The Tradition of the IT Bench
In the world of IT services, the 'bench' refers to employees who are on the company's payroll but are not currently assigned to a revenue-generating client project. For years, major Indian firms like TCS, Infosys, and Wipro maintained a significant bench,
sometimes as high as 20-30% of their workforce. This practice served as a strategic buffer, ensuring that when a new client contract was signed, a team of engineers could be deployed almost immediately without scrambling to hire new talent. It was a 'just-in-case' model, one that provided agility and readiness in a competitive market. However, this readiness came at a substantial cost, with companies paying salaries and benefits to a large number of idle, non-billable employees, which directly impacts profit margins.
The AI-Powered Pivot to Efficiency
The pressure to improve operational efficiency and protect margins, especially amid slowing revenue growth, has forced a major rethink of the bench. The primary driver of this change is the integration of Artificial Intelligence and predictive analytics into workforce management. Companies are now leveraging sophisticated AI tools to forecast project demand with far greater accuracy. These systems analyze historical data, market trends, and sales pipelines to predict what skills will be needed, where, and when. This allows for a more demand-driven, 'just-in-time' approach to staffing, moving away from the old model of speculative hiring. Instead of keeping a large, generic bench, firms can maintain a much leaner, more specialised pool of talent ready for deployment.
How AI Is Reshaping Resource Management
The role of AI goes beyond simple forecasting. It is being embedded into the core of human resources and operations. For instance, AI platforms can automatically map an employee's existing skills and certifications against upcoming project requirements. This ensures that the right person is allocated to the right project, optimising utilisation. Furthermore, AI helps identify skill gaps within the current workforce. If a certain programming language or cloud certification is predicted to be in high demand, the company can proactively launch targeted upskilling and reskilling programs for its existing employees, including those on the bench. This not only prepares the workforce for future needs but also makes the time spent on the bench more productive. Top firms like TCS, Infosys, and Wipro have already deployed AI tools like Microsoft 365 Copilot to tens of thousands of employees to boost productivity across the board.
The New Normal: A Leaner Bench
The results of this strategic shift are already evident. Across India's major IT firms, the average bench strength has reportedly dropped into the single digits or at most 12%, a significant reduction from the historical 20-30%. Projections suggest this could fall even further, to around 8-10% by fiscal year 2027. The time an employee spends on the bench has also been reduced, from a previous average of 45-60 days to a more compressed 30-45 days. This reflects a new industry-wide focus on maximizing employee utilisation—the percentage of staff actively working on billable projects. With AI driving productivity gains, companies can deliver more with fewer people, lessening the need for a large standby workforce.
The Human Impact and the Road Ahead
While a smaller bench is great for company balance sheets, it creates a new set of pressures for employees. The shrinking window of bench time means workers have less time to get allocated to a new project before facing uncertainty. There is a heightened emphasis on continuous learning and acquiring niche skills in high-demand areas like AI, cloud computing, and cybersecurity, as employees with legacy skills are seen as more at risk. The message from the industry is clear: upskilling is no longer optional, but essential for career stability. This transformation is not necessarily about more layoffs, but rather a concerted push towards reskilling the existing workforce to meet new demands. The IT professional of the future will need to be more adaptable and deployment-ready than ever before.
