After months of deeper engagement with artificial intelligence (AI), a SaaS developer realised that the technology that reduced his workload was quietly erasing his breaks and stretching his work hours.
The quick help from AI had tempted the employee to use it during short breaks—such as waiting for a file to load, between meetings, or before lunch—letting work continue instead of fully stepping away.
None of it felt like extra work. However, over time, the breaks that once alleviated the health impact of prolonged sitting had diminished.
It was what researchers describe as indicators of “workload creep”, when productivity gains from automation translate not into reduced effort, but into higher targets, tighter timelines and greater cognitive demands.
Top human resources executives and consultants said early signals of the shift are visible as generative AI tools move from experimentation to everyday work.
At healthcare technology firm Innovaccer, the focus of AI adoption is to shift the cognitive load of work upward rather than simply accelerating tasks, said Satyajit Menon, the company’s global head of people experience.
Once embedded, the pace of work accelerated rapidly. Innovaccer had to consciously reinforce prioritisation and manager check-ins to ensure that increased speed didn’t quietly turn into increased expectations.
Menon said the transition initially created friction as some workers resisted AI adoption, while teams faced tighter objectives and key results (OKR) and faster turnaround targets.
“Productivity gains often make us a bit greedier, because we want more,” he said, cautioning that efficiency gains do not automatically translate into better employee experience.Managers, meanwhile, are emerging as a key pressure point in the AI transition.Amit Khanna, partner at Grant Thornton Bharat, described adoption patterns across organisations as a U-shaped curve, with senior leaders and junior employees using AI more frequently than mid-level managers. For technology and engineering teams, the productivity impact is already measurable.
Dhirendra Nath, chief human resources officer at digital business enabler Altimetrik, said that where AI is effectively integrated into engineering workflows, such as faster drafting, coding assistance, test creation, documentation, and triage, it is driving significant efficiency gains.
“We’re seeing approximately 20-30% productivity improvement. The biggest shift is faster time-to-first-usable output and quicker iteration loops, rather than an immediate reduction in total workload,” said Nath.
These shifts are also reshaping workforce structures.
“AI is materially increasing throughput per employee,” said Anurag Malik, partner at EY India.