Employee using AI

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In a difficult business environment, artificial intelligence can look like an obvious answer for executives seeking faster work, lower costs and new efficiencies. But as AI moves into everyday business use, the more important question is whether those gains will last.

New research led by Professor Jan-Emmanuel De Neve of Saïd Business School, University of Oxford suggests companies may be making a strategic mistake if they treat AI mainly to cut headcount. The research argues that businesses focused too heavily on automation risk weakening the human capabilities they will need for growth, including creativity, judgment, institutional knowledge and leadership development.

“It’s a classic manifestation of status quo bias,” says De Neve. “It’s far easier for an executive to look at a spreadsheet and imagine using AI to streamline what people already do than it is to do the heavy strategic lifting of reimagining how technology can produce entirely new value. Treating AI as a headcount-slashing tool might yield quick, visible cost savings, but it is ultimately a path of organisational contraction that trades away a company's long-term capacity to innovate.”

The distinction is central to AI strategy. Automation replaces tasks people currently perform. Augmentation expands what people can do, giving employees more capacity for problem solving, customer relationships and higher-value decisions.

A recent survey from my company Prosper Insights & Analytics shows why this matters. In its survey of U.S. adults, Executives and Business Owners were more likely than Employees to say they already use generative AI, at 53% compared with 38%. That suggests leaders may be closer to the strategic promise of AI, while employees are closer to the disruption it creates in daily work.

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Prosper Insights & Analytics also found that 31% of employees are concerned AI will cause job losses, while 11% are concerned they personally will lose their job because of AI. A further 25% of employees say they do not trust that AI has their best interests in mind.

Those figures matter because AI adoption is also a trust exercise. If employees believe AI is being introduced to make them easier to replace, they are less likely to experiment with it openly, challenge its outputs or show managers where it can improve the business.

Professor Ashish Kumar from Trinity Business School calls this “an agency and incentive problem.” Senior leaders often see AI through the lens of strategy, efficiency and long-term opportunity, while employees deal with the practical consequences when new tools disrupt existing workflows. Kumar argues that workers are more likely to embrace AI when they feel involved in the decision, understand how it will help them, and trust that the technology is being introduced to support their work rather than threaten it.

De Neve argues that augmentation creates a different relationship between workers and AI. “When you choose augmentation, you unlock a compounding growth cycle. Employees engage with curiosity and agency, becoming 'pilots' of the technology rather than passive passengers.”

The danger of an automation-first approach is especially clear when companies reduce junior hiring or cut entry-level roles. These roles are often viewed as easy to automate because they involve repeatable tasks, basic analysis or administrative work. Yet they are also where future leaders learn judgment, build relationships and understand clients.

“When you cut human compensation and junior roles to fund technology, you hollow out your internal leadership pipeline and destroy the psychological safety required to innovate,” says De Neve. “The AI revolution will not be won by the organisations that replace people the fastest, but by those that empower them the best.”

The issue is not whether companies should ignore efficiency, it is whether efficiency becomes the whole strategy. Professor Guillaume Coqueret of Emlyon business school argues that AI adoption depends on both individual adaptability and organisational factors including leadership, incentives, culture and the choice of tools. Legacy organisations face a harder challenge because AI often has to be introduced into older processes, established habits and uneven levels of confidence.

That alignment also affects quality. One emerging risk is “workslop,” AI-generated content that looks useful but lacks the substance needed to move work forward. Alan Lerner, professor at the Open Institute of Technology - OPIT, describes workslop as “AI generated work content that masquerades as good work, but lacks the substance to meaningfully advance a given task.”

The danger is that AI increases output volume while shifting the burden onto employees who must check and interpret unclear work, creating the impression of productivity while increasing the effort needed to make AI output reliable.

Prosper Insights & Analytics data points to similar concerns. Among employees surveyed, 37% say AI needs human oversight, while 34% are concerned it can provide wrong information or hallucinations. These responses suggest many employees are not rejecting AI. They understand that its value depends on human judgment.

That judgment is harder to sustain when employees are anxious, overloaded or unsure how their work is being evaluated. Debora Nozza, Assistant Professor at Bocconi University, says AI can reduce some tasks while increasing the mental burden around others.

“In theory, AI should reduce the burden of work. In practice, it often changes the nature of that burden,” says Nozza.

Prosper Insights & Analytics also found that 17% of employees say AI makes them anxious. That does not mean AI adoption should slow, but companies need to pay closer attention to morale, training, communication and workload. If AI leaves employees less able to apply their judgment, the expected efficiency gains may prove smaller than leaders hoped.

The most successful AI strategies will likely be judged by more than adoption rates or cost savings. Companies will need to ask whether AI is improving work quality, helping employees make better decisions, strengthening retention and building the next generation of leaders.

Automation can make old processes faster. Augmentation can help companies develop new sources of value. Companies that recognise this may be better placed to turn AI from a short-term efficiency tool into a lasting source of competitive advantage.

Disclosure: The consumer sentiment study referenced above was conducted by my company, Prosper Insights & Analytics. This is the same dataset used by the National Retail Federation, and available from Amazon Web Services, Bloomberg, and the London Stock Exchange Group for economic benchmarking.