AI Saves Time, but Workloads Keep Growing

Indian employees say AI is making them faster at work. Most also say it has given them more to do. Korn Ferry, the consulting firm, surveyed 2,000 employees in India and released the findings on September 30. Of those, 81% said AI had improved their efficiency, while 73% said AI tools had increased the number of tasks expected in their role.

The two findings are not as far apart as they first seem. If AI helps employees finish individual tasks faster, companies can use the time saved to increase output, widen roles or shorten deadlines. Some of that time can also be lost to checking and correcting AI-generated work.

That means faster work does not automatically lead to lighter workloads. What happens next depends on what companies do with the time AI saves.

Saved Time Is Being Folded Into Bigger Jobs

Nikhil Kolur, a partner in human capital consulting at Deloitte India, sees companies at two stages. A relatively small set, particularly in parts of IT services and global capability centers, or GCCs, has scaled generative AI across routine work. In those companies, human roles are moving toward judgment, creativity and decision-making. A much larger group is still running existing operations while trying to redesign work around AI. In both, he said, “freed capacity tends to be absorbed into broader roles, higher throughput or additional responsibilities rather than simply reducing workload.”

Saravanan Balasundaram, founder and CEO of Han Digital Group, an AI and technology consultancy, said companies can respond to faster work by raising targets, expanding roles or shortening deadlines. He said AI can also let one employee take on tasks previously split across several roles.

“Saving time on individual tasks does not automatically mean reducing the overall workload,” he said.

Malvika Singh, chief operating officer of GFF AI, said faster work often leads to higher expectations. A report that once took two days may now be expected within hours, with deeper analysis.

Sidharth Thakur, director and CEO of the executive search firm Grassik Search, argues that the outcome is not necessarily unintended. Companies generally buy productivity technology to raise output. If a six-hour task can be done in one hour, the remaining time is likely to be filled with new deliverables or broader responsibilities. “The technology delivered on its promise. Organizations just redirected the dividend,” Thakur said.

Thakur also argues that AI is arriving in workplaces that were already under pressure. Korn Ferry’s figures support that view. In India, 78% of respondents reported significantly heavier workloads over the past two years, against 62% globally. Another 72% said they were taking on the responsibilities of more than one role, against 61% globally. The survey does not attribute those increases to AI alone.

In Indian IT services, clients are also pressing suppliers to pass on AI productivity gains. Balasundaram said they increasingly expect those gains to show up in faster delivery and in pricing. Kotak Institutional Equities, a brokerage, reported the same pressure in May. Its sector report, citing industry stakeholders, pointed to “pricing pressure in renewals due to client expectations of higher productivity pass-through driven by GenAI adoption.” Balasundaram said that pressure can translate into employees being expected to produce more with the same or smaller teams.

Checking AI Output Takes Back Part of the Gain

AI speeds up drafting, coding, research and analysis, yet its output still needs checking. In higher-risk work, someone must decide whether an AI-generated answer is accurate, secure and compliant enough to use.

IBM, which sells AI software and consulting, has tried to measure that work. Its 2026 study surveyed 1,500 chief human resources officers, or CHROs, and equivalent executives, along with 8,800 employees in 28 countries. In the study, 80% of the CHROs said AI adoption creates “invisible” work, such as validating recommendations, fixing mistakes, providing context and managing exceptions. Among employees, 42% said AI increases their work or leaves it unrecognized.

Workday, which sells HR and finance software, quantified it. Its survey covered 3,200 full-time employees who actively use AI, at companies with annual revenue of at least $100 million. It found that nearly 40% of the time AI saved was lost to rework. That meant fixing mistakes, rewriting content and double-checking output. Only 14% of employees consistently got clear, positive net outcomes from AI.

“Gross vs net is the real story,” Malvika Singh said. In GFF AI’s deployments, she said, productivity gains are stronger in structured work. In unstructured or judgment-heavy tasks, more of the initial advantage goes into hallucination checks, correction and review.

Balasundaram said checking complex AI-generated work can consume a substantial part of the initial saving. The cost rises when experienced employees must review it for accuracy, security, compatibility or compliance. “The real productivity gain is what remains after verification, and that is what leaders should measure,” he said.

Kolur expects part of that burden to shrink as organizations mature. “Employees can become better at deciding what to delegate to AI, what requires checking and where human judgment is essential,” he said. Companies therefore need to measure the time it takes to produce a finished result, not only the time AI takes to draft.

Checking AI output also ranked highly among Indian users in Microsoft’s 2026 Work Trend Index. Microsoft, which sells the Copilot AI assistant, surveyed 20,000 AI users in 10 markets. Among Indian respondents, 63% cited quality control of AI output as a key skill, the highest share among the 10 markets, against 50% globally. In India, 87% said they remained responsible for the thinking and treated AI output as a starting point.

In Indian IT services, Balasundaram said, senior professionals may spend more time reviewing AI output, even as routine work once done by junior employees declines. That can add to the load on experienced staff while reducing some of the routine work through which junior employees traditionally learn.