Nearly 90% of executives report AI has not yet boosted productivity, despite heavy spending.
Research links this disconnect to AI-driven layoffs, which increase employee fear and erode morale—one of the strongest predictors of firm productivity.
Layoffs announced alongside AI investments triggered sharp declines in worker sentiment, offsetting efficiency gains the technology was meant to deliver.
What happened
An Atlanta Federal Reserve study found that approximately 90% of executives believe AI has not yet increased productivity at their companies, even as firms pour billions into AI adoption. Research analyzing millions of employee reviews, corporate announcements, and financial data over five years revealed a clear pattern: as AI investment announcements rise, so do announcements of AI-driven job cuts.
Why it matters
The research identifies a hidden cost undermining AI returns—employee fear and negative sentiment. Analysis of Glassdoor reviews showed AI-related comments were much more negative than overall employee reviews, and there is a strong association between employee sentiment toward AI and firm productivity. When companies announce layoffs tied to AI, employee sentiment toward the technology drops sharply, actively offsetting the efficiency gains AI is supposed to deliver.
What to watch
Stock market reactions to layoff announcements were close to zero on average, and negative or near-zero for more than half of events, suggesting significant hidden costs. The research suggests managers should focus on building employee trust—investing in skills and expanding opportunity—rather than using AI as justification for cuts, to actually realize AI's promised productivity benefits.
Ask the AI about this article →
The core paradox facing business leaders stems from a mismatch between how companies expect AI to work and how it actually unfolds in practice. Managers, facing pressure to justify heavy AI investments through short-term stock price gains, have adopted a strategy that treats headcount reduction as integral to their AI deployment. The logic is straightforward from a financial standpoint: if AI makes workers more efficient, fewer workers should be needed. Some companies even began laying off staff before investing in AI, to free up capital for future AI spending.
However, the research reveals this approach is self-defeating. Employee sentiment toward AI emerges as one of the strongest predictors of whether firms actually achieve productivity gains from the technology. When workers watch colleagues lose jobs to AI or fear they will be next, they develop anti-AI sentiment that actively undermines the efficiency improvements the technology is supposed to deliver. Analysis of earnings-call transcripts showed management consistently expressed optimism about AI, yet this optimism bore no significant relationship to actual productivity outcomes—a stark contrast to the measurable impact of employee sentiment. The stock market's muted response to layoff announcements (average return close to zero, negative for more than half) suggests investors recognize these hidden costs are substantial enough to offset the anticipated gains.
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