India’s AI Talent Shortage: Supply Constraints and the Assessment Challenge

HCLTech is building a cadre of Forward Deployed Engineers. TCS wants AI-native talent with critical thinking skills. Infosys has revamped campus hiring to prioritise generative AI and data engineering. Wipro is adopting skills-first hiring that values demonstrable capabilities over academic credentials. India’s IT services firms are hiring again, but this time they are chasing a different profile: engineers who can deploy enterprise AI systems, not just graduates who have used ChatGPT. The challenge is that the supply of industry-ready AI professionals is not keeping pace with demand.

According to the June 2026 edition of Naukri’s JobSpeak report, AI hiring within the IT sector rose 16 per cent year-on-year, even as overall IT job listings declined 3 per cent during the same period. Companies are building specialised AI talent pools and shifting to skills-first models. The demand is real. The execution, however, is stalling.

90% Use AI Tools, 23% Are AI-Native

According to a Nasscom report, more than 90 per cent of early-career technology professionals use AI tools, but only 23 per cent qualify as AI-native engineers with the technical depth and independent problem-solving skills needed to build and deploy AI systems. That is a 4:1 signal-to-noise problem, and it raises questions about how reliably hiring processes can distinguish between the two groups.

Why AI Talent Is Becoming Harder to Find

Nasscom estimates that India could face a shortage of more than 600,000 AI professionals by 2027. The shortage is driven by real supply constraints: AI is evolving faster than traditional university curriculum revision cycles, faculty development has not kept pace, curricula remain siloed while AI is inherently multidisciplinary, and the number of institutes with the faculty expertise, advanced computing infrastructure and industry ecosystems to deliver high-quality AI education at scale is relatively small. Growing competition from global capability centres, startups and multinational technology companies, coupled with the rapid evolution of AI models and programming frameworks, is also making it harder for India’s IT services firms to secure and retain experienced AI professionals.

ManpowerGroup’s Global Talent Shortage Survey 2026 found that 82 per cent of employers globally report difficulty finding the skilled talent they need. AI model and application development (39 per cent) and AI literacy (38 per cent) rank among the hardest capabilities to hire. When companies say they cannot find AI talent, part of the challenge is the genuine supply shortage the industry faces.

Reskilling Is Outpacing External Hiring for a Reason

One data point adds nuance to the supply-shortage narrative: companies report that reskilling is proving more scalable and sustainable than external hiring for many AI roles. According to Tushar Dhawan, chief executive officer of TrueSales, a growing share of enterprise AI requirements is now being addressed through internal reskilling because it is more scalable and sustainable. Alongside the real supply constraints documented above, this suggests that distinguishing AI literacy from AI-native capability in external hiring may also be difficult.

That does not mean external hiring is obsolete. Specialised roles in Agentic AI, MLOps and AI security still require experienced practitioners. But when internal reskilling programmes are delivering enterprise-ready AI talent at scale, it suggests companies may be finding ways to develop and validate capability that complement external recruitment.

The Talent Shortage Is Real, and Assessment May Also Be Hard

The talent shortage is not a hiring process illusion. India’s IT sector faces genuine supply constraints driven by curriculum lag, faculty gaps, infrastructure limits and competition for experienced practitioners. The source evidence is clear on this point.

At the same time, the gap between 90 per cent AI tool usage and 23 per cent AI-native capability suggests that identifying who can build and deploy AI systems, versus who has used AI tools, is not straightforward. If your interview structures assess familiarity rather than capability, or if your hiring managers are evaluating skills they do not fully understand themselves, you may struggle to recognise qualified candidates when they do appear.

The question is not whether AI talent is scarce. It is. The question is whether your hiring process can recognise it when it is sitting across the table, and whether improving assessment methods might help you make better use of the candidates already in your pipeline.

Original reporting: Business Standard.