The spate of cuts and layoffs over the past two years, especially those that place the direct blame on artificial intelligence, has turned out to be counterproductive to the extreme. Organizations turning to AI to ostensibly replace human workers have not seen the magical transformation they expected. Instead, they have seen reduced productivity and anemic growth opportunities.

The pushback has been underway.

"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," according to a study recently published in Harvard Business Review.

A far more productive approach to ingraining AI into the workplace consists of redesigning, not cutting, job roles, as explained by the study’s authors, Tom Davenport of Babson College, Faisal Hoque, author of Transcend: Unlocking Humanity in the Age of AI , and Paul Scade, author of Reimagining Government: Achieving the Promise of AI .

Other research is bearing this out. “AI-driven layoffs and the resulting job insecurity are actively destroying the very conditions needed for AI to make workers more efficient," echoes a study published by Mark Ma at the University of Pittsburgh. "In fact, these job cuts damage employee sentiment toward AI – which is one of the strongest predictors of firm productivity when AI is used.”

Contradictions abound. Another study out of Revelio Labs found companies that blamed layoffs on AI grew AI headcount 11% over the prior two years lagged their industry peers in overall AI adoption anyway.

In his study, Ma reports working with colleagues analyzing “millions of job satisfaction reviews and thousands of reports of corporate financial performance, as well as hundreds of AI investments and layoff announcements made by U.S. public companies over the past five years.”

There is a correlation between AI investment announcements and AI-induced job cuts. “After investing heavily in AI, managers face pressure to show a strong financial return," Ma and his colleagues discovered "A quick way for managers to help businesses realize that anticipated return is by cutting headcount and lowering labor costs.”

Still, when Ma and his associates "examined stock market reactions to these layoff announcements, the average return was close to zero. Proclamations of AI investment don’t consistently boost a company’s share price.”

Davenport and his HBR co-authors explain where companies tend to go wrong with their perceptions of AI layoffs:

  • 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."
  • Cutting without understanding AI capabilities. “Layoffs are often made without adequate understanding of AI’s capabilities and limitations in context." A majority of HR leaders in one survey, 55%, "said their layoffs were not worthwhile because AI required more human oversight than expected.”
  • Poisoning the internal culture. “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.”

Avoid headcount targets when it comes to rethinking organizations moving into AI. This includes “breaking roles into tasks, identifying where AI can genuinely improve performance, and redesigning processes around the right mix of human and technological capabilities," Davenport and his HBR co-authors state. They provide guidance on avoiding the AI layoff trap:

  • Replace top-down headcount targets with a closer examination of how work gets done: This calls for an examination of “where AI can genuinely create value, and where human capabilities remain essential.”
  • Redefine the unit of work: The roles being evaluated for automation extend much deeper than purely transactional tasks. “Workers at every level typically communicate, collaborate, and make the kinds of judgments that are hard to reduce to algorithmic functions," the co-authors state. “One in three HR leaders reported losing critical skills and expertise along with their laid-off employees.”
  • Develop a business strategy, not an AI strategy: “Asking ‘What can AI do to reduce our headcount?’ starts from the wrong place. Treat AI as an integral part of the general suite of capabilities available to deliver on goals."
  • Understand what AI can do: “When leaders do not understand what AI can do, they either delegate workforce decisions to technical teams who lack the business context, or they make those decisions themselves based on flawed assumptions.”
  • Understand what AI cannot do: “Some functions are inherently human because they require moral agency and accountability," Davenport and his co-authors point out.