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AI & Emerging Tech

Performance in AI era must reward judgment, not just productivity: Equiniti India engineering head

As AI becomes a core part of enterprise workflows, organisations must rethink how they define performance, shifting focus from activity and output towards judgment, business impact and accountability.

Artificial intelligence is reshaping how work gets done across functions, from engineering and operations to customer service and business decision-making. As employees increasingly rely on AI tools to accelerate tasks and improve efficiency, organisations face a pressing challenge: how should performance be measured when outcomes are shaped by both human expertise and machine capabilities?

For Jyoti Prakash Dash, Head of Engineering, Equiniti India, the answer lies in moving beyond traditional measures of productivity and recognising the human capabilities that continue to drive business outcomes.

According to Dash, organisations need to rethink performance frameworks around business value, judgment, accountability and responsible AI adoption rather than focusing solely on activity levels or output volumes.

The shift from measuring activity to measuring value

As AI becomes embedded in day-to-day workflows, Dash believes organisations should pay greater attention to the outcomes employees create rather than the tools they use.

"Performance should be measured through business value, the quality of human judgment, and the responsible use of AI. As AI becomes embedded in everyday workflows, tool usage itself is no longer the differentiator."

In his view, the real distinction between high and average performance increasingly comes from how effectively employees frame problems, apply contextual expertise, validate AI-generated outputs and translate technology into measurable customer value.

This marks a significant departure from traditional performance systems, many of which were designed around individual effort, task completion and visible productivity.

"Traditional frameworks remain relevant but are no longer sufficient. Measures based primarily on activity, output volume, or task completion may reward access to AI rather than capability."

Instead, Dash suggests organisations should assess performance through a broader lens encompassing individual contribution, team outcomes, quality, customer impact, engineering effectiveness and continuous learning.

Why expertise matters more in an AI-enabled workplace

One concern emerging across organisations is how to distinguish genuine expertise from AI-assisted efficiency.

Dash does not see these as competing concepts. He believes AI-enabled productivity should be encouraged, provided organisations continue to recognise the capabilities that remain uniquely human.

According to him, expertise is demonstrated through:

• Problem framing

• Engineering and business judgment

• Architecture and systems thinking

• Validation of outputs

• Accountability for outcomes

• Risk awareness and contextual decision-making

"AI-assisted efficiency should be encouraged, not treated as a lesser contribution."

He adds that employees should be evaluated on their ability to question AI-generated outputs, identify limitations, manage risks and apply domain knowledge appropriately.

"The best performers use AI to solve more complex problems, not simply to produce more work."

For organisations investing heavily in AI transformation, this distinction could become increasingly important as generative AI tools become widely accessible across the workforce.

Redefining what high performance looks like

As technology takes over routine and repetitive tasks, Dash expects organisations to place greater emphasis on higher-order capabilities.

He believes future high performers will combine traditional professional strengths with a new set of competencies suited to an AI-enabled environment.

These include:

• Strong domain expertise

• AI fluency

• Systems thinking

• Critical reasoning

• Responsible decision-making

• Collaboration and knowledge sharing

Beyond technical proficiency, Dash highlights the importance of helping others succeed.

He notes that top performers will be those who can orchestrate AI capabilities effectively, transform experimentation into repeatable business value and strengthen the capabilities of teams around them.

In other words, performance will increasingly be linked not only to individual achievement but also to collective outcomes.

Looking beyond productivity metrics

The growing ability of AI to amplify output is also prompting organisations to reconsider whether productivity alone remains a meaningful measure of performance.

Dash believes it does not.

"Output volume is increasingly easy to amplify and therefore provides an incomplete measure of performance."

Instead, leaders should focus on indicators that reflect lasting business value, including:

• Customer outcomes

• Decision quality

• Reliability

• Security

• Speed to market

• Capacity created for higher-value work

"The central question should not be how much was produced, but whether the work solved the right problem and created sustainable business value."

This approach reflects a broader shift taking place across organisations as AI moves from experimentation to enterprise-wide adoption.

Making performance reviews fair in the AI age

The rapid adoption of AI also introduces new questions around fairness.

Employees often have different levels of access to AI tools, varying learning opportunities and distinct job requirements, making direct comparisons increasingly difficult.

Dash says organisations must create equitable conditions before expecting consistent outcomes.

"Fairness requires equitable access to approved tools, role-based learning, and transparent expectations."

He believes performance reviews should account for role context, enablement levels, AI adoption maturity and demonstrated outcomes rather than relying on simple usage metrics.

Leaders should also recognise that different teams face different operational realities, risk environments and opportunities to apply AI.

"AI usage metrics can support development conversations but should not become standalone performance scores or proxies for employee capability."

Preparing for a continuous performance future

Looking ahead, Dash expects performance management to become more continuous, skills-based and outcome-oriented.

"Performance management will become continuous, outcome-led, and skills-based."

He anticipates a gradual shift away from static annual objectives towards shorter feedback cycles, team-level outcomes and evidence grounded in quality, delivery and customer value.

To prepare for this transition, organisations should begin redesigning goals, establishing responsible AI standards, enabling managers and ensuring equitable access to learning opportunities.

At the same time, accountability must remain firmly human.

For Dash, the future of performance management is not about separating human effort from AI contribution. It is about understanding how effectively people combine expertise, judgment and technology to deliver meaningful outcomes.

As AI becomes a permanent feature of modern work, organisations may find their most valuable employees are not those who simply produce more, but those who use technology responsibly to improve decisions, strengthen teams and create sustainable business value.

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