lässt sich so als regelbasierter Workflow abbilden – ganz ohne großes DATEV-Einführungsprojekt.

Woran Low-Code und KI scheitern – und wie Sie das verhindern

Das größte operative Risiko bei Low-Code-Projekten ist Schatten-IT, beziehungsweise -KI. So hat der Digitalverband Bitkom in einer Befragung vom Oktober 2025 herausgefunden, dass 17 Prozent der Umfrageteilnehmer davon ausgehen, dass ihre Mitarbeiter KI-Tools über private Accounts dienstlich nutzen. Bei weiteren 17 Prozent gab es diesbezüglich vereinzelte Fälle – und bei acht Prozent ist dieses Gebaren weit verbreitet. Sind in einem solchen Fall Kundendaten im Spiel, handelt es sich um einen DSGVO-Verstoß ohne Auftragsverarbeitungsvertrag als Grundlage. Insofern braucht eine Low-Code-Strategie klare Nutzungsrichtlinien – ansonsten tauscht man ein ERP-Risiko gegen ein Compliance-Risiko, was eher kein Fortschritt wäre.

Darüber hinaus können Low-Code-Plattformen bei hochspezialisierten Logikstrukturen (mehrstufige Produktionsplanung, Konzernkonsolidierung, ISO-zertifizierte Auditpfade) an strukturelle Grenzen stoßen. Und: Der Vendor Lock-in ist real – proprietäre Plattformen lassen sich schlecht migrieren. Hier haben Open-Source- und Self-Hosting-Lösungen einen strukturellen Vorteil. Allerdings ist auch das kein Garant dafür, dass Abhängigkeiten künftig ausbleiben.

Vor diesem Hintergrund empfehle ich Mittelständlern, sich an den folgenden drei Schritten zu orientieren, um ihre Low-Code-KI-Ambitionen nachhaltig zu verwirklichen.

1. Pilotprozess nach harten Kriterien wählen: Der erste Kandidat sollte vier Kriterien gleichzeitig erfüllen: hohes Volumen, klare fachliche Regeln, keinen schreibenden Zugriff auf das ERP in der ersten Ausbaustufe, und einen Verantwortlichen, der den Prozess heute schon im Detail kennt. Die Bereiche Rechnungseingang und Angebotsvorbereitungerfüllen diese Voraussetzungen fast immer. Ein Prozess, der gleichzeitig produktionskritisch und schlecht dokumentiert ist, hingegen fast nie. Wer an dieser Stelle den falschen Piloten wählt, muss damit rechnen, dass die Akzeptanz bei einem zweiten Versuch deutlich geringer ist.
2. Datenschutz vor dem ersten Workflow klären: Ein Auftragsverarbeitungsvertrag mit dem Modellanbieter ist eine Sache, die vor dem Produktivstart stehen sollte, nicht in der Nachbereitung. Diese Prüfung muss auch mit Blick auf die Low-Code-Plattform selbst erfolgen, nicht nur das Sprachmodell dahinter. Wer hier sauber arbeitet, nimmt der eingangs beschriebenen Schatten-IT-Problematik die Grundlage: Wenn die offizielle Lösung schneller einsatzbereit ist als die inoffizielle, greifen Mitarbeiter auch seltener auf private KI-Accounts zurück.
3. Skalierungsgrenze vorab definieren: Legen Sie fest, ab welchem Komplexitätsgrad ein Prozess zurück auf die ERP-Roadmap wandert, bevor Sie ihn brauchen. Diese Grenze nachträglich einzuziehen, kostet – inmitten eines gewachsenen Workflows mit zwölf Verzweigungen – mehr Zeit als die ursprüngliche ERP-Entscheidung eingespart hat. Ein einfaches Signal funktioniert in der Praxis gut: Sobald ein Workflow mehr als drei verschachtelte Bedingungen benötigt, um eine einzelne Entscheidung abzubilden, sollte die Logik überprüft – nicht erweitert – werden.

Der eigentliche Nutzwert für KMU

Sechs Monate weniger Implementierungszeit bedeutet nicht nur sechs Monate früher produktiv sein zu können. Es bedeutet:

• Ergebnisse demonstrieren zu können, bevor das ERP-Budget freigegeben ist.
• Prozesse testen zu können, bevor sie skaliert werden.
• Scheitern zu können, weil es um einen Workflow für 4.000 Euro und nicht um ein Projekt für 200.000 Euro geht.

Ihr ERP-Projekt darf ruhig auf der Roadmap bleiben. Aber für die nächsten zwölf Monate gibt es meistens einen schnelleren Weg. (fm)

Dieser Beitrag wurde im Rahmen des deutschsprachigen Experten-Netzwerks von Foundry veröffentlicht. Lust mitzumachen? Jetzt bewerben!

Attackers are increasingly using AI agents to automate all phases of cyberattacks, prompting the security industry and enterprises to find new network defense approaches. One technique that shows promise is to intentionally plant decoy files with prompts that trigger the content safety guardrails built into LLMs with the goal of crashing rogue agentic workflows.

Using decoy resources as tripwires that alert defenders about potential unauthorized access is not a new idea in cybersecurity. These are known as canaries — after the canary in the coal mine early warning system — and can be fake documents, AWS access keys, database dumps, DNS records, and even URLs that would not be queried by legitimate processes, but would be attractive targets for attackers.

What’s new in the approach devised and tested by security firm Tracebit is to use these decoy resources not merely to trigger alerts, but to actually stop AI agents, buying defenders more time. Dubbed “context bombing,” the technique takes advantage of the fact that LLMs are inherently vulnerable to prompt injection — acting on instructions they might encounter inside the data they process.

Enterprises that build their own AI agents have to worry about malicious prompts placed by attackers on web pages, emails, documents, code comments, and in other third-party resources those agents might access. With no defenses in place, companies risk their own agents being hijacked and used against them to perform unauthorized actions. But the malicious AI agents used by attackers have the same vulnerability.

“We call the defensive version a context bomb: a short piece of text designed to trigger a model’s safety guardrails, planted directly in the attacker’s path — a decoy secret, environment variable, or DNS record,” Sam Cox, Tracebit’s CTO, said in a blog post. “An AI agent that reads it will frequently refuse to continue. You still get the alert; and the attack now halts early instead of merely being detected.”

Context bombing triggers massive drop in attack success rate

Tracebit’s tests performed in an AWS test environment cut the success rate normally achieved by AI models across multiple attacks paths by up to 90% by simply adding instructions that would trigger their content safety guardrails. LLMs are trained to refuse answering questions about creating biological, chemical, or nuclear weapons, or, in the case of Chinese open-weight models, sensitive political topics such as the Tiananmen Square massacre.

Furthermore, once one of these banned instructions make it into the context window, the whole session is poisoned, and the model will often refuse to continue without manual intervention. The researchers ran many tests to find short strings that would trigger model safety guardrails reliably, but it’s worth noting that the identified strings were different between the tested models: Claude Opus 4.8, Gemini 3.1 Pro, GLM 5.2, DeepSeek V4 Pro, and Kimi K2.6.

During baseline tests the AI agents managed on average to obtain full account admin in 54% of the 154 attack runs and full compromise (admin + persistence) in 36% of tests. With the context bombs in place, their success rate dropped to 5% for admin access and 1% for full compromise. Also, in 91% of baseline attack runs, the agents managed to complete at least one of ten possible attacks paths, but their average success rate dropped to 15% with the context bombs.

The models from Western AI labs — Opus and Gemini — proved the most capable at reaching full admin access, with 93% and 70% success rates, but were also the most impacted by the context bombs with both their success rates dropping to 0%. This shows that the content safety guardrails are much stronger in these models compared to the Chinese ones that were tested.

Attackers experimented with safety refusals first

The Tracebit researchers had already been experimenting with AI canaries for alert purposes. A paper published in May showed that canaries alerted defenders in 95.9% of 162 successful attack runs performed with AI agents using 10 different AI models. The alerts were triggered 8 minutes before the agents performed critical actions, while the runs required 14 minutes on average to reach admin access.

During those tests the researchers observed that if they instructed the models that the network might include deception, their success rate for full admin access and persistence dropped from 20% to 3% on average.

Then in June, researchers from security firm Socket.dev came across a software supply chain attack involving malicious PyPI wheels that contained prompt injections with content designed to trigger safety guardrails inside AI models. The malicious prompts were included in code comments at the start of the files and the purpose was likely to evade detection from LLM-powered security scanners.

“This header appears designed for AI-mediated analysis, not for Node, Bun, or Python,” the Socket.dev researchers said at the time. “It attempts to derail scanners or analyst copilots that feed the beginning of a file to a language model without clearly isolating the content as untrusted data. In weak pipelines, this can cause refusal behavior, prompt confusion, context pollution, or premature classification before the scanner reaches the actual malware.”

The Tracebit researchers then had the idea to flip the script and trigger such safety refusals through their canary technique against malicious AI agents. So not only does the agent run stop, but an alert is also triggered because the canary was accessed.

There is a risk that the canaries could also be discovered and accessed accidentally by legitimate LLM-powered tools used by engineering or security teams. But at the same time this means organizations could potentially deploy such canaries in sensitive places to stop their own AI agents that might become hijacked or go off the rails on their own.

There are many reports online where AI models performed destructive or unauthorized actions like deleting databases and folders or elevating their privileges because they got struck in failure loops and explored creative ways to complete their tasks. This is even more common with autonomous AI agents that are tasked to reach a goal without human intervention or supervision.

“The speed of autonomous AI attacks is why deception is climbing the priority list for security programs,” Tracebit’s Cox said. “When the attack chain takes minutes rather than days, every minute of response time you can claw back matters — and a control that stops the attacker outright, rather than just reporting them, changes the economics significantly.”

Dubbed the “Genius Hacker,” Park Chan-am began his white hat hacker journey at the precocious age of 11, winning awards at domestic and international hacking competitions since his teenage years.

He has since served as a cybersecurity advisor for various Korean government agencies, including the National Police Agency, and has played a key role in the country’s defense against Democratic People’s Republic of Korea (DPRK)-affiliated cyberattacks.

At a seminar held this month as part of the 15th Information Security Day event hosted and organized by government agencies including the Ministry of Science and ICT and the Korea Internet and Security Agency (KISA), Park, now CEO of security firm Steelion, explained the changes in the security environment in the AI ​​era and the priority response tasks for information security organizations under the theme of “Major AI Threats and Security Priorities.”

“While AI is a new technology, the core of security ultimately lies in access control, supply chain management, and human verification,” he said.

Like many security experts, Park sees AI fundamentally changing the speed of cyberattacks. In the past, infiltrating a corporate network required significant time analyzing a range of software systems to find exploitable vulnerabilities — a task that the use of AI has significantly accelerated.

“In the past, it took at least four weeks to find vulnerabilities, but now it takes less than a day,” he said. “In the era of AI, all software installed within a company becomes a much more critical target for attacks.”

As a result, securing internal software and the software supply chain are paramount — and require a different perspective on accountability, Park noted.

“Clients often ask, ‘Isn’t this just a product made by the vendor?’” he said. “From the moment it is installed in the company system, that software is no longer a vendor issue but part of the corporate system.”

“We are now in an era where third-party issues can no longer be attributed solely to vendor responsibility,” he stressed.

MCP under threat

Park also sees authorization management as a key challenge security teams will face in the agentic era. For example, companies have been increasingly utilizing Model Context Protocol (MCP)-based AI agents to read emails, analyze documents, and connect internal systems with various business tasks. But as the workload handled by AI agents increases, every step an AI agent takes, reading external documents and interacting with internal systems, can become a potential attack vector.

Prompt contamination through malicious documents and the leakage of internal information via agents with excessive privileges are quite realistic scenarios, Park said. In particular, he pointed out that issues that previously ended as minor problems, such as residual privileges left by former employees or outsourced personnel, could escalate into major incidents as AI automatically links these elements together.

“When introducing AI, permissions must be designed before functions,” he said. “The entire MCP process must be approached as a single attack path.”

The ever-widening blast radius of AI testing

Local AI testing environments are becoming a dangerous security blind spot that information security leaders often overlook. Rapid experimentation with open-source AI, such as LLaMA-based models, on personal or work PCs often results in servers or ports being left open, and if vulnerabilities are discovered, intrusion pathways immediately open up.

“When the [Ollama] remote code execution vulnerability was discovered in 2024, there were over 1,000 servers exposed to the internet, but recently in 2026, it has been confirmed that over 300,000 servers from the same targets are exposed,” said Park, adding that “the act of testing AI itself can become a new security risk.”

Vulnerability management on notice

Security operations must also change, Park stressed, noting that the number of alerts that security personnel must handle has increased tenfold, and in some cases up to a hundredfold, making it virtually impossible to respond to all vulnerabilities using the same standards.

As a solution, Park sees the Common Vulnerability Scoring System (CVSS) being insufficient for determining priorities. Instead, he suggested that vulnerability response priorities be determined by utilizing the Exploit Prediction Scoring System (EPSS), which predicts the actual likelihood of exploitation, along with the US government’s Known Exploited Vulnerabilities (KEV) list.

For example, if a vulnerability’s CVSS score is 7.5, it is not classified as critical, so it is likely to be pushed down the priority list. But the response priority changes completely if the same vulnerability is listed on the KEV list, has been exploited in actual ransomware attacks, and the probability of an attack based on EPSS has skyrocketed from 1% to 90% within two months. “You must consider these factors together to identify the vulnerabilities that actually need to be patched first,” he stressed.

“Amidst the vast noise known as the AI s​lop, the criteria for deciding what to patch first is now becoming a core competency for security personnel,” he added.

Park also presented new defense techniques applicable to the AI ​​era, such as methods to detect automated attacks by analyzing behavioral differences between humans and AI attackers, and proof of work (PoW) challenges that intentionally impose computational load on AI attackers to slow down their attacks. A prime example is filtering out abnormal access by analyzing mouse movements, keyboard input, and scrolling patterns.

“It is a more realistic strategy to reduce the burden on security teams by filtering out at least some attacks, rather than trying to block them 100%,” he said.

Even in the age of AI, technology alone cannot ensure complete security, he noted. “While AI can scan for threats broadly and quickly, verifying and confirming them ultimately falls to humans,” he said. “Humans are still important.”

Somewhere in the last eighteen months, “AI agent” stopped being a useful term. CIOs may even be afraid to ask what “agent” truly means, as it now seems to describe everything from a chatbot that answers HR questions to an autonomous research system that plans its own week of work. When a single phrase carries that much weight, well, it stops carrying any.

I’ve spent the last three years inside hundreds of enterprise AI deployments, and the factor that separates the programs scaling elegantly from the ones still shuffling is often the CIO’s architectural fluency: The ability to look at business problems across the organization and recognize, on sight, what kind of AI architecture is the right fit. In my experience there are six archetypes, each with their own nuances, that CIOs should internalize to make well-informed decisions going forward.

1. The conversational assistant

The first, and the one most enterprises meet first, is the conversational assistant: The chat-based partner that an employee or customer opens when they want to think out loud. Deloitte found that 38% of organizations report AI is already strengthening their client or customer relationships. This is the architecture people fall in love with: A well-designed assistant with constantly updated information, persistent user-level memory, tools that can act on behalf of users, and citations on every factual claim becomes a useful problem-solver that’s available at any hour of the day.

A global law firm I work with deployed an internal assistant that gives every attorney instant access to the firm’s accumulated precedent, memos and prior matter work. Associates who used to spend the first hour of a research task hunting through document management systems now start with a grounded, citation-backed answer and refine from there. This helped the firm’s institutional knowledge, previously locked in the heads of senior partners, become queryable by anyone with a deadline at 11 p.m., or later.

A second example: A mid-market wealth management firm built a client-facing assistant that handles portfolio questions, statement explanations and routine servicing requests. The assistant draws from each client’s actual holdings, recent activity and the firm’s published market commentary, with citations linking back to source documents. Advisors stopped being interrupted for the questions that didn’t require an advisor, and clients got answers on a Sunday.

2. The triggered workflow

Another pattern producing the value across the enterprises I work with is something that runs silently: An email arrives, a ticket is created, a file lands in a folder and the agent executes a process utilizing both reasoning and determinism. These agents don’t even require user adoption, because they’re invisible to the end user. They produce measurable outcomes, but fit cleanly into the audit and change-control processes IT teams have run for decades.

A commercial insurer I advise built a triggered workflow for inbound submissions. Every broker email that arrives at the underwriting inbox is classified by line of business, the attachments are parsed, key risk fields are extracted into the policy administration system, and a draft acknowledgment is queued for the underwriter’s review. Seemingly overnight, the inbox began arriving pre-sorted, and submission throughput rose meaningfully without any change to headcount.

Another example, this time from a private equity firm: Every inbound confidential information memorandum (CIM) that hits the deal team’s shared inbox triggers a workflow that extracts the financial summary, screens it against the firm’s investment criteria, drafts a preliminary memo and posts the result into the deal-tracking system. Associates still make the call on what to pursue, but the first three hours of manual work on each opportunity now happen before anyone even opens the file.

3. The autonomous agent — with sub-agents

Here we have the architecture that gets the most conference attention: The autonomous agent, given a task and left to plan its own steps by utilizing its own sub-agents. Autonomous agents are not one-size-fits-all, but they do meet a specific need: Multi-source research, complex cross-system lookups, deep-dive investigations. All of these are processes where the path isn’t usually specified in advance, but the tools are. With the right design discipline, an autonomous agent feels like having a self-sufficient teammate who can call in the right resources and specialists if needed.

A global consulting firm I work with uses an autonomous research agent for early-stage engagement scoping. Given a target company and a strategic question, the agent decides for itself which sub-agents to consult (choosing from internal proprietary databases, prior engagement archives, licensed market data, public filings) and produces a structured briefing with its reasoning chain attached.

Another large technology company I know of deployed an autonomous agent for cross-system incident investigation. When a production alert fires, the agent forms a hypothesis, queries the necessary sub-agents with relevant monitoring tools, log stores and deployment systems, and follows the trail until it reaches a defensible root-cause summary to surface to an engineer.

4. The multi-agent team

The fourth pattern is where the next wave of enterprise quality gains is going to come from. According to Databricks, usage of multi-agent systems grew 327% in just four months as enterprises moved beyond single chatbots. Several specialized agents, each with its own role and toolset, coordinate through a shared protocol: A researcher and a writer, a planner and a set of executors, a proposer and a critic. The proposer-critic feedback loop is one of the smartest techniques in agent design today. One model produces an answer; a second, with a different prompt and often a different provider, evaluates it against explicit criteria. For compliance review, contract analysis, high-stakes classification and any output that will be audited, this second pass is extremely helpful and mirrors how human teams work.

A global bank I work with uses a multi-agent system for marketing and communications review. One agent drafts client-facing copy, a second checks it against the firm’s regulatory and brand guidelines and a third checks it against jurisdiction-specific disclosure rules. Disagreements among the agents are surfaced to a human reviewer with the specific clauses flagged. The compliance team stopped being the bottleneck on every routine piece of copy and started focusing on the high-judgment cases instead.

The next example: A pharmaceutical company built a multi-agent workflow for medical literature summarization. A retriever agent gathers candidate studies, a reader agent extracts study design and findings, a critic agent challenges the reader’s claims against the source text, and a synthesizer agent composes the final brief. The proposer-critic loop in the middle is the reason the medical affairs team trusts the output enough to act on it.

5. The human-in-the-loop (HITL) agent

The fifth pattern is the one I think we’ll see increasingly more of in the future. While many see “full automation” as the goal, the right target is actually to let the agent handle the 80% of a task that is mechanical, while preserving human judgment at the most critical moments. This is achievable via human-in-the-loop (HITL) agents. According to Moody’s, 42% of compliance professionals believe that human oversight is mandatory, and I agree: AI should run right, by getting approval and review before any sensitive business action is taken. HITL is the architecture that can help turn a skeptical team into an enthusiastic one.

A regional health system I worked with uses a HITL agent for prior-authorization letters. The agent assembles the clinical evidence, drafts the letter against the relevant payer’s criteria, and routes it to a nurse case manager for review inside the existing workflow tool. The nurse approves, edits or rejects in seconds rather than minutes, and every edit helps make the next draft better.

A property management company uses a HITL agent to run its maintenance work orders. When a tenant emails about a problem (an HVAC unit that died overnight, say), the agent pulls the structured details (tenant, unit, issue type, urgency), matches the job to the right vendor from the directory, and drafts the work order. A team member approves it in Slack before anything goes out. From there the agent emails the vendor with the full order, confirms with the tenant that someone is on the way and updates Airtable, closing the loop completely.

6. The scheduled agent

On a set schedule or against a batch of inputs, this agent runs the same defined task: Produce a report, refresh a dataset, monitor a set of sources or summarize a period of activity. Under this archetype, unsexy work gets done consistently, integrated into existing operational rhythms like the Monday morning meeting, the daily standup and the monthly board deck, without asking anyone to change their behavior. This is the architecture that shifts AI from feeling like even more work, to a seamless teammate that just works.

A private equity firm I work with runs a scheduled agent every Monday at 6 a.m. that monitors news, filings and earnings activity across every portfolio company and produces a single PDF that lands in the deal partners’ inboxes before the weekly investment meeting. No one logs into a dashboard. The agent shows up, on time, with the same format every week, and the meeting now starts from a shared baseline rather than from whatever each partner happened to read over the weekend.

A second example: A global manufacturer runs a nightly batch agent that ingests the day’s quality-control reports across plants, summarizes anomalies against a rolling baseline, and produces an end-of-shift handoff document for each site lead’s morning. The agent doesn’t flag emergencies, but it ensures that the slow-moving patterns no human would catch reading one shift’s data in isolation get surfaced.

Bringing it together

None of these six archetypes is more advanced than the others or inherently better. But CIOs can have an edge by choosing the one that the operational problem actually calls for.

Before you scope a single deployment, you should be able to look at a business problem and name its shape: Is this a question someone needs answered in the moment, or a process that should run the instant a trigger fires? Does the path need to be discovered, or is it known in advance and just waiting to be executed? Where, exactly, does human judgment have to stay in the loop, and where is it just friction?

Going forward, CIOs should start treating the architecture decision as the first design choice. Everything downstream — adoption, governance, trust — only gets easier if the architecture is the right fit.

This article is published as part of the Foundry Expert Contributor Network.
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Dozens of former Apple employees now working at OpenAI have been put on notice after Apple reportedly sent legal letters ordering them to preserve documents and communications relevant to its trade secrets lawsuit against OpenAI. 

The Financial Times reports that “around 40” employees have been targeted with these letters, which repeat Apple’s claim that its confidential information might have been exfiltrated, alleging “trade secret misappropriation and breach of contract.”  The letters also require them to arrange to meet with Apple’s lawyers.

The underlying lawsuit

This comes on the heels of Apple’s explosive lawsuit against OpenAI in which Apple accused the AI company (and former Apple Vice President Tang Tan) of extensive coordinated data theft. Tan was at Apple for 24 years and is now Chief Hardware Officer at OpenAI. 

Apple’s lawsuit is defined by claims OpenAI took a range of steps to pry confidential Apple data from existing Apple employees, including using information such as internal project code names, to gain even more knowledge during interviews. The company says the evidence it has presented so far is only the “tip of the iceberg” concerning OpenAI’s approach.

The lawsuit requests that OpenAI be prevented from using any Apple information during the development of its hardware. Apple is also seeking damages and suing two former employees for breach of contract for violating their employment agreements.

The letters are significant. They represent formal directives that require former Apple staff to preserve documents, messages, emails, and other communications that could be relevant to the case. The demand reflects Apple’s belief that the alleged misuse of confidential information could be more widespread across the competing company. What’s critical is that orders of this kind override any standard data destruction policy and deletion of the requested information becomes a legal offense. 

Why this matters beyond the protagonists

In making its move, Apple shows this is not a dispute about just one or two hires, but an attempt to constrain the movement of intellectual property between the two firms. With AI hardware emerging as the next major battleground in tech, the case could become a defining one; whatever resolution is eventually reached could define the extent to which former employees can carry experience and knowledge between competing firms. The case might also define what the line is between experience and knowledge and the sharing of trade secrets.

This is important, because modern hardware development relies on far more than just finished designs. Product design leans into supplier relationships, manufacturing assumptions, physics, extensive prototyping, and product-roadmap priorities. If courts treat those accumulated insights as protectable secrets, hiring between major technology companies could become far more legally sensitive.

The Jony Ive question

The case comes as Apple prepares to combat OpenAI in hardware. Its competitor is now working with legendary former Apple designer Jony Ive. Ive is not named in the litigation, but Apple will be keen to find out whether confidential product knowledge, design processes, or supply-chain insights have travelled with former staff into OpenAI’s device work.

Ultimately, for Apple, it’s about protecting its many blueprints for whatever hardware the company expects will come after the iPhone.

For its part, OpenAI has refuted Apple’s lawsuit, arguing that it is “not aware of any evidence” that the lawsuit has merit. “We have no interest in other companies’ trade secrets,” said OpenAI spokesperson Drew Pusateri. “We remain focused on building innovative technology that empowers people everywhere.” The company’s lawyers also claim it did respond to Apple’s initial inquiries on the matter.

What’s at stake

The significance of Apple’s newly-shared communication preservation orders is that if discovery uncovers evidence supporting Apple’s claims, the case could complicate OpenAI’s hardware plans and create unwelcome scrutiny ahead of any future public offering.

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Threat actors are now abusing an ordinary font file to deliver low-detection malware capable of stealing credentials and establishing persistence on compromised Windows systems.

According to a new research from Fortinet’s FortiGuard Labs, a global phishing campaign is actively using heavily obfuscated JavaScript and a Lua-based loader posing as a TrueType Font (TTF) file to evade security and drop RATs and infostealers.

A TTF file is a standard font file used by operating systems and applications to display text.

The campaign has been deploying malware families such as Agent Tesla, Remcos, XWorm, and a Snake Keylogger variant known as Best Private LOGGER, since at least late March 2026. “In these attacks, the threat actor impersonates several well-known companies, using the guise of business cooperation to launch phishing attacks,” FortiGuard researchers said in a blog post.

Talking about how a new attack technique seems to still rely on conventional phishing tricks, Shane Barney, CISO at Keeper Security, said, “The most sophisticated technical evasion in the world still starts the same way: someone opens an email from what looks like a trusted company and acts on it.”

“The obfuscation layers, the Lua loader disguised as a font file, the fileless execution chain – all of it exists to survive detection after that human decision has already been made, and organizations would do well to keep that in their sightline,” he added.

Business and payment-themed phishing lures used

According to the researchers, victims receive phishing emails impersonating well-known companies and using business collaboration or payment-related themes to trick recipients into opening compressed archives. These archives contain the obfuscated JScript that establishes persistence before dropping either a legitimate Autolt executable or a LuaJIT interpreter, along with a malicious script packaged within a .ttf extension.

The fake font file functions as a Lua-based loader that runs multiple de-obfuscation steps before decrypting and executing shellcode directly in memory.

“Security controls cannot treat a file extension as proof of file type or intent,” said Jason Soroko, senior fellow at Sectigo. “Each component (of the campaign) may appear less suspicious when reviewed alone, while the combined sequence leads to in-memory execution of RATs and infostealers.”

Some of the new variants, the researchers pointed out, are getting more sophisticated by introducing segmented shellcode encryption, Vectored Exception Handler (VEH)- based runtime decryption, AMSI and ETW bypasses, API unhooking, and other anti-analysis techniques designed to evade endpoint defenses.

The final malware payload is delivered using Donut shellcode, allowing execution without writing the payload to disk.

Protection requires targeted mitigations and routine security hygiene

Fortinet’s findings confirm the attackers’ endgame to be stealing credentials and maintaining long-term access. The malware families observed, including Agent Tesla, Remcos, XWorm, and Best Private LOGGER, are all focused on credential theft, surveillance, or remote access.

Barney said organizations should resist focusing exclusively on the loader’s technical sophistication and instead strengthen the systems attackers eventually want to compromise.

In his opinion, identity and access controls are what it comes down to, as signature-based detection often fails against the loader sophistication of this grade. “Limiting what any given set of credentials can reach, enforcing least privilege, requiring re-authentication for sensitive systems, and monitoring for anomalous session behavior will not stop every phishing email from landing, but they significantly constrain what an attacker can accomplish after one succeeds,” he explained.

Soroko, on the other hand, recommends focusing controls on the technical indicators. He urged organizations to restrict Windows Script Host, Autolt, and LauJIT wherever they are not operationally required, monitor for behaviors such as process injection, remote memory allocation, and shellcode execution, and use Fortinet’s published indicators for threat hunting.

The indicators of compromise (IOCs) Fortinet shared include the command-and-control (C2) addresses, file hashes, and filenames.

Soroko warned against relying solely on hashes or C2 infrastructure because the loader has changed over time. “The stronger approach is to detect the stable behavior across versions, then test controls against the complete chain,” he said.

Enterprises are facing two simultaneous challenges with AI: The risks associated with it are evolving faster than governance frameworks, while the business benefits are often difficult to measure.

This is one of the key findings of The Value of AI, a study commissioned by SAP from Oxford Economics. Now in its second year, the study surveyed 2,600 executives from 13 countries worldwide.

High expectations, limited preparation

On average, the enterprises surveyed plan to spend around $28 million on AI (up from $26.7 million last year), and expect a 21% ROI (from 16% last year). Expectations for AI agents are particularly high, with ROI expected to reach 17% this year, up from 10% last year. Furthermore, 83% of respondents worldwide said agentic AI has the potential to fundamentally transform their organization. On the other hand, only 3% of respondents said their enterprises were fully prepared for the deployment of AI agents.

There are gaps, particularly when it comes to governance:

• Only 12% of respondents said their skills or processes were able to govern AI effectively,
• 38% do not have human-in-the-loop processes in place for oversight of AI agents, and
• only 63% have established permissions and access controls for agents.

Other concerns include weaknesses in the organization of AI deployment, poor data quality, insufficient employee training, and the widespread use of shadow AI.

Governance is the bigger challenge

In an interview, Sean Kask, Chief AI Strategy Officer at SAP, commented on the study’s key findings.

Mr. Kask, in the study’s foreword, you write that companies are currently facing two challenges simultaneously: The risks associated with AI are evolving faster than governance, while the business benefits are often difficult to measure. Which of these poses the greater problem for companies?

Sean Kask: Measuring the business value of IT investments has never been easy. The same applies to AI. That’s why I currently consider the governance issue to be the greater challenge. While traditional governance principles and best practices for secure software development remain important even in the age of large language models and agent-based AI, entirely new risks are emerging at the same time.

For example, as soon as companies roll out AI on a broad scale, they suddenly discover hundreds or even thousands of so-called shadow agents that employees are using without central oversight. Or they find that a significant portion of the workforce is copying content into private ChatGPT accounts. Such risks often only become apparent once AI is already being used productively.

According to your study, German companies invest an average of nearly $40 million in AI, more than companies in all other countries surveyed. Why is that?

Kask: I was less surprised by the amount of investment than by the fact that, overall, the level of investment and the return on investment achieved have developed very similarly across the various countries. There’s no clear answer as to why Germany invests more. In part, it’s likely simply because costs here are higher than in India, for example.

However, we’re also seeing a high level of AI adoption among German companies. SAP has a dashboard that allows us to track how our customers are using AI features. Germany is among the countries with particularly high usage. Added to this are the strong industrial base and the political impetus from Europe, which are driving the use of AI. Accordingly, companies there are making targeted investments in building the necessary expertise.

According to the study, 47% of German companies are satisfied with the return on investment from their AI investments. At the same time, 77% say they are still far from realizing AI’s full potential. Isn’t that a contradiction?

Kask: No, we see this pattern worldwide. Companies initially invest in a few AI use cases and realize: This works; we’re creating added value. Accordingly, they’re satisfied with their investment.

But this is precisely what leads them to identify further use cases. They explore AI agents and want to utilize them as well. However, it is exactly at this point that many encounter new challenges in implementation and scaling.

The study therefore primarily highlights a learning curve: The more experience companies gain with AI, the greater their awareness of its previously untapped potential becomes.

According to the study, only 33% of companies surveyed have KPIs at the executive board level that are directly linked to the implementation of AI. In your view, which metrics should supervisory boards and CEOs definitely be tracking?

Kask: For us, a key indicator is employee enablement. How many employees have already successfully completed training or upskilling programs related to AI? Without the appropriate skills, AI adoption will fall short of its potential.

Transparency is equally important. Companies should know which AI agents are actually in use within their landscape. SAP offers the SAP AI Agent Hub for this purpose, which automatically discovers and inventories agents from SAP and third-party environments. Customers have already been able to identify thousands of agents this way, which highlights the need for centralized governance and transparency.

In addition, companies should have a complete overview of all AI use cases. A robust business case should be in place for each use case. We often see two extremes: Either the executive board is under pressure to implement AI as quickly as possible and allocates a lump-sum budget for this purpose. Or management initially takes a wait-and-see approach. This leads to independent pilot projects springing up throughout the company, with individual departments procuring their own tools and entering into their own contracts.

At SAP, we therefore follow a clearly structured selection process. Each idea first undergoes an assessment of its expected business value. We then examine technical feasibility, data availability, and ethical and governance aspects. From management’s perspective, it is crucial to maintain transparency regarding all ongoing AI projects at all times and to consistently prioritize them based on their business value.

Agents, too, need a ‘hire-to-retire’ lifecycle

Even with the introduction of dozens or even hundreds of AI agents, governance becomes increasingly complex. What capabilities do enterprise platforms need to manage AI agents securely and in a controlled manner at scale?

Kask: We make a conscious effort not to anthropomorphize AI too much. Nevertheless, the analogy is helpful: Agents require a complete hire-to-retire lifecycle. This begins with the detection and registration of an agent. It is then integrated into the enterprise environment, granted the necessary permissions, and given access to the data sources it needs to perform its tasks.

Observability is just as important. Companies must be able to track what an agent is actually doing in the system at all times. In addition, they should track key performance indicators: Is the agent achieving the desired results? How efficiently is it working? How many tokens does it consume? How many processing steps does it require for a task?

Ultimately, this involves several key components: a complete inventory of all agents, appropriate governance, risk, and compliance (GRC) mechanisms, transparency regarding agent behavior, and continuous monitoring. This is the only way to ensure that AI agents consistently operate within defined parameters and deliver the desired business value.

In your estimation, which business processes will companies actually delegate entirely to AI agents over the next two to three years?

Kask: Currently, such agents work particularly well in clearly defined use cases. SAP will release more than 50 (currently 34) specialized AI agents.

One example is periodic financial reporting. In this context, journal entries must be made based on numerous rules stored in documents, emails, or previous transactions. The agent analyzes these various sources of information, derives a recommendation from them, and suggests the appropriate journal entry to the user.

Based on what we’ve heard from customer projects, employees at medium-sized companies currently spend about twelve hours per month on these tasks. With the help of an AI agent, this effort can be reduced to two to three hours.

Another area of application is production planning. If delivery dates change or new orders come in at short notice, the entire production plan must be adjusted. It is precisely these kinds of complex optimization tasks that are ideally suited for AI agents.

In principle, there are virtually no limits to the narrowly defined business processes in which agents can be deployed. However, they will not operate completely autonomously at first.

Trust in AI begins with a stable foundation

Many companies still struggle to trust AI agents. After all, large language models operate probabilistically and can produce false information. This is particularly problematic in financial processes. How do you build trust?

Kask: Trust begins with a stable foundation. ERP systems remain the reliable system of record. They operate deterministically, contain the business logic, and hold the relevant company data. AI agents build upon this foundation. They do not replace it.

Equally important is the human-in-the-loop principle. Employees must be able to understand what the agent is doing, verify its results, and intervene if necessary. That’s why employee training also plays a crucial role. They must understand how generative AI works and where its limitations lie.

Of course, language models can hallucinate. At the same time, we must not forget that humans are not infallible either. The key lies in the collaboration between humans and AI. This allows us to improve both the efficiency and the quality of many business processes.

Another important component is transparency. Our global AI ethics policy, for example, stipulates that users must always be able to recognize when AI is involved. In Joule, it’s possible to trace which data sources the agent used and which steps it went through in reaching its decision. This traceability is an essential prerequisite for trust.

What distinguishes an SAP agent from a general AI agent that merely accesses an ERP system?

Kask: The key difference is that Joule and the SAP agents are directly embedded in the ERP system. There, for example, we’ve built a knowledge graph that describes the semantic relationships between all tables, business objects, and data fields.

To put this into perspective: The SAP S/4HANA Knowledge Graph is based on approximately 452,000 ABAP tables, 7.3 million data fields, and thousands of analytical views. The semantic relationships between these artifacts are modeled in the Knowledge Graph and made available for AI applications.

For example, if a user wants to view all open purchase orders, the agent does not first have to laboriously search for the relevant information. It immediately knows which tables and objects are relevant and also understands the relationships between a purchase order, a purchase requisition, the responsible approvers, and other business objects. As a result, the agent not only works much more precisely but also requires significantly fewer tokens because it can greatly narrow down the search space.

If, instead, one attempts to simply overlay AI onto an existing system or extract data from a relational ERP system, many of these relationships are lost. In a sense, this destroys the semantic context that is crucial for precise answers.

That is why we view the ERP system as an enormous strategic advantage. It has been the system of record for decades and contains roughly 50 years of codified business and process knowledge. This knowledge forms the foundation for what we call the autonomous enterprise. The agents build upon this knowledge and continue to develop it.

In the future, SAP agents will also communicate bidirectionally with agents from other providers via standards such as Agent-to-Agent (A2A).

According to your study, AI currently creates the greatest added value in decision-making, customer interaction, and gaining new insights, rather than in traditional productivity gains. Will this change the way companies justify AI investments in the future?

Kask: In our study, productivity was simply rated slightly lower than, for example, gaining new insights. In the long term, however, productivity remains the ultimate goal. Europe, in particular, has been suffering from comparatively weak productivity growth for years.

At SAP, we therefore first evaluate every new AI feature based on its specific business value. For all agents and AI features that we include in our AI Feature Catalog, we first conduct a value analysis. We ask: What benefit does the feature offer the user? Does it contribute to higher revenue? Does it increase productivity? Only then is it developed further.

At the moment, the greatest added value often still lies in consolidating information from structured and unstructured data sources and making it accessible via natural language. The next step, however, is to translate these insights directly into more efficient business processes. That is precisely where the greatest productivity gains will be realized in the future.

If you could give CIOs just one or two pieces of advice for the transition from generative AI to AI agents, what would they be?

Kask: In my view, the biggest mistake would be to try to transform the entire company all at once or to attempt to perfectly prepare all the data right from the start.

Instead, you should consider what kind of agent can create significant added value, and then implement it. Of course, this agent needs access to consistent and context-rich enterprise data. That’s exactly what we’re working on at SAP with technologies like the knowledge graph, which maps the semantic relationships within enterprise data.

In addition, with data products and the SAP Business Data Cloud, we provide tools that make data from various sources usable for AI agents. Thanks to zero-copy and data fabric approaches, information from legacy systems, Snowflake, or ERP systems can be consolidated without first having to extensively replicate the data. For a procurement agent, this makes it possible to provide exactly the relevant data for the specific use case.

The key point is this: Companies do not have to wait until they have fully migrated to the cloud or consolidated their entire data landscape. With the technologies available today, data can already be made usable for specific AI agents, managed in a controlled manner, and used to quickly generate initial business value. On the other hand, those who wait for the perfect starting point run the risk of falling behind.

This article is adapted from one first published by Computerwoche.

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