The Big Misread: Revenue Per Employee Isn’t Productivity. It’s A Legacy Illusion

In IT services leadership circles, revenue per employee still gets treated as a north star. It sounds clean, feels measurable, and promises easy comparison. However, this metric misleads more than it informs. Built for a different era, it conceals complexity and overstates efficiency. It’s a comforting story, but no longer a true one.

Let’s be blunt: this metric hasn’t kept pace with the way modern technology companies operate. The idea that one can measure productivity by dividing revenue by employee count ignores everything about how work is actually done today. Most companies don’t run on fixed headcount anymore. They operate with a mix of full-time employees, contractors, offshore teams, AI copilots, automation tools, and platform partners. The denominator, the employee count, is no longer a reliable reflection of effort.

We’ve seen mid-sized IT firms like Persistent Systems and Mphasis post impressive gains in revenue per employee. But under the surface, those gains are less about squeezing more from each individual and more about relying on subcontractors, licensing third-party software, and ramping up automation. They’ve grown smarter in how they execute, but the rise in this one metric doesn’t prove their people have become more productive. It simply shows they’re playing by a new rulebook. Meanwhile, the old metric hasn’t updated its lens.

What’s worse is how the larger players have started to follow suit. Some are holding off on hiring freshers, trimming lateral hires, and leaning harder on third-party delivery. That pushes revenue per employee higher on paper. But it also raises operational risk, makes teams brittle, and undercuts long-term talent pipelines. This is theatre, not strategy.

The rise of automation only complicates things further. In the past year, we’ve seen AI tools take over everything from documentation to internal queries to drafting client responses. Work that once took a team of analysts is now partially handled by a single tool. Output rises. Revenue climbs. The employee count stays flat. Voila, the metric looks better. But it’s not because people are doing more. It’s because machines are doing part of the job.

At the same time, the kinds of deals companies are executing have changed. More revenue now comes from fixed-price projects, reselling cloud credits, and monetising platforms. In many cases, these don’t require adding staff. So when a company books a multimillion-dollar contract by brokering software or cloud services, the numbers go up, but the human effort behind it may not.

This mismatch becomes even more obvious when you look at the bigger firms. Take TCS, Infosys, and Wipro. All three resumed fresher hiring this year to rebuild bench strength. As a result, their revenue per employee dipped. But this isn’t a sign of inefficiency. It’s a sign of foresight. They’re investing in capability, planning for scale. And yet, because the metric dropped, the market misread the move. That’s the problem. This number punishes companies for building capacity and rewards those who delay hiring to protect optics.

As AI tools get embedded in everyday workflows, something subtle but serious starts to happen. Employees begin to trust the machine more than their own judgment. Internal logic gets replaced by templated outputs. The company tone is slowly flattened into generic phrasing. Over time, the enterprise starts losing its voice, its reasoning muscle, and its institutional memory. And no dashboard captures that loss, especially not revenue per employee.

This is where the metric does real damage. It feeds the illusion of hyper-efficiency while core capabilities erode. Boards see a number moving in the right direction and assume all is well. But behind the scenes, delivery is fragmented, teams are overstretched, and decision quality is slipping.

The need of the hour isn’t a new metric. It’s a better lens. We need to go beyond topline revenue and ask harder questions: How much of that revenue is truly non-linear? How much depends on subcontractors or third parties? Are margins growing with revenue? Are we tracking what AI tools are learning from our systems and teams?

Revenue per employee can’t answer any of that. It’s an output stat, not a health check.

Instead of over-relying on a single number, leadership teams need to look at a broader set of indicators: profit per employee, automation return on investment, customer retention, internal talent development, and delivery mix. These metrics demand more work to track, yes. But they also tell the truth.

What’s on the line isn’t just accurate reporting. It’s how clearly a company sees its own direction. When leaders treat lean as a strength or automation as efficiency, the business starts slipping. They hold back on hiring, stretch teams too far, and lose the experience they depend on. All this while chasing a number that lost meaning years ago.

Technology firms in 2025 aren’t running on manpower. They’re running on models, platforms, partners, and hybrid teams. Trying to evaluate them through the lens of revenue per employee is like assessing a streaming service by its DVD sales. It’s not just outdated. It’s irrelevant.

So the next time a company boasts about rising revenue per employee, ask what’s driving it. Ask what’s missing from the denominator. Ask whether that growth is sustainable, or just a statistical trick. Because when you strip away the optics, what you often find isn’t productivity. It’s exposure.

And exposure, as we’ve seen too often, is the last thing any enterprise can afford to misread.

Sanchit Vir Gogia is Chief Analyst, Founder and CEO of Greyhound Research.

Disclaimer: The views expressed in this article are solely those of the author and do not necessarily reflect the opinion of NDTV Profit or its affiliates. NDTV Profit does not guarantee the accuracy, completeness, or reliability of the information presented in this article.