How should companies measure performance when AI does part of the work?

Employee performance reviews are a way to reflect how work is done, as well as what gets delivered. In India, where AI tools are now a part of routine business processes, companies are starting to include AI capabilities in their assessment of employees.

Korn Ferry’s Workforce 2026 Insights Report, reported by The Economic Times, found that 70% of surveyed Indian employees said AI fluency was being incorporated into performance reviews. The findings suggest that employers are moving beyond encouraging employees to experiment with AI and beginning to consider how the technology features in their work.

Frequent use of AI does not necessarily indicate better performance. An employee may complete more tasks with the technology while another may use it selectively to improve accuracy or resolve a complex problem. Treating adoption as a measure of contribution can reward activity without establishing whether the work has improved.


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Output alone does not tell the whole story

Productivity metrics remain useful, but they need to be considered alongside the quality and impact of the work. For instance, faster completion time matters when they help teams deliver accurate results, serve customers more effectively, or devote more attention to demanding assignments. The distinction is particularly important in roles where the consequences of an error are significant. An analyst who uses AI to prepare a report still needs to check the underlying data and assess whether the conclusions are sound. A software developer remains responsible for reviewing generated code, while a recruiter must exercise judgement when evaluating candidates.

Microsoft’s 2026 Work Trend Index findings for India reinforce the importance of these capabilities. Among Indian AI users surveyed, 63% identified quality control of AI output as an important skill, while 59% highlighted critical thinking. The results point to the continued importance of human judgement as AI takes on more work.

Performance assessments therefore need to consider how employees verify results and apply their expertise.

What should managers assess?

Companies can start by defining performance criteria around the requirements of each role. The measures will differ across functions, but several principles can guide the process.

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Output and efficiency should be assessed alongside accuracy, reliability and the value delivered to customers or the business. Managers should also consider whether employees can identify suitable uses for AI, challenge questionable results and take responsibility for final decisions.

Individual contribution needs careful assessment, too. AI-assisted work may involve several people, shared systems and established processes. Giving all the credit to the person who generated the final output can overlook the colleagues who checked the work, supplied specialist knowledge or improved the underlying process.

Employees should also know whether AI proficiency is a formal performance criterion, how it will be evaluated and what responsible use entails. Without consistent standards, appraisals risk becoming dependent on individual managers’ preferences or on how visible an employee’s use of AI happens to be.

Performance management needs to keep pace

The move towards AI-assisted work also gives organisations a reason to review how frequently they assess performance. Annual reviews can struggle to capture changing responsibilities, new workflows and contributions that emerge as employees learn to use unfamiliar tools. Regular feedback can help managers identify where expectations need to change and where employees need additional support.

These ideas will form part of the wider conversation at The Economic Times’ Future of Knowledge Work Summit 2026 in Mumbai, where leaders will examine how technology is changing jobs, productivity and the workplace.

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