A new study from Harvard Business Review suggests that AI doesn't reduce work, but rather intensifies it, enabling more high-speed, individual tasks that require protective \u201ehuman connection\u201d time to avoid professional burnout.<\/p>\n\n\n\n
One of the promises of AI is that it can reduce workload so employees can focus more on higher-value and more engaging tasks. But, according to a new study, AI tools don't reduce work, they steadily intensify it: In the study, employees worked at a faster pace, took on a wider range of tasks, and extended work across multiple hours of the day, often without being asked. This may seem like a victory, but it's not quite that simple. These changes can be unsustainable, leading to increased workloads, cognitive fatigue, burnout and weakened decision-making, reveals the new Harvard Business Review analysis. The productivity gains enjoyed in the early days can give way to lower-quality work, staff turnover and other problems. To correct this problem, companies need to adopt an \u201eAI ethic,\u201d or a set of rules and standards around the use of AI, which can include purposeful breaks, sequencing work, and adding a broader human approach.<\/p>\n\n\n\n
Currently, many companies are concerned about how to get more employees to use AI. After all, the promise of reducing the burden of some tasks through AI \u2013 drafting routine documents, summarizing information, and decoding \u2013 and freeing up workers' time for high-value tasks is tempting.<\/p>\n\n\n\n
But are they prepared for what might happen if they succeed? While leaders focus on the promised productivity gains, they may be blindsided by the complex reality and not see how much these gains are costing them until it's too late.<\/p>\n\n\n\n
\u201dIn the present research, it was discovered that AI tools have not reduced work but have instead constantly intensified it. In an eight-month study of how generative AI has changed work habits at a US tech company with about 200 employees, it was found that employees worked at a faster pace, took on a wider range of tasks, and extended their working hours across more of the day, often without being asked to. It is important to note that the firm did not mandate the use of AI (though it did provide subscriptions to commercially available enterprise AI tools). Of their own volition, workers did more because AI made \u201edoing more\u201d seem possible, accessible, and, in many cases, intrinsically rewarding,\u201d say the study's authors.<\/p>\n\n\n\n
Although this may seem like a dream come true for leaders, the changes brought about by the enthusiastic adoption of artificial intelligence can be unsustainable, causing problems down the line. Once the initial excitement of experimentation fades, workers may find their workload has quietly increased and feel overwhelmed by juggling everything suddenly in front of them. This gradual increase in workload can, in turn, lead to cognitive fatigue, burnout, and weakened decision-making. The increased productivity initially enjoyed may give way to lower-quality work, staff turnover, and other issues.<\/p>\n\n\n\n
This puts leaders in a difficult situation. What should they do? Asking employees to self-regulate is not a winning strategy. Rather, companies must develop a set of norms and standards for AI use\u2014what we call an \u201eAI ethic.\u201d Here's what leaders need to know and what they can do to set their employees up for success.<\/p>\n\n\n\n
How generative AI intensifies work<\/strong><\/p>\n\n\n\n From April to December of last year, we studied how generative AI tools altered work habits at a tech company, according to HBR specialists. We did this through personal observation two days a week, monitoring internal communication channels, and over 40 in-depth interviews across fields such as engineering, product, design, research, and operations.<\/p>\n\n\n\n I have identified three main forms of intensification:<\/p>\n\n\n\n Because AI can fill knowledge gaps, workers have increasingly taken on responsibilities that previously belonged to others. Product managers and designers have started writing code; researchers have taken on engineering tasks; and people across the organization have tried their hand at work they would have outsourced, postponed, or avoided entirely in the past.<\/p>\n\n\n\n Generative AI has made these tasks feel accessible again. These tools have provided what many have experienced as an empowering cognitive boost: they reduced reliance on others and offered immediate feedback and corrections along the way. Workers have described this as \u201ejust trying things out\u201d with AI, but these experiments have accumulated into a significant broadening of job scopes. In fact, workers have increasingly absorbed work that previously might have justified assistance or additional headcount.<\/p>\n\n\n\n There have been side effects to expanding people's responsibilities. For instance, engineers, in turn, have found themselves spending more time reviewing, correcting, and guiding AI-generated or AI-assisted work from colleagues. These demands have extended beyond formal code reviews. Engineers have found themselves increasingly mentoring colleagues who were \u201evibe-coding\u201d and completing partially filled-out pull requests. This omission often came informally\u2014in Slack threads or quick desk-side consultations\u2014adding to engineers' workloads.<\/p>\n\n\n\n Because AI has made starting a task so easy\u2014it has reduced the friction of facing a blank page or an unknown starting point\u2014workers have squeezed small amounts of work into moments that were previously breaks. Many have requested information from AI during lunch, during a meeting, or while waiting for a file to load. Some have described sending a \u201elast quick query\u201d just before leaving the office, so that the AI could work while they were away.<\/p>\n\n\n\n These actions rarely felt like they were doing much work, but over time, they yielded a workday with fewer natural breaks and more continuous engagement with the work. The conversational style of prompting further dulled the experience; typing a line to an AI system felt closer to conversation than to completing a formal task, making it easier to extend work into the evening or early morning without deliberate intention.<\/p>\n\n\n\n Some workers described realizing, often in retrospect, that as requests during breaks became commonplace, downtime no longer offered the same sense of recovery. Consequently, work felt less bounded and more ambient \u2013 something that could always be pushed a little further. The boundary between work and non-work didn't disappear, but it became easier to cross.<\/p>\n\n\n\n AI introduced a new rhythm where workers managed multiple active threads simultaneously: manually writing code while AI generated an alternative version, running multiple agents in parallel, or reactivating long-dormant tasks because AI could \u201ehandle\u201d them in the background. They did this, in part, because they felt they had a \u201epartner\u201d to help manage their workload.<\/p>\n\n\n\n Although this feeling of having a \u201epartner\u201d allowed them a sense of momentum, the reality was a constant shift in attention, frequent checking of AI outputs, and an ever-growing list of open tasks. This created a cognitive load and a feeling of constant juggling, even if the work felt productive.<\/p>\n\n\n\n Over time, this pace has increased expectations for speed\u2014not necessarily through explicit demands, but through what has become visible and normalized in everyday work. Many workers have noticed themselves doing more at once\u2014and feeling more pressure\u2014than before using AI, even though the time savings achieved through automation were ostensibly meant to reduce such pressure.<\/p>\n\n\n\n What does this mean for organizations \u2013 and how can \u201eAI ethics\u201d help\u201d<\/strong><\/p>\n\n\n\n All of this has produced a self-reinforcing cycle. AI has accelerated certain tasks, which has increased expectations for speed; increased speed has made workers more dependent on AI. Increased dependence has broadened the scope of what workers are trying to accomplish, and a broader scope has further expanded the amount and density of work. Several participants noted that while they felt more productive, they did not feel less busy, and in some cases felt busier than before. As one engineer summarized, \u201eYou thought maybe because you could be more productive with AI, then you'd save time, you could work less. But in fact, you don't work less. You just work the same amount or even more.\u201d<\/p>\n\n\n\n Organizations might see this voluntary expansion of work as a clear win. After all, if workers are doing this of their own volition, what's the harm? Isn't this the productivity explosion we were promised?<\/p>\n\n\n\n However, our research reveals the risks of allowing work to expand and accelerate informally: what appears to be higher short-term productivity can mask a silent increase in workload and escalating cognitive strain as employees juggle multiple AI-driven workflows. Because the extra effort is voluntary and often framed as enjoyable experimentation, it's easy for leaders to overlook how much additional burden workers are shouldering. In time, overload can impair judgment, increase the likelihood of errors, and make it difficult for organizations to distinguish between genuine productivity gains and unsustainable intensity. For workers, the cumulative effect is fatigue, burnout, and a growing sense that it's harder to switch off from work, especially as organizational expectations for speed and responsiveness rise.<\/p>\n\n\n\n Instead of passively reacting to how AI tools are reshaping jobs, individuals and companies alike should embrace an \u201eAI practice\u201d: a set of intentional norms and routines that structure how AI is used, when it\u2019s appropriate to pause, and how work should and should not expand in response to newly acquired capability. Without such practices, the natural tendency of AI-assisted work is not contraction, but intensification, with implications for burnout, decision quality, and long-term sustainability. As organizations work to develop their AI practice, they should consider adopting:<\/p>\n\n\n\n Intentional pauses<\/strong><\/p>\n\n\n\n As tasks accelerate and boundaries blur, workers might benefit from short, structured moments that regulate pace: protected intervals to assess alignment, reconsider assumptions, or absorb information before moving forward.<\/p>\n\n\n\n These pauses would not slow down work in general; they would simply prevent the quiet accumulation of overload that occurs when acceleration is unchecked. For example, a decision pause might require, before finalizing a major decision, a counterargument and an explicit link to organizational goals\u2014broadening the scope of attention sufficiently to guard against drift. Incorporating such pauses into the daily workflow is one way organizations can sustain better decisions, healthier boundaries, and more sustainable forms of productivity in AI-augmented environments.<\/p>\n\n\n\n Sequencers<\/strong><\/p>\n\n\n\n As AI enables constant background activity, organizations can benefit from norms that deliberately shape when work advances, not just how fast. This includes batching non-urgent notifications, holding updates until natural breakpoints, and protecting focus windows where workers are shielded from interruptions.<\/p>\n\n\n\n Instead of reacting to every AI-generated output as it appears, sequencing encourages advancing work in coherent phases. When coordination is paced this way, workers face less fragmentation and fewer costly context switches, while teams maintain overall throughput. By fine-tuning the order and timing of work\u2014rather than requesting continuous reaction\u2014sequencing can help organizations maintain focus, reduce cognitive overload, and support more deliberate decision-making in AI-driven workplaces.<\/p>\n\n\n\n Human communication<\/strong><\/p>\n\n\n\n As AI enables more individualized and autonomous work, organizations can benefit from protecting time and space for listening and human connection. Short opportunities to connect with others\u2014whether through brief check-ins, moments of shared reflection, or structured dialogue\u2014interrupt continuous individual engagement with AI tools and help restore perspective.<\/p>\n\n\n\n Beyond perspective, social exchange fuels creativity. AI offers a synthesized, unique perspective, but creative perspective depends on exposure to multiple human viewpoints. By institutionalizing time and space for listening and dialogue, organizations re-anchor work in social context and help counteract the draining and individualizing effects of fast-paced, AI-mediated work, the study authors conclude.<\/p>","protected":false},"excerpt":{"rendered":" Un \u200b\u200bnou studiu realizat de Harvard Business Review sugereaz\u0103 c\u0103 IA nu reduce munca, ci o intensific\u0103, permi\u021b\u00e2nd mai multe sarcini individuale, de mare vitez\u0103, care necesit\u0103 timp de \u201econexiune uman\u0103\u201d protectoare pentru a evita burn-out-ul profesional. Una dintre promisiunile IA este c\u0103 poate reduce volumul de munc\u0103, astfel \u00eenc\u00e2t angaja\u021bii s\u0103 se poat\u0103 concentra<\/p>","protected":false},"author":3,"featured_media":6260,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[7],"tags":[],"class_list":["post-6258","post","type-post","status-publish","format-standard","has-post-thumbnail","category-ai-for-business"],"aioseo_notices":[],"aioseo_head":"\n\t\t\n\t\n\t\n\t\n\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n
\n
\n