For India's massive IT services sector, the era of audited, tangible results from artificial intelligence investments has arrived. While companies frequently tout aggressive adoption through metrics like AI-generated code percentages, the audited financial data revealing the true impact on productivity and economics remains conspicuously absent. A stark comparison of key performance indicators since 2020 indicates that the much-hyped AI push has yet to deliver a transformative step-change in per-employee performance across both global and Indian IT giants.
The Sobering Reality Behind the AI Hype
Data compiled by Ramkumar Ramamoorthy, a partner at growth advisory firm Catalincs, paints a revealing picture. The analysis tracks pre- and post-pandemic productivity, particularly following the landmark launch of ChatGPT in November 2022. The findings are clear: despite significant external tailwinds like merger-led expansion and a sharply depreciating Indian rupee, core productivity metrics have largely stagnated.
The top 10 Indian IT services firms collectively spent approximately $4.3 billion on acquisitions over this period, highlighting a heavy reliance on mergers and acquisitions to fuel growth rather than organic productivity leaps. In the first nine months of the current fiscal year alone, Infosys, HCL Tech, and Wipro deployed around $1 billion to acquire seven companies. Cognizant made major moves in 2024, including its $1.3 billion purchase of US engineering firm Belcan and a $430 million acquisition of ServiceNow partner Thirdera, followed by the buyout of 3Cloud to boost its Azure capabilities.
Furthermore, firms have aggressively pursued large deals, promising significant productivity gains. A tactic to boost revenue growth has involved booking licence sales for software products like Salesforce and Workday directly into their financial statements.
A Close Look at the Numbers: Giants in a Holding Pattern
The per-employee economics for industry leaders tell a consistent story of minimal movement. Accenture's revenue per employee saw only a marginal increase, from $86,191 in August 2020 to $89,408 in August 2025, despite the firm averaging over a dozen acquisitions per year. Its EBIT per employee grew by a mere 3.6% across the same five-year span. Notably, Accenture—a pioneer in explicitly disclosing AI revenue—has now decided to stop reporting it separately, arguing AI is now deeply embedded across its core offerings rather than existing in standalone projects.
Among Indian peers, Infosys demonstrated the strongest structural improvement, with revenue per employee rising from $53,591 in September 2020 to $59,297 in September 2025. However, its EBIT and net profit per employee stayed largely flat. Tata Consultancy Services recovered from its post-pandemic dip, lifting revenue per employee from $47,434 to $50,380, but its EBIT per employee remains only slightly above 2020 levels. In an interesting contrast, TCS recently highlighted its AI revenue for the first time, with CEO K Krithivasan announcing AI services have reached $1.5 billion in annualised revenue, growing 16.3% quarter-on-quarter.
Cognizant's revenue productivity remained essentially flat over five years, though EBIT per employee improved via cost control measures. Wipro continues to lag, with both its revenue and EBIT per employee in 2025 still trailing or barely matching their 2020 levels.
The Path Forward: From Promise to Measurable Impact
Ramkumar Ramamoorthy summarised the situation succinctly: "Despite massive forces such as large acquisitions, extreme offshoring driven by the pandemic and recent immigration changes, and a sharp depreciation of the rupee, metrics like revenue per employee, EBIT per employee and net profit per employee remained largely flat for large IT services companies."
However, he offers a crucial perspective for the future. This sobering picture does not mean AI's potential is a myth. Instead, it indicates that firms are still in the early, foundational stages of building AI-ready talent and scalable, enterprise-grade use cases. The real inflection point will come with large-scale deployments.
"Only when enterprise-grade deployments happen at scale, these metrics move meaningfully," Ramamoorthy added. "The next 12–24 months will be pivotal in translating AI’s promise into measurable business impact." For the IT industry and its investors, the call to action is clear: move beyond vague adoption percentages and demand audited, financial proof of AI's value. The hour of accountability for AI's return on investment has begun.
