Indian Companies Must Redesign Work as AI Deployment Accelerates

Output Gains Can Hide Quality Tradeoffs

The experiment also shows why managers should not treat faster production as a complete measure of productivity. AI-assisted teams produced more material and better text, but weaker images and less varied work. Neither team type gained an overall advantage in the field test.

Greater delegation was associated with better text quality, no improvement in image quality and lower output diversity. It was also associated with slightly lower productivity. Because the amount delegated was not itself randomly assigned, the relationship does not establish that delegation caused the decline. It does show that handing over more work did not automatically improve every result.

That distinction matters inside a job. A system may summarize a document well but judge poorly which evidence matters. It may produce a strong first draft yet miss the context behind an unusual customer request. A single capability label cannot tell a manager which parts of the work are safe to delegate.

Ju and Aral suggest matching tasks to the tool’s uneven strengths. For work where variety matters, they propose preserving human-led ideation before AI-assisted execution. These are management recommendations drawn from one setting, not universal rules. Companies still need to test them against their own work and risk levels.

Capability Does Not Carry Accountability

Delegation becomes more consequential when an agent can act rather than only prepare material for review. Parminder Singh, co-founder of Redscope.AI, which builds AI agents for website sales engagement, describes the problem in organizational terms.

“The defining challenge for leaders is that, for the first time, they’re managing contributors that have capability but no accountability,” Singh said.

Drafting an email and sending it carry different authority. So do recommending a discount and approving one, or identifying a questionable expense and rejecting the payment. The software cannot be disciplined, demoted or held legally responsible as an employee or executive can. Responsibility must remain with a person or governing body inside the organization.

Rahul Aggarwal, vice president and general manager at sales technology company Proshort, said leaders need to establish which actions a system may take independently, where human judgment is required and who owns the outcome. Those boundaries should be set before a system begins acting in live workflows.

Many organizations have not reached that point. A Deloitte survey of 3,235 leaders across 24 countries, conducted in August and September 2025 for its 2026 enterprise AI report, found that only 21% reported a mature model for governing autonomous agents.

The management question is therefore not only whether the technology works. Leaders must decide who can authorize it, who reviews its behavior, who can restrict it and who answers for a decision made with its help.

Performance Measures Must Separate Volume From Value

Most performance systems assume that a person or a human team produced the work under review. That assumption weakens when employees use one system for research, another for analysis and a third for drafting before deciding what to accept or revise.

Counting output can reward the employee who delegates the most rather than the one who exercises the best judgment. Balasubramanian A, senior vice president at TeamLease Services, argues that companies should give greater weight to outcomes, productivity and value while continuing to assess critical thinking and accountability. Mookiah similarly favors decision quality over a record of who completed each step.

The BCG survey shows why time saved is an incomplete measure. Among regular frontline AI users, 42% said AI saved them at least a full working day each week. Yet 66% reported little or no guidance on what to do with that time, and more than half did not redirect it toward strategic work. These were self-reported experiences, not audited productivity gains.

The issue has particular weight for India’s technology services companies. The KPMG-Nasscom paper argues that the sector’s long-standing connection between headcount and output is weakening as clients expect workers to operate alongside AI and deliver higher-value outcomes. That is the paper’s interpretation of an emerging workforce shift, not evidence that every services company has already changed its operating model.

Managers still determine whether individual efficiency becomes organizational value. Microsoft’s 2026 Work Trend Index surveyed 20,000 knowledge workers who used AI across ten markets, including India. Only 26% said their leadership was clearly and consistently aligned on AI.

In Microsoft’s statistical modeling, organizational factors such as culture, manager support and talent practices accounted for 67% of the relative importance attached to self-reported AI impact, compared with 32% for individual attitudes and behavior. Microsoft states that the relationship is an association, not proof of causation.

Aggarwal expects routine supervision, status tracking and repetitive reporting to become less important. Coaching, context and judgment should matter more. Managers may spend less time checking whether a task was completed and more time deciding whether the result meets the required standard.

What Leaders Should Change

C-suite leaders. Define an authority map before deploying an agent. Specify what it may do on its own, what needs approval and which executive owns the result. Evaluate AI programs through cost, quality, customer outcomes and rework rather than the number of tools or pilots.

Managers. Break jobs into tasks and test the system at that level. Set quality thresholds and review rules in advance. Record when people override the system and reward employees who catch weak automated work. Preserve a human-led ideation stage when variety or originality matters.

HR and people leaders. Redesign performance reviews for work produced jointly by people and software. Separate useful outcomes from output volume. Continue to assess domain expertise, judgment, originality, coaching and accountability. Train managers to evaluate machine-produced work, not only to operate AI tools.

AI agents do not have to replace employees to alter management. They only have to perform enough of the work to make effort and output harder to connect. The evidence so far shows gains in speed alongside tradeoffs in quality and variety, with responsibility still resting on people.

For Indian companies, the immediate task is to bring work design up to the pace of deployment. Managers need clear authority, review standards and measures that reveal whether additional output creates value. Without those changes, faster work may leave the organization with more activity and the same unresolved decisions.