India’s IT sector needs a new talent architecture

The latest technology-services earnings season points to growing client appetite for AI-related services and AI-led transformation. Yet, with few exceptions, providers have struggled to convert that demand into material revenue at scale. The size of the opportunity makes this conversion gap consequential. Gartner estimates that the global direct AI services market will reach $592.5 billion by 2030, growing at a compound annual rate of 15.7%. Much of the industry, however, remains at the starting line. A significant part of the constraint is structural, as the sector is trying to capture tomorrow’s opportunity with yesterday’s workforce architecture.

The prevailing talent model was built three decades ago. Companies recruited graduates, trained them in defined technical skills and deployed them through standardised hierarchies. Process discipline, a robust talent pyramid and rapid demand fulfilment became sources of competitive advantage. AI is reshaping the market for which this model was designed. As coding, testing, documentation, reporting and support work become increasingly automated, clients expect providers to proactively identify opportunities, reimagine workflows and take greater responsibility for business outcomes. Meeting these expectations requires stronger problem-finding skills, deeper domain expertise and the ability to combine human judgement with AI.

Companies have invested heavily in training and certification, but most have left grades, job families, career paths and rewards largely unchanged. This puts the sequence the wrong way around. Companies must first redesign the work and define the roles needed for an AI-native market, and then build the skills required to perform them. Otherwise, they risk training people for jobs that are becoming less relevant.


An EY-Nasscom study examining more than 1,000 tasks across IT and business process management helps explain how work is changing. Some activities will be automated, freeing up time and capacity. Others will be augmented, enabling people to complete them faster. A third group can be amplified, allowing greater judgement, creativity and context to be applied to complex work.

Consider a software engineer who writes standard code, tests applications and prepares documentation. As AI takes on more of these activities, the position can evolve into a broader solution-engineering role, using AI to deliver faster, address more of the client’s problem and take greater responsibility for outcomes. Skills, progression and commercial positioning must evolve with the role. Companies can redirect the released effort towards higher-value services, provided career paths and performance measures reflect the emerging division of labour between people and AI.

Many talent systems still favour tenure, annual grade-based increments and progression into people management. Technical specialists often find that the clearest route to advancement is to become managers and generalists, even as clients place greater value on domain expertise and niche technical skills. Talent architecture must address this mismatch. Three priorities should lead the agenda.

Redesign jobs and simplify the structure. Companies should consolidate overlapping job families into capability-based pools, eliminate duplication and decide which positions should be elevated, combined or created. As AI automates project reporting and governance, for example, a project manager can evolve into a technical delivery leader accountable for productivity, engineering quality and client outcomes.

Link progression and rewards to capability and value. Technical pathways should offer the same status and earning potential as managerial careers. An AI specialist should advance based on automation impact, reusable intellectual property and contribution to AI-led revenue, without having to become a people manager. Promotion and compensation should reflect the role performed, the skills demonstrated and the value created rather than tenure alone.

Align enterprise systems with the new model. Redesigned jobs must shape recruitment, staffing, learning and performance management. A “solution engineer” title achieves little if the company continues to hire narrow developers, deploy people through legacy skill codes and recognise them mainly for team size and years served.

Together, these shifts can create substantial value. Based on our work, an enterprise-wide redesign could reduce people costs by 8–10%, increase the proportion of specialists by 30-35% and support 25-40% higher bill rates for selected roles. The result would be a workforce aligned to higher-value demand, with deeper technical expertise, improved commercial yield and more meaningful careers.

Talent architecture redesign should now rank among the sector’s most urgent strategic priorities. It will enable providers to build differentiated capabilities, capture a larger share of the new revenue pools created by AI and improve commercial returns through greater pricing power. Providers cannot lead in an AI-native market while relying on a workforce model designed for an earlier era.

Nitin Bhatt is Technology Sector Leader and Neha Sharma is People Consulting Partner, EY-India