AI’s mountain-slope pattern
Employers are enthused by it. There is a section of employees who are consumed by it. But new research on the use of Artificial Intelligence (AI) shows that the benefits may not be sustainable, and soon result in employee burnouts, and work fatigue. A recent study within a firm that uses AI tools, and has 200 employees, indicates that the graph of AI gains may follow a mountain-slope pattern. After the initial excitement, and unexpected hikes in productivity, AI may frustrate, disgust, and jeopardise the employees. This may lead to low morale, lower productivity, and possible losses for the firms over the medium term. In effect, AI is not a double-edged sword that helps some firms, and hurts others, and benefits some employees, and makes others redundant. It is a single-edged sword whose initial slices and slashes may seem positive but lead to a disruptive and chaotic work environment.
Published in the Harvard Business Review magazine, the study concludes that in the initial phase, employees work at a faster pace, as expected, took on a broader scope of tasks, as desired, and extended work into more hours of the day, “often without asking to do so.” As the study adds, “On their own initiative, workers did more because AI made ‘doing more’ feel possible, accessible and, in many cases, intrinsically rewarding.” Although this may seem like a dream come true for the managers, and unexpected benefits to the employees, who feel more productive, smarter, and on an upward trajectory, the problems become evident. Suddenly, the workers realise that their workload has quietly stretched and expanded, they spend more hours in office which disrupts the work-life balance, and they juggle with more on their plates. This may lead to “cognitive fatigue, burnout, and weakened decision-making.” The productivity surge slumps, which is akin to walking up the slope quite fast, and plummeting downwards after the peak.
The research identifies three forms of work intensification, or growth in responsibilities that lead to the negatives. The first relates to task expansion. AI fills in the gaps in knowledge and skills, and subconsciously nudges the employees to take on more, especially work that needs to be done by colleagues, juniors, or seniors. It is an empowering cognitive boost, which “reduced dependence on others, and offered immediate feedback and correction.” According to the study, “Workers described this as ‘just trying things’ with the AI, but these experiments accumulated into a meaningful widening of job scope. In fact, workers increasingly absorbed work that might previously have justified additional help or headcount.” Suddenly, people took on semi-formal and informal work, which was not a part of their responsibilities. This is akin to the Gen Z feeling of ‘I do because I can.’ Employees did more, went beyond their calling because it was easy.
Over time, as expected, the trends blurred the boundaries between work and non-work. Since the initial assimilation of extra work was easy and simple, as well as faster, “workers slipped small amounts of work into moments that had previously been breaks.” It was logical for them to prompt AI tools during lunchtimes, and coffee breaks, or during conversations with colleagues. Indeed, many admitted that they would send a “quick last prompt” before they left the office so that AI could keep working overnight while they were at home. This enabled them to come in the mornings, and start work straight away on a problem or issue. It did not feel like extra work. In fact, AI was filling in the hours when the employee was not at work. But the result was a typical workday when the employee spent more time at work. Although it started as chatting with AI, work spilled into late evenings.
Finally, the extra use of AI, which was fun and exciting earlier, as well as empowering and satisfying, translated into a new work rhythm that typified, and accelerated the art and science of multi-tasking. Now, the employee seems to work with a tech partner, who was helpful, non-complaining, and ever-ready to share the work burden. Since two people, a real and virtual one, were at work, they could do several tasks at the same time. “While this sense of having a ‘partner’ enabled a feeling of momentum, the reality was a continual switching of attention, frequent checking of AI outputs, and a growing number of open tasks,” states the study. Hence, over time, the new rhythm “raised expectations for speed, not necessarily through explicit demands, but through what became visible and normalised in everyday work.” In the end, the employees felt the pressures, and found that their workloads were higher, although AI use was supposed to achieve the opposite.
Logically, the workers felt busier than before after a period. One of the engineers, who worked for the firm, said, “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 do not work less. You just work the same amount, or even more.” Firms may see this scenario as a win-win-win, for which manager does not like voluntary initiative on the part of the employees to work harder, and put in more hours. In today’s corporate jungle, working more hours is ideal. In the case of AI, the delivery was implicit. This was a productivity explosion at its best. The employees seem to be caught in work warp, and trap, and that too without a human prompt. The employers got the best of the AI bargain without a major push. Tech offered what it needed to.
“But our research reveals the risks of letting work informally expand, and accelerate: What looks like higher productivity in the short run can mask silent workload creep, and growing cognitive strain as employees juggle multiple AI-enabled workflows,” concludes the study. In fact, overwork can impair judgments, increase errors, and make it harder to differentiate genuine gains from unsustainable intensity. The firms’ expectations rise, even as the employees feel the brunt of burnouts and fatigue, with almost no way to step away. Firms plug away with the higher use of AI, nestled in the comforts of the visible benefits, and employees seek ways, mostly in vain, to find their earlier rhythm. In the end, the employees’ fall on the steep downward slope is followed by the firm’s similar trajectory, which may come with a lag. AI, which pushes both to a new high, pulls them down to unbelievable lows. The trend is one-way, upwards, and downwards, with no choices and options.
