Seven in 10 employees say AI is making them more productive, yet workloads remain virtually unchanged, reveals new research, raising questions over whether AI is giving workers time back or simply enabling more work.
Artificial intelligence may be helping employees feel more productive, but it does not appear to be making their workloads any lighter. New global data from Culture Amp reveals that 71% of employees say AI tools help them feel more productive, rising to 93% among the heaviest users.
Yet employees report remarkably similar workloads regardless of how intensively they use AI. Some 72% of AI power users say their workload feels reasonable for their role, compared with 69% of employees who do not regularly use AI – a difference of just three percentage points.
The findings raise a bigger question for businesses investing heavily in workplace AI: if the technology is creating additional capacity, who is benefiting from the time it saves? Is the AI dividend being used to reduce pressure, improve the quality of work, create space for innovation and learning or support career development? Or is every hour saved simply being replaced with more work?
That distinction could become increasingly important as businesses move beyond measuring AI adoption towards understanding whether the technology is actually improving work.
AI INTENSITY AND PRODUCTIVITY
“Seven in ten employees say AI is making them more productive. Most executive teams would read that as a return on investment,” said Amy Lavoie, Vice President of People Science at Culture Amp. “But I’d read it as an unanswered question, because workload scores remain steady. The organisations that see a genuine return on AI over the next twelve months won’t be the ones with the highest adoption rates. They’ll be the ones that decided, explicitly, what they were going to stop doing with the time it gave back.”
Culture Amp’s first AI at Work Benchmark, How Employees Really Feel About AI at Work, draws on data representing approximately 112,000 employees. It found a clear relationship between the intensity of AI use and employees’ perceptions of productivity. Overall, 71% said AI tools help them feel more productive, rising to 93% among power users. But the same pattern does not appear in workload.
The study grouped employees into four categories according to how frequently they use AI: non-users, light users, daily users and power users. Across those groups, the proportion agreeing that their workload is reasonable varies only slightly. Some 69% of non-users say their workload feels reasonable, compared with 71–72% among employees who use AI. Culture Amp cautions that these figures measure employees’ perceptions rather than objective changes in output, hours worked or productivity.
Nevertheless, the contrast is significant. If employees feel considerably more productive when using AI but do not feel substantially less burdened by work, businesses need to understand what is happening to the capacity AI is supposedly creating.
WHO GETS THE AI PRODUCTIVITY DIVIDEND?
The question goes beyond whether AI “works”. Productivity improvements have always created choices for organisations. A business can use greater efficiency to produce more. It can improve quality and reduce costs. It can invest in innovation. Or it can allow employees to accomplish the same work with less pressure.
AI potentially creates the same choice – but at much greater speed. If an employee can complete a task in three hours that previously took five, what happens to the two hours saved? They could be used for more valuable work, professional development, creative thinking, collaboration or simply reducing unsustainable workloads.
But if the organisation immediately fills those two hours with additional tasks, AI may increase output without employees experiencing much of the productivity dividend themselves. That distinction matters because productivity gains that simply translate into continuously escalating expectations could eventually undermine some of the engagement, wellbeing and retention benefits employers hope AI will create.
It also complicates the business case for the technology. Previous research found that two-thirds of employees spend up to six hours each week correcting poor-quality AI-generated work or “workslop”. That research highlighted another side of the productivity equation: AI can save one worker time while creating additional work for somebody else who has to identify and correct poor-quality output.
Together, the findings suggest organisations need to look beyond how quickly AI helps employees complete individual tasks and examine how it is changing work across the organisation as a whole.
AI POWER USERS ARE ALSO MORE MOTIVATED
Culture Amp’s findings reveal another interesting difference between the heaviest AI users and those who do not regularly use the technology. Some 72% of AI power users say their organisation motivates them to go beyond what they would in a similar role elsewhere, compared with 57% of non-users – a 15-point difference.
That does not establish that using AI causes employees to become more motivated. More motivated or engaged employees may be more likely to experiment with AI in the first place. Power users may also work in organisations or teams providing stronger support for AI adoption.
But the finding suggests businesses should pay attention to their most intensive AI users for reasons beyond adoption statistics. Organisations can use these insights to identify which tasks AI is improving, where it is creating capacity, what support employees need and how they could redesign jobs.
The question for employers should therefore move beyond “How do we get more people using AI?” It should increasingly become “What are our most effective AI users doing differently – and what can we learn from them?”
ENCOURAGING AI EXPERIMENTATION
The research also reveals a substantial gap between organisations encouraging AI experimentation and leaders explaining what they actually want AI to achieve. Some 85% of employees say their organisation actively encourages exploration and experimentation with AI. Yet only 58% say leaders have clearly explained where the company is headed with the technology. That represents a 27-point gap between encouragement and strategic direction.
The disconnect continues at manager level. Only 60% of employees say their manager shares examples of how AI can support their goals or workflows. Employees, in other words, are receiving considerable encouragement to experiment – but considerably less clarity about why.
“The workforce is telling us something important,” said Lavoie. “Employees are telling us they understand the risks, they are using the tools, and they feel more productive. That is not a workforce resisting change. That is a workforce that has done its part and is waiting on leadership to do theirs.”
The findings reinforce growing evidence that workplace AI adoption may be moving faster than organisational strategy and workforce planning. As Fair Play Talks recently reported, workers are adopting AI faster than many employers can govern it, with employees across 72 organisations actively using almost 50 different AI applications.
Separate research has also found that 70% of workers say they are ready for AI, while only 39% of business leaders believe their employees are prepared. The latest findings suggest the challenge may increasingly be less about persuading employees to use AI and more about leadership providing a coherent reason for doing so.
EMPLOYEES UNDERSTAND AI RISKS
A lack of strategic clarity does not appear to mean employees are unaware of AI’s risks. Some 86% say they understand the risks associated with using AI in their work, making this the highest-scoring item in Culture Amp’s benchmark. Risk awareness also rises sharply with AI usage. It stands at 67% among non-users and reaches 95% among power users.
Meanwhile, 80% of employees say they trust their organisation to use AI responsibly, while the same proportion believe they will have a say in how their company uses it. But Culture Amp’s wider analysis suggests the weakest areas are the organisational infrastructure surrounding adoption: systems and processes supporting AI scored 64%, managers sharing practical examples scored 60%, and leadership connecting AI to company goals scored just 58%.
That points towards a recurring theme in workplace AI: employees may be moving faster than the organisational systems around them. As previously reported, businesses may still be underestimating the people challenges associated with AI transformation. Technology deployment alone does not determine whether AI succeeds. Organisations also need to redesign work, develop skills, equip managers and explain how AI fits into the wider business strategy.
AI ADOPTION RISES AS CAREER VISIBILITY FALLS
The lack of clarity extends beyond employees’ immediate work. Culture Amp found that employee awareness of internal career opportunities has fallen by 10 percentage points since July 2025 – the largest single-year movement in the benchmark data set. At the same time, satisfaction with career development remains at 66%, where it has been since 2021. The distinction is important. Employees are not necessarily reporting that they are less satisfied with the development they currently receive.
They appear to be becoming less clear about what opportunities will be available to them next. That uncertainty is understandable as organisations work out which tasks AI will automate, which jobs it will reshape, what new roles it will create and which skills will become more valuable. But silence can create its own problems.
“Leaders are being asked to explain a future none of us can see clearly yet. We do not know with certainty which roles AI reshapes, which skills hold their value, or what a career path looks like three years out,” said Lavoie. “As a result, leaders may go quiet, because saying nothing feels safer than saying something that turns out to be wrong. The most useful thing a leader can do right now is admit the plan is unfinished, and then keep talking anyway. Certainty is not what employees need from you right now. Candor is.”
The findings reinforce a broader point: AI is not just transforming technology – it is transforming jobs, skills and careers too. Earlier research found that businesses are still underestimating the people challenges of AI, with organisations often focused on helping employees use today’s tools rather than redesigning roles, skills and career pathways for an AI-enabled future.
AI PRODUCTIVITY GAINS
The Culture Amp findings raise a question that will become increasingly difficult for employers to avoid as AI adoption expands. What exactly is the productivity gain for? Businesses understandably expect investment in AI to produce commercial returns. But those returns do not have to come exclusively from asking employees to produce a greater volume of work.
The AI dividend could also take the form of better-quality work, fewer repetitive tasks, reduced administrative burden, greater innovation, improved customer service, more time for learning and development or healthier workloads. Those outcomes can themselves contribute to business performance through higher-quality decisions, stronger engagement, reduced burnout and improved retention. This is particularly important given existing concerns about AI dependence.
Previous research found that nearly half of employees fear AI is making them less intelligent, highlighting concerns that excessive reliance on the technology could weaken critical thinking and other skills. If every efficiency gain is converted immediately into additional output, organisations may leave little space for employees to develop precisely the human capabilities that become more important as routine work is automated.
AI ROI NEEDS A BROADER DEFINITION
The findings also raise questions about how organisations measure the return on AI investment. Adoption rates are relatively easy to track. So are time savings. But neither necessarily demonstrates that AI is improving work.
An organisation could have very high AI adoption while employees remain overloaded, managers struggle to explain its purpose, career paths become less visible and colleagues spend hours correcting poor-quality output. Conversely, AI could create value by eliminating low-value work, improving decision quality or giving employees more time for higher-value activity even where the number of tasks completed does not dramatically increase.
This is why businesses may need to measure AI success across several dimensions rather than relying on usage alone. That includes quality, workload, employee experience, time saved, rework, skills development, innovation, customer outcomes and sustainable productivity.
It also complements emerging evidence that the way organisations govern AI can affect whether they realise value from it. Recent research found that organisations with strong trustworthy AI practices were 15 times more likely to report strong or high returns from AI investment, suggesting governance, explainability, data quality and accountability should increasingly be considered part of the business case for AI rather than barriers to innovation.
WHAT EMPLOYERS & BUSINESS LEADERS SHOULD DO NEXT
Decide what the AI dividend is for
Before encouraging ever-greater adoption, leaders should determine what they want employees and the organisation to gain from the capacity AI creates. That could mean greater output, better quality, reduced workloads, more innovation, more development time – or a deliberate combination of these. Without that decision, AI-generated capacity may simply disappear into continuously expanding workloads.
Measure capacity, not just adoption
Knowing how many employees use AI says little about whether it is improving work. Employers should examine where time is actually being saved, what employees are doing with that time and whether AI is reducing or simply redistributing work.
Study power users
Power users report both higher perceived productivity and greater motivation. Rather than simply celebrating their adoption rates, employers should examine which tasks they use AI for, what support they receive and whether their working practices contain lessons that could benefit other employees.
Give managers practical guidance
With only 60% saying managers provide examples of how AI can support goals and workflows, managers need more than a corporate AI policy. They need practical examples relevant to their teams, clarity about acceptable use and enough knowledge to help employees decide when AI adds value – and when it does not.
Explain why AI matters
Encouraging experimentation without connecting AI to organisational goals risks creating activity without direction. Leaders do not need perfect answers about the future, but employees should understand what problems the organisation hopes AI will solve and what successful adoption is supposed to look like.
Talk openly about careers
Falling visibility of internal opportunities deserves attention. Employers should communicate what they know about changing roles and skills – and be transparent about what remains uncertain. Waiting for complete certainty before talking about careers risks allowing employees to fill the information vacuum themselves.
Protect time for human development
If AI creates additional capacity, organisations should consider deliberately allocating some of it to learning, critical thinking, collaboration, creativity and career development. Those capabilities may become more valuable, not less, as routine work becomes increasingly automated.
Watch for workload creep
Managers should examine whether AI productivity gains are quietly resulting in higher expectations and additional tasks. Sustainable productivity means improving what people can accomplish without automatically assuming that every saved hour must be filled.
WHY THIS MATTERS FOR RESPONSIBLE BUSINESS
AI is frequently sold to businesses through the promise of productivity. The Culture Amp findings suggest employees themselves increasingly believe that promise: 71% say AI makes them feel more productive, rising to 93% among power users.
But productivity is only part of the equation. If workloads remain broadly unchanged, leaders need to understand where the additional capacity is going – and who ultimately benefits from it.
That is the emerging question of the AI dividend. Businesses are entitled to expect a return from substantial technology investments. But sustainable returns can take different forms: more output, better-quality work, lower costs, greater innovation, stronger skills, reduced pressure or more time for the human work technology cannot easily replace.
The challenge is to make those choices deliberately. Otherwise, organisations risk falling into a familiar productivity trap: every efficiency gain simply becomes the new baseline for how much more people are expected to do.
ENABLING AI
Culture Amp’s findings also suggest employees may already be ahead of their organisations in some respects. They are experimenting with AI. Many understand its risks. Power users feel considerably more productive. Yet leadership direction, practical manager guidance and career visibility appear less developed.
The real measure of workplace AI may therefore ultimately be less about how many employees use it and more about what organisations do with the capacity it creates. If AI enables people to work faster but every saved hour is immediately replaced with another task, employees may reasonably question who is actually benefiting from the productivity revolution.
The organisations that get this right will need to make an explicit choice about the AI dividend: more output, better work, more development, reduced pressure – or some combination of all four.
Read the full Culture Amp’s 2026 AI at Work Benchmark here.
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