Why Big Tech’s AI-Driven Layoffs Have Failed to Deliver Value

AI-driven automation and efficiencies have now become the leading cause of layoffs at tech companies across the globe. According to layoffs tracker TrueUp.io, more than 144,000 tech workers were laid off in the first five months of 2026, with the projected job cuts to cross 360,000 by the end of the year. 

And yet, despite the sweeping scale of these cuts, something is not adding up.

A survey by Gartner, published in May 2026, found that approximately 80% of organisations that had piloted or deployed autonomous business capabilities had reduced their workforces in tandem. The 350 companies surveyed had all reported annual revenues of at least $1 billion, and all had already deployed AI agents, robotic process automation, digital twins, and intelligent automation in live business environments.

However, when Gartner examined the returns these organisations were generating from their AI investments, the numbers did not line up with the scale of layoffs. Workforce reduction rates were nearly identical among companies reporting strong returns on their AI investments and those experiencing modest gains or outright negative outcomes. 

In other words, cutting jobs to fund AI did not necessarily give any better returns. 

Misplaced Confidence

Boards and chief executives are under enormous pressure to demonstrate that the billions being poured into AI are generating measurable returns. Headcount reduction is visible, immediate, and quantifiable. But Gartner's analysts argue that this appearance of efficiency is a mirage. 

"Many CEOs turn to layoffs to demonstrate quick AI returns; however, this disposition is misplaced," Helen Poitevin, Distinguished VP Analyst at Gartner, tells AIM. "Workforce reductions may create budget room, but they do not create returns. Organisations that improve ROI are not those that eliminate the need for people, but those that amplify them by aggressively investing more in skills, roles, and operating models that allow humans to guide and scale autonomous systems."

The distinction Poitevin draws is critical. Budget room and return on investment are not the same thing. One is a cost reduction that frees up capital. The other is a genuine increase in business value. 

The Evidence Behind the Warning

Gartner found that not only did layoff rates fail to distinguish high-ROI companies from low-ROI ones, but in several cases, the organisations that cut less actually performed better on returns. 

When asked why Gartner considers this an unsustainable strategy, Poitevin says, "We contrasted the strategies of organisations that were leading in gaining higher ROI from their AI investments and those that were lagging, with much lower or even negative ROI. Those gaining higher ROI were not laying off people at a higher rate. Indeed, those with more ROI were more likely to invest in upskilling initiatives for employees to build AI agents or autonomous systems, transition paths for skill sets and roles impacted by AI, and new roles to orchestrate multi-agent systems.” 

“When organisations focus on pre-emptive layoffs with the hope or belief that AI can fill the gaps, they miss many other forms of ROI that would set the foundations for ongoing success through growth or more effective use of resources," she comments.

Indian IT at the Crossroads

Nowhere is this tension more acutely felt than in India's technology sector, which employs millions and forms the backbone of global IT services delivery. 

Tata Consultancy Services, one of India's largest private employers, shed more than 23,000 employees in FY26 even as it announced aggressive expansion into AI infrastructure. Oracle, too, initiated a restructuring affecting tens of thousands globally, with roughly 12,000 roles in India in the crosshairs.

However, Sateesh Seetharamaiah, CEO of EdgeVerve, a wholly-owned subsidiary of Infosys, believes that companies need to treat AI as a capability multiplier. “AI has generated more productivity for businesses, but ultimately, there has to be a human being to take accountability for the jobs getting done," he affirms.

Sumeet Mathur, Senior Vice President and Managing Director at ServiceNow India, similarly argues that the right response to AI disruption is not displacement but reinvention. Speaking in the context of research projecting that agentic AI will redefine more than 10 million roles in India by 2030, Mathur urges enterprises to "equip employees not only to review AI outputs, but to actively interrogate the processes and data that shape them."

Gartner's analysts also observed that some organisations that moved too aggressively were subsequently forced to rehire employees they had recently made redundant. Companies are, in effect, making permanent workforce choices based on where AI sits at a single moment in time, even as the technology shifts beneath them.

Poitevin described this pattern as "a kind of one-time exercise by many in small amounts—but not what translates to getting full ROI from their AI investment."

What Good AI Strategy Actually Looks Like

Gartner's research identifies a specific set of practices that distinguish high-ROI organisations from those struggling to generate returns.

The first shift is conceptual. Executives need to stop measuring AI performance solely through the lens of headcount reduction and begin tracking the far richer value that AI can generate across a business. This means examining faster time to product, shorter contract cycle times, improved management of financial assets, and better customer service outcomes. These are harder to communicate in a quarterly earnings call, but they reflect genuine business value in a way that a headcount number simply cannot.

The second shift is operational. “Companies need to manage the cost of AI agents with the same rigour they apply to any other significant expenditure. This means measuring value generated per agent cost, agent cost per completed task, and context utilisation rates that examine task success, accuracy, and overall return,” says Poitevin.

The third—and perhaps most transformative—shift is cultural. "Instead of trying to determine what tasks can be automated, look at how employees can use AI to serve their stakeholders better, or meet quality objectives better," she adds.

The frame moves from replacement to amplification, and the results, according to the data, are fundamentally different.