Sourcing for GenAI Leadership: Overcoming the Talent Deficit in India's Deep-Tech Ecosystem

The mandate came in December 2024. A large financial services organisation building an enterprise AI platform needed a Head of AI Architecture. The role required someone who had worked on production-scale language model deployments, understood the full stack from model selection and fine-tuning to inference infrastructure and responsible AI governance, and could lead a team of twenty-odd engineers while also engaging credibly with the C-suite on AI strategy. Compensation was not the constraint. The constraint was that the candidate described in the brief existed in vanishingly small numbers in India.

This mandate is not unusual. Over the course of 2024, the share of Adviti's pipeline constituted by AI and machine learning leadership roles increased to above 20 percent. A year earlier, it was under 5 percent. The acceleration of enterprise AI adoption has produced a talent demand that the supply side cannot match, and the search dynamics that result are unlike anything else in the technology leadership market.

The Nature of the Deficit

India's AI talent story has two distinct chapters that are often conflated. The first chapter is one of genuine abundance. India produces an exceptional volume of machine learning engineers, data scientists, and AI researchers. The country's representation in global AI research, measured by contributions to leading academic conferences, has grown substantially over the last decade. The talent supply at the individual contributor level is real.

The second chapter, the one that matters for leadership mandates, is a story of scarcity. The number of individuals in India who have built and led production AI systems at enterprise scale, who have managed the full lifecycle from research to deployment to monitoring to governance, who have the technical depth to make architecture decisions and the leadership capability to build and develop the teams that execute them, is genuinely small.

The reason is historical. Large-scale production AI deployment in Indian enterprises only began in earnest around 2020 to 2022. Before that, most AI work in India was either research (which did not develop the operational and leadership dimensions of the role) or analytics and data science (which is adjacent to but distinct from production AI engineering leadership). The cohort of leaders who have the full profile is therefore only three to five years into their development, and many of them are still in individual contributor or small team lead roles.

Where the Talent Lives

The strongest AI leadership candidates in India today come from three primary sources. The first is the Bangalore GCC ecosystem, where a growing number of technology companies have built genuine AI infrastructure teams and where several senior leaders have accumulated the right combination of technical depth and organisational leadership. These individuals are well-networked, visible in the community, and actively pursued.

The second source is the Indian diaspora, specifically the cohort of senior AI practitioners at US and UK technology companies who have been considering a return to India and for whom the current moment represents a genuinely compelling opportunity. The timing is good: the domestic opportunity set has matured sufficiently to offer roles that are not a step down from what they are leaving.

The third source, underutilised and underappreciated, is the research community. India's premier research institutions have produced a generation of AI researchers who have increasingly been moving into industry, often in leadership roles. These individuals bring exceptional technical depth and, when they have navigated the transition from research to applied engineering well, a perspective on AI architecture that practitioners who came up entirely through industry sometimes lack.

Assessment Criteria for AI Leadership

The assessment framework for AI leadership roles has to work harder than standard technology leadership assessment, because the domain is new enough that conventional proxies are unreliable. Many candidates are skilled at presenting AI fluency without demonstrating it. Many organisations are not yet sophisticated enough in the domain to probe effectively.

Adviti's assessment approach for AI leadership roles includes a technical architecture discussion conducted or reviewed by a genuine AI practitioner, not a general technology leader. The discussion probes specific decisions the candidate has made in production systems: model selection rationale, fine-tuning versus retrieval-augmented generation trade-offs, inference cost management, monitoring and observability design. Candidates who can speak to these decisions with specificity have been in the work. Those who speak at a conceptual level may not have.

Equally important is the governance and ethics dimension. Enterprise AI leaders are increasingly required to navigate responsible AI frameworks, bias auditing, and explainability requirements. Candidates who are technically strong but have not thought seriously about the ethical dimensions of the systems they are building are a material risk for organisations in regulated industries.

What Organisations Can Do

For organisations facing the GenAI leadership deficit, the strategic options are limited but meaningful. The first is to develop from within: identify the strongest AI engineers in your current team and invest systematically in developing their leadership capability. The second is to hire for adjacent capability and bridge the gap: a strong technology leader with genuine AI curiosity and the organisational standing to bring in strong technical advisors can be more effective than a technically perfect candidate who cannot lead.

The third option, and one that Adviti is increasingly advising clients to consider, is to structure the role differently. A fractional or part-time AI architecture advisor who provides the technical direction while a full-time engineering leader handles the operational and people dimensions can, in some contexts, deliver better outcomes than searching indefinitely for the unicorn profile that combines both.