by Faisal Hoque, Tom Davenport and Paul Scade
If leaders default to headcount reduction, the result will be an organization that is smaller but not smarter.
Summary. AI is beginning to reshape workforce decisions, but many companies are moving faster than the evidence warrants. Early AI-driven layoffs have often failed to deliver expected returns, exposed gaps in organizational knowledge, and created new demands for human oversight—prompting some employers to reverse course. A more effective approach starts not with headcount targets but with the work itself: breaking roles into tasks, identifying where AI can genuinely improve performance, and redesigning processes around the right mix of human and technological capabilities. That also means aligning AI adoption with business strategy, strengthening leaders’ understanding of the technology, preserving areas that depend on human judgment and accountability, and making workforce changes gradually enough to adapt as AI capabilities evolve.
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Precisely how AI will impact the workforce in the short to medium term is unclear. Many layoff announcements are better understood as AI-washing: ordinary restructuring initiatives packaged in AI language to reassure investors that leaders have a grip on the coming technological change. Goldman Sachs estimates that AI reduced monthly U.S. payroll growth by only around 16,000 jobs over the past year, adding roughly 0.1 percentage points to the unemployment rate—a drag that includes slowed hiring as well as outright job elimination. These numbers may well increase, but for now, we are much earlier in the process than the prevailing narrative suggests.
The fact that we are early, however, and that the scale of layoffs is smaller than the news cycle implies, does not mean all these layoffs are wise. Many companies are already making real workforce changes in response to AI—and they are frequently getting them wrong. An international survey of 1,000 business leaders published in April 2025 found that 55% of businesses that laid off workers due to AI implementations admitted they had made wrong decisions around staffing. In a more recent survey of 600 HR leaders who made AI-driven layoffs in the 12 months to February 2026, only 8.4% said the restructuring had delivered as promised and that they would do the same again. Costly reversals are already happening. Gartner predictsthat half of companies that cut customer service staff due to AI will rehire workers. Forrester, meanwhile, expects that half of all AI-attributed layoffs will be reversed, although the replacement positions may be offshored or come with lower salaries.
These figures are not outliers. They are supported by other reporting that point in the same direction: companies are often not getting AI rightsizing right.
The problem is not simply that companies are moving too quickly or cutting too deeply. It is that many are approaching AI-driven restructuring from the wrong starting point: headcount rather than work. A more durable approach begins by understanding how AI changes individual tasks and business processes, then redesigning the organization around the capabilities—human and technological—needed to execute them. As we will argue, that requires companies to rethink how they define work, connect AI decisions to business strategy, build a realistic understanding of what the technology can and cannot do, and preserve enough flexibility to change course as both the technology and the evidence evolve.
Where Companies Go Wrong
Most companies that have already attempted AI-driven workforce changes are making one or more of a recognizable set of mistakes.
- Anticipatory cuts. Many companies are restructuring their workforces based on forecasts and market narratives about future AI capabilities rather than on evidence from their own active implementations. A recent survey of over 1,000 global executives found that only 2% of organizations had made large headcount reductions based on actual AI implementation, while 60% had laid off people or reduced hiring with an expressed connection to AI. Block’s elimination of nearly half its workforce in February 2026 is the highest-profile example: CEO Jack Dorsey attributed the cuts to AI but framed them as a bet on where the technology is heading rather than what the technology could do for the company right now. (He also said some of the redundancies were the result of overhiring due to the pandemic.)
- Cutting without understanding AI capabilities. Even when companies move beyond anticipation to decisions rooted in current AI implementations, layoffs are often made without adequate understanding of AI’s capabilities and limitations in context. 54% of HR leaders said they would have made better choices with a more informed understanding of what AI could do. Nearly a quarter admitted that layoff decisions were made without first testing the business case. And 55% said their layoffs were not worthwhile because AI required more human oversight than expected.
- Poisoning the internal culture. A survey of 5,400 U.S. employees found that engagement drops to 44% at organizations where layoffs (for any reason) have occurred, compared to 51% overall, and that 58% of unaffected employees become more likely to look elsewhere. Framing cuts as a response to the emergence of AI can undermine remaining employees’ psychological safety, leading them to disengage from their jobs and to avoid experimenting with AI-driven productivity. AI-washing risks generating the same cultural damage without delivering any corresponding transformation at all.
A recent Gartner survey found that 80% of companies made reductions to their workforces when implementing new “autonomous business capabilities,” but that there was no correlation between lower employee numbers and return on the investments. Until organizations measure success by decision quality, organizational capability, and learning rather than by cost savings and headcount, they will continue to make the same mistakes.
[Image: Marcos Osorio/Stocksy]
Full article @ Harvard Business Review.
