AI is reshaping the economy: Can India's education system keep pace?

Artificial intelligence (AI) is often seen as a technical marvel — powering new apps, automating jobs, and shaping the future of software and devices. But this perspective misses the larger picture. AI is not merely a technological leap; it is transforming how economies operate. Much like electricity or the internet did in earlier eras, AI is altering the very cost structure of decision-making.

 

Economists Ajay Agrawal, Joshua Gans, and Avi Goldfarb argue that AI’s primary impact lies in dramatically reducing the cost of prediction. Just as cheaper computation drove the IT revolution, the falling cost of prediction is now enabling machines to perform tasks that once required human expertise — such as diagnosing diseases, detecting fraud, or assessing creditworthiness.

 

Yet prediction is only one component of decision-making. AI does not possess human judgment, an understanding of incentives, or the ability to navigate the moral and governance dilemmas inherent in real-world decisions. In this sense, AI is not a replacement for human intelligence, but a powerful new input into the economic machinery. It changes how decisions are made and who makes them.

 

This shift has far-reaching consequences — not only for technology firms and economists but for countries, institutions, and education systems. Societies must now choose whether to be active participants in shaping AI’s trajectory or passive consumers of solutions built elsewhere. For India, this is no abstract debate. With over half its population under 25 and nearly 65 per cent under 35, the country stands at a demographic crossroads. This young population could become the world’s largest AI-ready workforce — or face large-scale displacement in a job market transformed by automation.

 

To seize this opportunity, India must urgently reimagine its education system. Even premier institutions such as the IITs, IIMs, NITs, and IIITs often struggle with outdated curricula, sluggish adaptation, and rigid pedagogies. Many students turn to online platforms, coding bootcamps, and independent projects to gain relevant skills. Classroom lectures are frequently treated as formalities — attended for grades, not learning.

 

The challenge extends well beyond elite institutions. Across more than 1,000 universities and colleges in India, AI is still missing from most syllabi. A handful of universities are beginning to offer AI modules, but foundational training in AI and data science remains rare. As a result, a significant share of India’s youth may be ill-equipped to participate meaningfully in the AI economy.

 

The urgency becomes even clearer when we consider changes in entry-level employment. Increasingly, companies are automating tasks traditionally assigned to fresh graduates — basic coding, data entry, and document drafting — using AI tools. These roles have long served as stepping stones, enabling new entrants to gain workplace experience, understand norms, and learn on the job. Economist James Bessen has emphasised the value of this “learning by doing” in helping workers thrive amid technological shifts. If such roles disappear before the education system adapts, millions may be locked out of crucial learning opportunities.

 

This is a structural crisis in the making. Cosmetic changes or superficial reforms will not suffice. India’s higher education system needs a deep overhaul — one that redefines what education must accomplish in the AI era.

 

We must move beyond viewing technical skills and broad academic education as opposing goals. A successful AI-era education will demand both. For instance, programmers must understand algorithms from first principles — even if AI tools can generate code. Economists need a solid grasp of market dynamics before they can analyse how AI affects employment or inequality. The same holds true in management, law, journalism, and public policy: every field will need professionals who can both use AI tools and critically evaluate their consequences.

 

This requires breaking down rigid disciplinary boundaries. Rather than siloed courses in computer science, economics, or sociology, universities must offer integrated programmes exploring AI’s intersections with society, institutions, and governance. Encouragingly, some institutions are already pointing the way.

 

At the global level, MIT’s Schwarzman College of Computing exemplifies how computing education can be embedded across disciplines. Students from economics, urban planning, biology, and political science engage with AI and data science not as isolated skills, but as tools deeply tied to their domain. In India, IIIT Delhi has developed several forward-looking BTech programmes that combine computer science with other domains — such as Computer Science and Social Sciences, Computer Science and Economics, and Computer Science and Biosciences. These programmes are designed to prepare students who can not only build AI systems but also understand their implications for society, policy, and the life sciences.

 

More institutions must follow suit. New degree programmes blending engineering with social sciences — covering algorithmic governance, data capitalism, and AI’s impact on labour — are essential. These must be paired with foundational courses in AI and data literacy, made accessible across disciplines and delivered in multiple Indian languages.

 

But theory alone is not enough. Students must become practical problem-solvers. Education should prepare them to work with real data in messy, real-world contexts — whether optimising crop yields, analysing public services, or auditing fairness in algorithmic credit scoring. This will require mentoring systems, flexible curricula, and incentives for faculty to promote applied learning. Elite institutions can foster industry partnerships to support internships and projects. Colleges in smaller towns can focus on local challenges, offering meaningful ways for students to apply what they learn.

 

Equally critical is the ability to think clearly and critically. In an era of deepfakes, algorithmic manipulation, and persuasive misinformation, students must be equipped to question the data and tools they use. They must be taught not just how to build or apply AI systems — but how to interrogate and scrutinise them. This has always been a core goal of higher education, but the AI era raises the stakes dramatically.

 

As James Bessen’s research shows, workers do not benefit automatically from new technologies. Productivity and wage gains materialise only when workers acquire the skills to implement and adapt these technologies. For India, this means placing probability, statistics, computing, and data analysis at the core of education across all disciplines — not just in engineering or computer science.

 

Moreover, India must resist turning its educational institutions into coding factories serving global demand. The goal should not be to flood the labour market with routine technical workers. Rather, we must cultivate a generation of thinkers — teachers who can explain the ethical implications of algorithms, civil servants who demand accountability in automated systems, and entrepreneurs who design inclusive and transparent AI platforms.

 

AI’s development is path-dependent: early choices in education and capacity-building will shape India’s role in the AI economy for decades to come. If we invest only in technical skills without fostering broader understanding, we risk ceding leadership to others. But if we build a thinking society — one that grasps how AI reshapes decisions, work, and governance — India can lead not just in building AI systems, but in deciding how they are used.

 

The challenge is immense, but the opportunity is greater. India doesn’t just need a workforce trained in AI — it needs a generation ready to think with AI. That transformation must begin in our universities. 

 

Vimal Kumar is a professor in the Department of Economic Sciences and Wadhwani School of Advanced AI & Intelligent Systems at IIT Kanpur and does research related to the digital economy.

 

Bikramaditya Datta is an associate professor in the Department of Economic Sciences at IIT Kanpur and specialises in finance-related research.

 

Disclaimer: These are the personal views of the writers. They do not necessarily reflect the opinion of www.business-standard.com  or Business Standard newspaper.