Companies are spending heavily on artificial intelligence while simultaneously announcing waves of layoffs attributed to that same technology. The expectation is straightforward: AI makes workers more efficient, so fewer workers are needed, and costs fall. Research from a University of Pittsburgh professor of business administration challenges that logic at its foundation, and the findings point to a mechanism that most corporate AI strategies have failed to account for.
The Gap Between Investment and Results
An Atlanta Federal Reserve study found that roughly 90% of executives believe AI has not yet boosted productivity at their companies. That figure is striking on its own. What makes it more significant is the context: U.S. companies have poured substantial sums into AI adoption, and the gap between spending and measurable returns is widening.
Research analyzing millions of job satisfaction reviews, thousands of corporate financial performance reports, and hundreds of AI investment and layoff announcements from U.S. public companies over a five-year period identified a clear pattern. As AI investment announcements increase in frequency, so do announcements of job cuts attributed to AI. This correlation is not treated as coincidental in the research. It reflects a deliberate corporate strategy in which workforce reduction is built into the AI investment thesis from the start. Some companies studied even began laying off employees before committing capital to AI, using headcount reduction as a way to fund future technology spending.
The market, notably, has not rewarded this approach. When researchers examined stock market reactions to layoff announcements tied to AI, the average return was close to zero. For more than half of these events, the reaction was negative or close to zero. Block, a financial technology platform, was cited as an exception where stock prices rose on news of AI-related staff reductions, but that outcome was far from typical. The muted market response suggests that investors, at least in aggregate, are pricing in costs that the announcements do not acknowledge.
The Hidden Cost That Undermines the Strategy
Here is what most coverage of the AI productivity debate misses. The research did not just find that layoffs fail to generate returns. It found that AI-driven layoffs actively damage the conditions required for AI to work in the first place.
To understand employee perception, the researchers analyzed millions of reviews on Glassdoor, a workplace review platform, focusing specifically on AI-related comments. Those comments were substantially more negative than the overall tone of reviews on the platform. Workers cited job security fears as the dominant concern, well ahead of other issues such as inadequate training, limited opportunities to upgrade skills, and doubts about whether AI genuinely improves their work.
The research then tested what happens to employee sentiment toward AI when companies announce AI-related layoffs. The result was a sharp decline. Employees who have watched colleagues lose jobs to AI, or who fear they will be next, are actively resisting the tools they are being asked to adopt. This matters because employee sentiment toward AI turned out to be one of the strongest predictors of firm productivity when AI is deployed. Anti-AI sentiment among workers lowers productivity and offsets the efficiency gains the technology is supposed to deliver.
A Reuters/Ipsos poll cited in the research found that half of Americans fear AI could put someone in their household out of work. That level of anxiety does not stay outside the office door.
Management sentiment told a different story. Analysis of approximately 10,000 earnings-call transcripts showed that executives discuss AI in consistently optimistic terms. That optimism, however, bore no significant relationship to actual productivity outcomes. The people whose attitude toward AI most determines whether it delivers value are not the ones speaking on earnings calls.
What This Reveals About How Organizations Actually Change
The deeper issue here is about how technology adoption works inside organizations. AI tools do not generate productivity on their own. They generate productivity when the people using them engage with them, adapt their workflows, and develop genuine competence over time. That process requires a degree of psychological safety: workers need to believe that becoming more capable with AI will benefit them, not accelerate their own displacement.
When companies frame AI as a cost-cutting mechanism and then demonstrate that framing through layoffs, they send a signal that is impossible to walk back with internal communications or training programs. The rational response for an employee in that environment is to minimize engagement with AI tools, not maximize it. The technology becomes something to be managed around rather than worked with.
This is what the research describes as a self-defeating strategy. The efficiency gains that justify the investment depend on employee adoption. The layoffs that are meant to accelerate the financial return undermine that adoption. The two parts of the strategy work against each other.
The research suggests a different path: companies that share AI gains with employees, invest in skills development, and create genuine opportunity rather than using AI as cover for headcount reduction are better positioned to see actual returns on their AI spending.
In Short
Ninety percent of executives report that AI has not yet improved productivity at their companies, and research points to a specific reason: AI-driven layoffs generate job insecurity, which generates resistance to AI among workers, which cancels out the efficiency gains the technology is supposed to create. Management optimism about AI, however consistent, does not predict productivity outcomes. Employee sentiment does. Companies treating workforce reduction as the financial mechanism through which AI investment pays off are, according to this research, undermining the very conditions that would allow it to pay off at all.
Based on reporting from The Conversation - Technology.