Work is accelerated by AI, but breaks are subtly reduced

As artificial intelligence becomes more deeply integrated into everyday work, some employees are beginning to experience what experts describe as “workload creep”, where productivity gains from automation gradually translate into longer work hours and higher expectations rather than reduced effort.

One SaaS developer recently realised that while AI tools had significantly reduced the time required to complete tasks, they were also quietly eliminating the short breaks that once punctuated the workday. Quick assistance from generative AI meant that instead of stepping away while waiting for a file to load, between meetings or before lunch, the developer would often continue working with the help of AI. Over time, these small pauses—which previously helped reduce the physical strain of prolonged sitting—began to disappear.

Human resources leaders and consultants say early signs of this shift are appearing across workplaces as generative AI tools move from experimental use to routine adoption in daily workflows. At healthcare technology company Innovaccer, leaders say the goal of AI adoption is not simply to speed up tasks but to shift the cognitive load of work toward more strategic activities.

Satyajit Menon, global head of people experience at Innovaccer, said that once AI tools became embedded in workflows, the pace of work accelerated rapidly. The company had to consciously reinforce prioritisation practices and regular manager check-ins to ensure that increased efficiency did not quietly lead to higher expectations and pressure on employees.

Menon noted that the transition to AI-driven workflows initially created friction within teams. Some employees resisted adopting the technology, while others faced tighter objectives and key results (OKRs) and faster turnaround targets as productivity improved. He warned that efficiency gains can sometimes lead organisations to demand more output from employees rather than improving their work-life balance.

Managers are emerging as a key pressure point in this transition. According to Amit Khanna, partner at Grant Thornton Bharat, AI adoption across organisations tends to follow a U-shaped pattern. Senior executives and junior employees often adopt the technology more actively, while mid-level managers use it less frequently as they navigate both operational responsibilities and strategic expectations.

In technology and engineering teams, however, the productivity impact of AI is already measurable. Dhirendra Nath, chief human resources officer at digital business enabler Altimetrik, said that where AI tools are effectively integrated into engineering workflows—such as drafting, coding assistance, test creation, documentation and issue triage—they are producing noticeable efficiency improvements.

He estimated that teams are seeing productivity gains of about 20% to 30%. The biggest change is not necessarily a reduction in total workload but faster delivery of the first usable output and quicker iteration cycles, allowing teams to refine products more rapidly.

These changes are also reshaping workforce structures. According to Anurag Malik, partner at EY India, artificial intelligence is significantly increasing the amount of work that can be completed by each employee. Data from the EY Work Reimagined Survey 2025 suggests that advanced AI users gain roughly one to one-and-a-half days of productivity each week by using AI tools for complex tasks.

Many professionals now treat AI as a collaborative partner rather than simply a search or summarisation tool, using it as a problem-solving assistant, analytical resource and idea generator. As AI becomes more deeply integrated into business processes, organisations are seeing shorter project cycles, the ability to handle multiple tasks simultaneously and higher utilisation rates without a proportional increase in headcount.

Malik said these changes are gradually pushing companies toward more skill-fluid organisational structures, where employees contribute across functions rather than remaining confined to narrow roles.

However, signs of workload creep typically appear several months after AI adoption, once outputs generated with AI begin shaping performance expectations rather than being treated as experimental tools. Historically, during major technological transitions, productivity gains often lead first to higher output demands before eventually translating into reduced workloads.

Khanna also noted that performance evaluation systems are becoming more uncertain as companies rethink how to measure employee productivity in an AI-driven environment. Even senior executives are under pressure from corporate boards to demonstrate clear evidence that AI investments are delivering measurable results.

At the same time, some companies are recognising the potential strain that these changes can place on employees. Conglomerate RPG Group has introduced or expanded mental health and counselling programmes to help workers adapt to new work patterns shaped by artificial intelligence.

Experts say that as AI continues to transform workplaces, organisations will need to balance efficiency gains with employee well-being to ensure that automation improves productivity without silently increasing workloads.