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Leadership1 distinct publisher3 min readPublished
A Harvard Business Review study finds early AI-driven cuts often missed their expected returns and exposed knowledge gaps, while a University of Pittsburgh analysis puts the average market reaction to those announcements close to zero.
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The order of operations is what makes these cuts expensive. According to Mark Ma of the University of Pittsburgh, managers who have spent heavily on AI face pressure to show a strong financial return, and cutting headcount to lower labour costs is the quick way to produce one [7]. The Harvard Business Review authors describe the same sequence from the other end, calling out companies that restructure on forecasts and market narratives about future AI capability rather than on evidence from their own active implementations [10]. A reduction lands in the quarter the spending needs justifying; the work it creates is discovered afterwards, by whoever is still there.
That discovery now has a rough size, and it comes from the buyers themselves. Since 55% of the HR leaders in the survey cited by the HBR authors judged their layoffs not worthwhile on oversight grounds [3], at most 45% of that group judged them worthwhile [15]. That is a majority of practitioners marking their own completed decision as a mistake, for a reason they did not price in advance.
The mechanism underneath is about the workforce that remains. Ma's team argues that AI-driven layoffs and the resulting job insecurity are destroying the conditions AI needs to make workers more efficient, because employee sentiment toward AI is among the strongest predictors of firm productivity when AI is in use [5]. The HBR authors put it in organisational terms: framing cuts as a response to AI undermines the psychological safety of the people left behind, who then disengage and steer clear of AI-driven productivity work [11].
Self-reported regret alone would be a thin basis for this conclusion, since HR leaders surveyed after a hard year might call almost any restructuring unwise. The market evidence, however, is not self-reported. Ma and colleagues examined stock reactions to these layoff announcements and found the average return close to zero, with proclamations of AI investment not consistently lifting share prices [8], drawn from hundreds of AI investment and layoff announcements by U.S. public companies over five years alongside millions of job satisfaction reviews [6].
The alternative on offer is slower and harder to put on a slide. Tom Davenport of Babson College, Faisal Hoque and Paul Scade argue for redesigning roles rather than cutting them [2], replacing top-down headcount targets with an examination of how work actually gets done and breaking roles into tasks to find where AI genuinely improves performance [12]. Their framing is that the question "what can AI do to reduce our headcount" starts in the wrong place, and that AI belongs in the general suite of capabilities used to hit business goals [13]. The trade-off is explicit: reduction produces a number this quarter and an oversight bill next quarter, while redesign produces no number this quarter at all.
Two gaps in the record deserve naming. The study's own wording is that "some" employers reversed course, with no count or rate attached [16], and the Revelio Labs finding relayed by Forbes is ambiguous as printed, reporting that companies blaming layoffs on AI grew AI headcount 11% over the prior two years and lagged industry peers in overall AI adoption anyway [9]. Forbes contributor Joe McKendrick's own gloss, that the cuts proved counterproductive and left organisations with reduced productivity and anemic growth [14], is a characterisation rather than a measured effect. What survives the discount is narrower and still decision-relevant: the people who ran these cuts say oversight was underestimated, and the market paid nothing for the announcement.
Ranked by verification strength, evidence, and original report placement.
A study published in Harvard Business Review states that 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.
The HBR study's authors are Tom Davenport of Babson College, Faisal Hoque and Paul Scade, and they argue that a more productive approach to ingraining AI into the workplace is redesigning job roles rather than cutting them.
A majority of HR leaders in one survey, 55%, said their layoffs were not worthwhile because AI required more human oversight than expected.
One in three HR leaders reported losing critical skills and expertise along with their laid-off employees.
Mark Ma of the University of Pittsburgh states that AI-driven layoffs and the resulting job insecurity are actively destroying the conditions needed for AI to make workers more efficient, and that the cuts damage employee sentiment toward AI, which is one of the strongest predictors of firm productivity when AI is used.
Ma reports that he and colleagues analysed millions of job satisfaction reviews, thousands of reports of corporate financial performance, and hundreds of AI investments and layoff announcements made by U.S. public companies over the past five years.
Distinct publishers with included, body-backed reporting in this cluster.
forbes.com
1 article · August 30, 2026
Evidence-backed comparisons of source perspectives and observed adoption signals. Read the methodology
Which Builder, Operator, and Investor concerns the observed source mix emphasized—not a truth score.
Evidence, demonstrated adoption, hype gap, incentives, and confidence are assessed independently, each on its own current evidence. How these are measured.
Three studies, all at arm's length
Nothing in this story was checked by the outlet carrying it. The Harvard Business Review findings, Mark Ma's Pittsburgh analysis and the Revelio Labs count all reach us as quotations inside a Forbes column, with no link, venue or method behind any of them. The direct quotes are clean; the paraphrase is where it frays — the Revelio sentence fuses two clauses so tightly that the 11% growth figure loses its subject, and neither survey supplying the 55% and one-in-three numbers is named.
Reversals asserted, never counted
The claim that employers are backing away from AI-justified cuts rests on two words — 'some employers' — and the story never returns to size them. What is quantified sits one step to the side: oversight burden and lost expertise as reported by HR leaders, and Revelio Labs' finding that the loudest AI-blaming employers were adoption laggards anyway. Those are reasons a reversal would make sense, not evidence that one is happening at scale.
Verdict outruns the arithmetic
'Counterproductive to the extreme' is the column's own phrase, and it is asked to cover a body of evidence that measures something narrower: an unnamed survey's regret rate, a one-in-three knowledge-loss figure, and an average market reaction near zero. Note what near zero actually says — not that these layoffs destroyed value, but that announcing them moved nothing. The direction of the reporting is defensible; the intensity of the framing is borrowed against research the reader cannot inspect.
Advice with credentials attached
The prescription and the people selling adjacent expertise are the same people: Forbes introduces two of the three authors by their book titles in the sentence that recommends redesigning roles instead of cutting them, and the third is among the most-published names in enterprise analytics. That does not make the task-level argument wrong — it is the least contested part of the piece — but a study whose conclusion is 'this needs closer study of how work gets done' has a natural constituency, and the column relays it without noting as much.
Plausible in aggregate, uncheckable in parts
Three independent research groups pointing the same way is worth something, and the failure modes described — cutting on forecasts, then discovering the oversight bill — match how these decisions are made. But we hold this at arm's length because every strand comes through one contributor, one relayed statistic is broken on arrival, and the reversal in the framing is never counted. Read it as a well-aimed hypothesis with three citations to go verify.