India is on the verge of Artificial Intelligence (AI) leap. The technical sector of the country is growing. From Bengaluru to Hyderabad, startups are thriving and investment in digital infrastructure is accelerating. Yet there is a significant obstacle standing in the way of this momentum: the growing AI talent gap. As employers repeatedly warn: “We can find coders. We need AI professionals who bring context, ethics, and communication to the table.”
This is not a uniquely Indian challenge, but it has special significance in India. A recent report by Bain & Company warns that by 2027, India’s AI jobs could reach 2.3 million, while the supply will remain at only 1.2 million and more than a million roles will remain unfilled unless it rapidly reskills or upskills. Already, salaries for AI-savvy freshers are four times higher than standard entry-level salaries, showing how rare and valuable adaptable talent has become
The solution lies not in producing more coders, but in changing how we educate the AI professionals of tomorrow. They must be ethical, interdisciplinary problem-solvers equipped to work in complex real-world contexts. It calls for a radical rethinking of the curriculum with five key pillars:
Five pillars of a future-ready curriculum:
- Technical proficiency is necessary, not sufficient.
Core skills required are in algorithms, machine learning and data science. But if graduates stop there, employers have to invest heavily in retraining or look for solutions that cut the cost of simple coding. The recent rollout of the “Poly-AI” system by Infosys, which has reduced manpower needs by 35%, shows why companies are now looking for professionals who can add value immediately, not just code theoretically. - Interdisciplinary foundations and flows.
AI should be taught in real contexts. In India, this could mean creating crop-disease detection tools using drone data, or designing a fintech platform that provides credit access to small entrepreneurs. Financial regulators are already moving. RBI’s proposed Free-AI framework aims to balance innovation with risk safeguards in banking and finance. For engineers entering the banking, financial services and insurance industries, this means employers now expect technical ability as well as fluency in the regulatory landscape. Similarly, agritech startups need graduates who understand weather and soil data, not just drone imagery. Without it, businesses face costly delays and policy missteps. - Ethics and social context.
The IndiaAI Mission and the creation of the AI Safety Institute highlight that policymakers view fairness, impartiality and accountability as national priorities. For businesses, these concerns translate into reputational and financial risks. Incorporating ethics into every stage of education helps graduates design systems that do not have adverse effects in the boardroom or legislature. - Communication and stakeholder engagement.
AI projects often fail because insights remain cloaked in technical jargon. Employers need engineers who can explain predictive models to managers, regulators or customers in the language they trust. Whether it’s public health algorithms or rural credit scoring tools, communication makes the difference between adoption and abandonment. HR leaders are increasingly citing this as the missing skill in their hiring pools. Domain expertise and market knowledge are essential for any AI tool to be effective.
Project-based, real-world learning.
Businesses want portfolios from students where they have demonstrated their abilities to tackle real challenges. Capstone (real-life experience-based) projects that simulate real challenges like optimizing traffic flow in Indian cities, predicting floods in Assam, or automating compliance reporting in banks produce graduates who are industry-ready from day one. For policymakers, this ensures that educational investments directly align with national development goals, from resilient agriculture to renewable energy. For academics and administrators, it also means skilling the faculty who can share such experiences with students.
To close the AI talent gap, curriculum reform must become a national priority and be viewed not as an academic exercise, but as an economic and strategic imperative. Universities should incorporate interdisciplinarity, ethics and project-based learning into core programmes. Employers should co-create curriculum, provide real-world problem statements, and reward technical speed as well as judgment and communication. Policymakers can accelerate progress by encouraging academic-industry collaboration, ensuring that AI education aligns with India’s broader development agenda.
With the right long-term approach, India’s huge STEM graduate base can be harnessed not only to meet domestic needs but also to shape the global AI workforce. But if they remain narrowly trained, India risks creating a generation of coders who can be easily replaced by automation, rather than adaptive professionals who can lead.
This article is written by Anna Marbut, Professor of Practice in Applied Artificial Intelligence, and GB Singh, Academic Director of Engineering Management and Leadership at the University of San Diego.
