Artificial Intelligence (AI) is changing the way we think and work, the skills to acquire and competencies to develop and maintain.
Most people who work with AI do not really need AI skills or knowledge of how such systems function, and there is very little insights or research out there exploring how artificial intelligence will change the skills demanded from the workers.
Big tech companies in the United States are in an AI-race, and the workers behind the scene are mainly Indians. And now, India is tapping into its highly-sought after engineers to put the country first. But with the majority of young and skilful workforce engaged with bigger and much higher paying corporates, does India have a problem.
Prashanth Tondapu, Founder & CEO of Innostax, tells OceanBluMedia that India does not have a raw talent problem.
The problem is exposure and experience.
We produce roughly 8.3 lakh engineering and technology graduates a year, and India already ranks among the world’s strongest markets for AI skills. The issue is not intellectual ability. The issue is how many engineers have actually had the opportunity to own difficult, production-grade problems.
In AI, there is a huge difference between knowing the technology, building a proof of concept, and being responsible for a system running in production. That requires judgement across data architecture, security, privacy, model evaluation, hallucination management, latency, cost, governance and business outcomes. That capability cannot be created through a course; it comes from repeated exposure to difficult problems.
The same pattern appears elsewhere. In sustainable technology, India will scale solutions fastest when they are not merely greener, but cheaper, better and more accessible than the alternative. In biotechnology, India has already proved its ability to manufacture at global scale; the next opportunity is to move from scaling other people’s discoveries to originating more of those discoveries here.
So India can simultaneously have enormous technical talent and a shortage of people ready to build frontier systems.
The missing ingredient is not brainpower. It is experience density.
India’s next leap will come from giving more engineers and scientists ownership of architecture, product decisions and outcomes – not simply more specifications to execute.
No Shortage for AI
India is not fundamentally short of engineers. What is scarce is the combination of engineering depth, domain understanding, production experience and judgement required to take AI from an impressive demonstration to a dependable system.
India already has one of the world’s largest technology workforces, with AI skill penetration among the highest globally. The real question is not how many people can build with AI. It is: how many can be given an ambiguous business problem, make the right architectural decisions, put the system into production and remain accountable for what happens afterwards?
That is a much smaller pool.
Building AI prototypes has become dramatically easier. Production AI is different. Engineers must decide whether AI should be used at all, where human oversight is necessary, how to handle hallucinations, security and data leakage, how to evaluate systems continuously, control cost and latency, and ultimately who is accountable when the system fails.
Production-grade AI is therefore as much a judgement problem as an engineering problem.
This is why India’s challenge is best understood as an experience-density problem. Courses can teach tools; judgement comes from owning difficult systems, making decisions under uncertainty and living with the consequences.
AI itself will make average coding ability increasingly abundant. The premium will shift towards knowing what should be built, how it should be built and whether it can be trusted. India’s challenge now is to convert technical talent into technical judgement.
Also Read: AI Agents Could Outnumber Human Workers Globally by 2029
The Gaps
The three biggest capability gaps in India’s engineering workforce today are technical judgement, systems thinking and product ownership, says Tondapu.
First, technical judgement. India has no shortage of engineers who can execute a clear requirement. The scarcer capability is being given an ambiguous problem, deciding what the real problem is, making trade-offs and remaining accountable for the outcome.
In AI especially, knowing how to build a model is not the same as knowing whether AI should be used, how it can fail, and what happens when it does.
Second, systems thinking. Modern enterprise technology is interconnected across AI, data, cloud, security, APIs, infrastructure and operations. The most valuable engineers increasingly understand not just their component but also the second-order consequences of decisions across the whole system.
Third, product and business understanding. The engineer of the future cannot simply ask, ‘What do you want me to build?” They need to ask, “What are you actually trying to accomplish?’
India’s challenge is therefore not simply a skills shortage. It is an exposure shortage.
We already have the raw talent. The next step is to give more engineers ownership of architecture, product decisions and real business outcomes.
The next generation of Indian engineers should not just execute somebody else’s technology roadmap; they should be capable of writing it.
Heart of the Problem
A meaningful part of the AI talent problem is really a data problem, but it is important to distinguish that from an infrastructure problem.
Infrastructure is probably the easier capability to scale. Cloud architecture, deployment patterns, observability, security controls and MLOps increasingly have established standards, proven tooling and repeatable reference architectures. Good engineers still matter, but much of the knowledge can be documented, taught and replicated.
Data is different. AI systems are only as useful as the data, context and feedback loops behind them. Engineers need to understand what data matters, whether it is trustworthy, how it should be structured, who owns it, what can legally be used and how quality changes over time. Those decisions require far more judgement.
But the biggest constraint is still exposure.
India has a large pool of engineers capable of learning AI, data and infrastructure. The challenge is ensuring they get to see the kinds of problems being solved at the frontier: how leading teams evaluate models, architect data systems, manage failure modes, control cost and decide what should, or should not, be automated.
You cannot create frontier capability by teaching yesterday’s best practices. Engineers need visibility into today’s hardest problems.
So, the solution is not simply more infrastructure talent. It is increasing the experience density of the entire engineering workforce.
The Mistake
The executive believes the biggest mistake Global Capability Centre (GCC) leaders make is assuming that building a GCC means taking job descriptions from the parent company, hiring people in India and then handing performance back to headquarters.
That builds a remote workforce. It does not build an integrated GCC.
The real challenge begins after hiring. Teams across geographies often differ in communication styles, expectations around ownership, feedback culture, management practices and escalation. If those differences are not actively bridged, even technically strong teams can underperform.
When engineers are exposed to the right operating framework, their output changes significantly. Instead of thinking only in terms of tickets to be completed, they begin to understand what the client is actually trying to achieve, why their work matters and how their decisions affect the broader product or business outcome.
That context creates ownership. And when ownership increases, there is typically better judgement, stronger productivity and better overall performance.
This is why GCCs should be viewed as a managed operating model, not a recruitment service. The objective is to combine technical leadership, people leadership and regular feedback between the client and the India team.
The most important job of a GCC partner is not filling positions. It is reducing the cultural and operational distance between two organizations until engineers stop feeling like an offshore team and start behaving like owners of the outcome.
Recommendations
Tondapu suggests stop treating the AI talent problem primarily as a hiring problem.
India already has enormous engineering capacity. The bigger challenge is creating experience density: giving more engineers repeated exposure to ambiguous problems, production systems and decisions where the consequences are real.
That means companies should move more than implementation work to India. They should move architecture, product ownership, data responsibility, R&D and decision-making authority as well. You do not develop judgement by executing somebody else’s specification.
Second, organizations should upgrade their existing engineering workforce rather than building isolated AI teams. AI/ML is a skill; AI readiness is an organizational capability. Engineers across software, data, cloud, security and product need to understand how AI changes architecture, reliability, economics and risk.
Third, it is necessary to change how we evaluate talent. Knowing frameworks and models matters, but we should increasingly test whether engineers can define an ambiguous problem, challenge assumptions, make trade-offs and remain accountable for outcomes.
Finally, GCCs must evolve beyond recruitment. Hiring creates capacity; leadership, feedback and ownership create capability.
The companies that win will be those that systematically expose engineers to harder problems and give them responsibility earlier. India does not need another million engineers who have learned AI. It needs far more engineers who have learned how to make good decisions with AI.
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