In the same email that announced record revenue earnings and double-digit growth, Cisco Systems notified staff May 13 that it was cutting thousands of jobs and restructuring its business to focus on AI infrastructure. Layoffs of about 4,000 people began the next day.
“We don’t always have the exact resources that we need going forward in the right places,” Cisco CEO Chuck Robbins said on a call with analysts. “That’s really what this is about versus savings. The companies that will win in the AI era will be those with focus, urgency, and the discipline to continuously shift investment toward the areas where demand and long-term value creation are strongest,” he said.
Cisco’s layoffs reflect a broader trend across the tech industry, as companies downsize headcount to free up cash for AI investment. Executives from some of the world’s largest companies — including Amazon, IBM, Meta, and Oracle — have increasingly linked workforce reductions, hiring slowdowns, and organizational restructuring to the rise of AI.
In earnings calls and investor presentations, leaders have framed leaner workforces as preparation for an AI-driven future where software agents, automation tools, and generative AI systems absorb growing portions of knowledge work. The trend has been most noticeable in tech, but the same sentiment has been expressed at some of the largest financial, retail, and professional services companies.
Even though there is no solid measure for AI-driven layoffs, outplacement firm Challenger, Gray & Christmas track public layoff announcements and estimated that about 55,000 cuts in 2025 were tied directly to AI adoption. And AI is listed as a leading reason for job cuts in 2026, with 21,490 planned layoffs in April attributed to artificial intelligence and automation efforts, Challenger reported.
Yet beneath the headlines lies a more complex reality — one that many labor economists, HR leaders, and management scholars say is being oversimplified.
Some layoffs are clearly connected to automation, while others appear driven by a desire to free up cash for AI investments and still others remain pandemic-era corrections, disguised in innovation language that resonates with investors. At the same time, in many organizations, the actual productivity gains from AI remain modest, difficult to measure, or largely hypothetical.
“Disentangling all of these things to identify and isolate root causes becomes tricky,” said Justin Ladner, senior labor economist at SHRM.
“The real story can be hard to pin down,” said Thomas Davenport, distinguished professor of IT and management at Babson College in Wellesley, Mass. “AI is behind at least some layoffs, but these are almost completely in anticipation of AI’s impact. In other words, the job losses and slowed hiring are real, even though companies are still waiting for generative AI to deliver on its promises.”
The New Corporate Logic
Historically, layoffs signaled distress: declining revenue, weak demand, or recessionary pressure. Today, many companies announcing cuts remain highly profitable. Investors often reward them for reducing headcount aggressively while simultaneously increasing AI spending.
That shift reflects a broader transformation in how companies think about labor.
“Corporate leaders have come to see large teams as impeding progress, not helping it,” said Kenny Pyle, HR technology lead analyst at SHRM. Pyle said invoking AI creates a particularly attractive narrative for investors.
“Leaning on AI as a reason for cutting staff can be absolutely a more attractive reason to convey [coming layoffs] because it allows two positive messages instead of a negative one,” he said. “One — that the company is technologically advanced and able to achieve something most companies are still chasing in terms of AI efficiency gains. And two — it says the company is at a stage where they are becoming more efficient and should be able to reduce their operating expenses.”
In many cases, analysts say the layoffs are less about AI replacing workers today and more about reallocating capital toward AI infrastructure tomorrow.
That dynamic is especially visible among enterprise software firms. Amazon, Microsoft, and Oracle are simultaneously trimming headcount while dramatically increasing spending on AI data centers, cloud infrastructure, and chips.
“Payroll is being converted into capital expenditure,” said Evan Sohn, managing director at Revelio Labs in New York City. The financial incentives are powerful. Wall Street increasingly rewards firms that demonstrate “AI readiness,” even when the operational impact remains unclear, he said.
Jason Schloetzer, faculty affiliate at the Georgetown University McDonough School of Business Psaros Center for Financial Markets and Policy, said executives often frame reductions through the lens of innovation because it signals momentum.
“CEOs may say these cuts are because of AI, when they really mean to say that they don’t have enough cash flow generation to fund the investments they would like, so they need to trim headcount to free up capital,” he said. For investors, he added, “reduced expenditures are boosts in profit. The reason doesn’t matter.”
The Rise of ‘AI-Washing’
As AI investment became the dominant corporate narrative, a new phrase has been introduced: “AI-washing,” referring to companies overstating the role AI plays in layoffs or business transformation.
Research firm Forrester warned earlier this year that many organizations announcing AI-related cuts “do not have mature, vetted AI applications ready to fill those roles.”
J.P. Gownder, vice president and principal analyst at Forrester, believes the disconnect between rhetoric and operational reality is substantial.
“AI washing is pervasive right now,” Gownder said. “Some of the organizations and leaders who are claiming layoffs due to AI have very self-interested points of view.”
He pointed to the gap between public messaging and actual deployment maturity. “If you are laying people off without a mature, ready-to-go AI agent to do the work, you are not laying off people because of AI,” Gownder said. “You are laying people off for financial reasons and then imagining that at some future date, AI may be able to do the work.”
That distinction matters for HR leaders trying to separate long-term transformation from short-term cost cutting.
Gownder said many companies are using AI language to justify decisions that were already likely because of economic uncertainty, overhiring during the pandemic, and pressure to improve margins. “We know that AI is not yet doing the work to justify the level of layoffs that have been happening,” he said. “When you try to identify the very specific jobs and roles impacted by AI the picture starts to get cloudy.”
Peter Cappelli, professor of management at The Wharton School at the University of Pennsylvania, said that the financial case for AI-driven layoffs is often overstated.
“The companies that are laying off are not struggling,” Cappelli said. “The reason companies — especially the rich tech companies — are cutting jobs is not because they are in financial trouble or that AI has taken jobs or that they know already which jobs will be taken over.”
Instead, he points to investor pressure and changing attitudes toward labor. “The answer begins with pressure from investors who always want them to cut headcount. It makes revenue per employee jump up quickly and many investors seem to feel that employers were coddling employees during and immediately after the pandemic. No one wants to say that out loud and risk public backlash,” he said.
Displacement by Automation Is Happening – But Narrowly
Even skeptics of AI-washing acknowledge that some forms of work are already being automated.
The clearest early impacts are emerging in repetitive, rules-based, high-volume tasks — particularly customer service, administrative support, data processing, and portions of software development.
Ladner said some company layoff announcements are credible precisely because they identify narrowly defined tasks with plausible automation pathways.
“There are examples like the IBM announcement laying off a couple of hundred HR professionals doing routinized tasks, to be replaced by AI,” Ladner said. “That is tailored and plausible.”
Ladner said that announcements where half of the workforce is laid off [Block cut roughly 4,000 jobs in February — nearly 40% of the company’s workforce] or when CEOs forecast extreme levels of displacement are “over the top,” he said.
According to Eva Johnson, SHRM-SCP, HR executive advisor at Gartner, the most vulnerable roles tend to share common characteristics. “Roles impacted the most are high volume, rules-based, entry or admin type roles,” Johnson said. “That is what AI does really well.”
Yet even there, she said, the broader story is more about role redesign than elimination. “A big part of the story is not about the layoffs but more about role consolidation and transformation and hiring avoidance — not backfilling roles like they used to,” she said.
That nuance is critical. Many organizations are reducing headcount indirectly through hiring freezes, attrition, and delayed recruiting rather than mass replacement.
The Productivity Paradox
One of the biggest tensions in the AI layoffs narrative is that the promised productivity revolution has not yet fully materialized. Executives continue discussing transformational efficiency gains, yet many researchers say actual enterprise-wide returns remain elusive.
“Companies are investing heavily in artificial intelligence, yet few can point to sustained financial returns,” said Daniel Schmeltz, managing director with Alvarez & Marsal’s Corporate Performance Improvement practice in Houston.
“AI has rarely translated into measurable improvements in EBITDA, cash flow, or return on invested capital,” he said. The problem, he argues, is organizational rather than technological.
“AI is being introduced without the operating-model redesign required to turn capability into value,” Schmeltz said. “Until AI is embedded into how decisions are made, owned, and governed, it will continue to generate visible activity but little lasting impact.”
Schloetzer sees a similar gap between expectations and reality. “I’ve talked to dozens of senior executives at large organizations across industries and they say that the gains due to AI use are on the margin,” he said.
He added that research tasks that once took several hours may now take 30 minutes. Tedious spreadsheet work can be automated. Drafting communications is faster. But those incremental efficiencies do not necessarily justify massive workforce reductions.
“Organizations are not seeing gains from AI that would lead to tens of thousands of layoffs,” Schloetzer said.
Gownder agreed. “The vast majority of over 200 clients I have spoken with have struggled to define and quantify any noticeable financial return on investment from AI technology,” he said. “When you look in detail at how complex most jobs are, it is very challenging to create AI solutions that can do entire jobs. There are exceptions. But for the most part, jobs are comprised of a wide variety of tasks that cannot be completely automated with AI today.”
That disconnect between expectation and measurable value may ultimately create new tensions for leadership teams.
The Human Risks of AI-Led Restructuring
For HR executives, there is another risk: presenting the AI-driven restructuring narrative may profoundly impact employee trust and morale, technology adoption, and employer reputation.
Davenport warned that premature AI layoff narratives can backfire internally. “Announcing layoffs or slower-to-no hiring because of AI may be appealing to the press or to investment analysts, but it has important negative consequences,” he said.
Schloetzer added that many workers feel deeply demoralized when executives imply that machines can replace them. Employees who believe AI is inevitably coming for their jobs may become less willing to engage with it, he added.
There are also operational risks. Some organizations that aggressively reduced human support functions have since walked back portions of those decisions. Klarna, often cited as a leading AI-first organization, later acknowledged that relying too heavily on automation degraded service quality and required reinvestment in human support staff. Duolingo is another example.
Davenport said these episodes illustrate why anticipatory cuts can become strategically dangerous. The central question facing executive teams is no longer whether AI will affect work. The real questions are how quickly the technology matures, which tasks prove reliably automatable, and whether organizations can redesign work thoughtfully enough to capture value without destabilizing culture, capability, and trust.
What Does the Future Hold?
Most experts believe the workforce displacement due to AI transition will unfold more gradually than doomsday forecasts suggest.
The World Economic Forum’s latest projections conclude that 92 million jobs could be displaced globally by 2030, offset by an estimated 170 million new ones. But that optimistic math depends on retraining programs that, so far, don’t exist at scale in any country.
SHRM researchers found that 15% of U.S. employment is at least 50% automated right now, a share that translates to about 23.2 million jobs. Additionally, in 2025 SHRM found that 12.6% of roles in the U.S. were at high risk, or very high risk, of displacement due to the proliferation of AI-powered tools.
Forrester projects that approximately 6% of jobs will ultimately be lost to AI and automation by 2030 — significant, but far from apocalyptic. “That represents about 10.4 million jobs out of a 160 million job economy,” Gownder said. “As a comparison, about 8.7 million jobs were lost during the Great Recession. It’s not like half of all jobs are going away.”
Ladner likewise expects adoption to be uneven and constrained by economics, regulation, consumer behavior, and organizational risk tolerance. “There are costs and risk to replacing people with AI at scale,” he said. “Apocalyptic displacement — I don’t think so. There are so many types of jobs where even if the technology was perfect, it is hard to imagine the adoption of that technology not having significant barriers. I would guess that most companies will take a more measured approach.”
Johnson believes the future workforce will increasingly blend technical fluency with uniquely human capabilities. Competitive advantage may come from how effectively organizations redesign roles, retrain employees, and integrate human judgment alongside AI systems.
“So many people are thinking about this as a technology issue; this is very much a human issue,” she said. “The key is keeping humans continuously skilled to lead and make decisions, augmented by the technology.”
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