AI Burnout Warning

New research suggests that generative AI adoption may actually intensify work patterns and increase burnout risk rather than reduce workload.

Research (Inside A Live Company)

For several years, generative artificial intelligence has been promoted as a way to reduce administrative burden and free professionals to focus on higher value tasks. Tools based on large language models, systems trained on vast datasets to generate text, code and other content, are widely used to draft documents, summarise meetings and assist with programming and analysis.

However, a February 2026 article in Harvard Business Review (by Aruna Ranganathan and Xingqi Maggie Ye) reports findings from an eight month in progress study inside a 200 person United States technology company, concluding that “AI tools didn’t reduce work, they consistently intensified it.”

Eight-Month Study

Over eight months, the researchers observed day to day work inside the firm and conducted more than 40 in depth interviews across key teams, enabling them to compare how roles changed as AI use increased. Crucially, staff were not instructed to use the tools or to raise performance targets, yet workloads expanded as employees voluntarily adopted AI and took on more.

Observed Changes In Work Patterns

The researchers reported that once employees adopted AI tools, they worked at a faster pace, took on a broader scope of tasks and extended work into more hours of the day. These changes occurred without formal instructions from management to increase targets or output.

One of the main mechanisms identified was task expansion. For example, because generative AI can fill gaps in knowledge and provide rapid feedback, employees were found to have increasingly stepped into responsibilities that previously belonged to other roles. Product managers and designers began writing code, while researchers undertook engineering tasks. Over time, it was observed that individuals therefore absorbed work that might previously have required additional headcount or external contractors.

The researchers describe generative AI as providing what many workers experienced as an “empowering cognitive boost”, whereas employees referred to “just trying things” with the AI, experimenting with unfamiliar tasks. The researchers found that these experiments gradually accumulated into a widening of job scope, which in turn created additional review and oversight work for others. For example, engineers reported spending more time reviewing, correcting and guiding AI assisted work produced by colleagues, often through informal exchanges on internal messaging platforms.

Blurred Boundaries Between Work And Non Work

A second pattern identified in the study was the erosion of natural breaks in the working day. For example, because AI systems reduce the friction of starting a task, workers began prompting tools during moments that previously functioned as downtime, including lunch breaks and short pauses between meetings.

In fact, some employees even described sending “a ‘quick last prompt’ right before leaving their desk so that the AI could work while they stepped away”. Although these interactions were brief and conversational, it was noted that they reduced opportunities for recovery. The researchers observed that work became more continuous and less clearly bounded, with fewer natural pauses.

Over time, this pattern contributed to a sense that work was harder to step away from. In essence, the boundary between work and non work did not disappear, but it became easier to cross, particularly as faster turnaround times became visible and normalised within teams.

Increased Multitasking And Cognitive Load

The third form of intensification that the researchers observed involved increased multitasking. For example, workers seemed to be managing several AI assisted threads simultaneously, manually drafting material while AI generated alternatives, running multiple agents in parallel or revisiting deferred tasks because AI could handle parts of them in the background.

While this created a sense of momentum, it also required frequent checking of outputs, prompt refinement and attention switching. The study notes that higher speed did not necessarily translate into reduced busyness. For example, as one engineer summarised, “You had thought that maybe, oh, because you could be more productive with AI, then you save some time, you can work less. But then really, you don’t work less. You just work the same amount or even more.”

Risks Of Silent Workload Creep

In their article about their study, the researchers argue that voluntary expansion of work can initially appear positive for organisations, but they warn that higher short term output may conceal unsustainable intensity. For example, because additional tasks are often self initiated and framed as experimentation, leaders may not immediately recognise the cumulative increase in load.

Fatigue And Burnout

The researchers warn that what appears to be higher productivity may actually mask a more damaging pattern. “Over time, overwork can impair judgment, increase the likelihood of errors, and make it harder for organisations to distinguish genuine productivity gains from unsustainable intensity.” They add that the cumulative impact on employees can be “fatigue, burnout, and a growing sense that work is harder to step away from, especially as organisational expectations for speed and responsiveness rise.”

The study does not argue that AI fails to enhance human capability, but its central point is that when augmentation makes it possible to do more, organisations and individuals may gradually raise expectations, expand scope and accelerate pace, reshaping everyday work in ways that increase pressure rather than reduce it.

Wider Evidence On Productivity And Perception

That said, other research has produced mixed findings on AI related productivity gains. For example, a recent working paper from the National Bureau of Economic Research examining AI adoption across thousands of workplaces reported average time savings of around 3 per cent, with no significant impact on earnings or hours worked across occupations.

Also, in 2025, the research organisation METR conducted a randomised trial involving experienced software developers and found that developers using AI tools took 19 per cent longer to complete certain tasks while believing they were 20 per cent faster. This study highlights the potential gap between perceived and measured productivity and the hidden time required to review and correct AI generated outputs.

Corporate surveys have also indicated that while many employees report time savings from AI, overall workload pressures often remain due to organisational factors and rising expectations for speed and responsiveness.

Implications For Organisations

It should be noted here that the study results highlighted in the Harvard Business Review do not diagnose clinical burnout among participants, but rather identify patterns that may increase burnout risk over time, including workload creep, reduced recovery periods and sustained cognitive strain.

The researchers, Ranganathan and Ye, therefore argue that organisations should establish what they call an “AI practice”, defined as intentional norms and routines governing how AI is used and how work expands in response to new capabilities. They recommend structured pauses to regulate tempo, clearer sequencing of tasks to reduce fragmentation and deliberate opportunities for human interaction to counterbalance continuous AI mediated work.

The researchers conclude that “without intention, AI makes it easier to do more—but harder to stop”, thereby showing the real issue here to be one of organisational design rather than technological failure.

What Does This Mean For Your Business?

What this research ultimately seems to highlight is a governance issue rather than a technological one. When AI increases what individuals can do, organisations must decide whether to translate that into sustainable efficiency or into higher expectations and faster pace. The evidence suggests that without clear boundaries, intensification can happen quietly, even when no formal targets change.

For UK businesses investing in generative AI, that means monitoring more than output. For example, leaders may need to track workload sustainability, quality control and employee wellbeing alongside productivity metrics. AI adoption may need to be treated as organisational redesign, not simply a software rollout.

Also, the implications seem to extend beyond employers. For example, employees may feel pressure to prove the value of AI tools, managers may normalise faster turnaround without assessing long term strain, and regulators focused on workplace health may begin to examine how AI affects cognitive load and recovery time.

In essence, the research does not argue against AI, but shows that augmentation alone does not guarantee relief from pressure. The point here is that whether AI reduces workload or intensifies it will depend less on the tools themselves and more on how organisations set limits, pace expectations and define what productive work should look like.