E-Paper
Rethinking the curriculum for an industry-ready workforce
This article is authored by Anna Marbut and GB Singh.
India is on the brink of an Artificial Intelligence (AI) leap. The country’s tech sector is surging. Startups are flourishing from Bengaluru to Hyderabad and investment in digital infrastructure is accelerating. Yet amid this momentum lies a critical roadblock: a widening AI talent gap. As employers repeatedly warn: “We can find coders. What we need are AI professionals who bring context, ethics, and communication to the table.”
This is not a uniquely Indian challenge, but in India it has particular significance. A recent Bain & Company report warns that by 2027, India’s AI job openings could hit 2.3 million, while supply remains at just 1.2 million leaving over a million roles unfilled unless it reskills or upskills rapidly. Already, salaries for AI-savvy freshers are up to four times higher than standard entry-level pay, reflecting how rare and valued adaptable talent has become
The solution lies not in producing more coders but in transforming how we educate tomorrow’s AI professionals. They must be ethical, interdisciplinary problem-solvers equipped to operate in complex real-world contexts. This demands a radical rethink of the curriculum along five key pillars:
The five pillars of a future-ready curriculum:
- Technical mastery necessary, not sufficient.
Core skills in algorithms, machine learning, and data science are essential. But if graduates stop here, employers are left investing heavily in retraining or looking for solutions that cut costs of simple coding. Infosys’ recent rollout of “poly-AI” systems, which cut manpower needs by up to 35%, shows why companies now seek professionals who can immediately add value, not just code in theory. - Interdisciplinary foundation & fluency.
AI must be taught in real contexts. In India, this could mean building crop-disease detection tools using drone data, or designing fintech platforms that deliver 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 Service and Insurance industry, this means employers now expect fluency in regulatory landscapes alongside technical ability. Similarly, agritech startups need graduates who understand weather and soil data, not just drone imagery. Without this, businesses face costly delays and policy missteps. - Ethics and social context.
The IndiaAI Mission and the creation of an AI Safety Institute highlight that policymakers see fairness, bias, and accountability as national priorities. For businesses, these concerns translate into reputational and financial risk. Embedding ethics into every stage of education equips graduates to design systems that won’t backfire in the boardroom or the legislature. - Communication and stakeholder engagement.
AI projects often fail because insights remain locked in technical jargon. Employers need engineers who can explain predictive models to managers, regulators, or clients in language they trust. Whether it’s a public health algorithm or a rural credit scoring tool, communication makes the difference between adoption and abandonment. HR leaders increasingly cite this as a missing skill in their hiring pools. Domain expertise and market knowledge are imperative for any AI tool to be effective.
Project-based, real-world learning.
Businesses want portfolios from students where they have showcased their capabilities to address real challenges. Capstone (real life experience-based ) projects that simulate real challenges like optimising traffic flows 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 academic investments align directly with national development goals, from resilient agriculture to renewable energy. For academicians and administrators, this also means upskilling the faculty that 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 embed interdisciplinarity, ethics, and project-based learning into core programs. Employers must co-create curricula, provide real-world problem statements, and reward judgment and communication alongside technical speed. Policymakers can accelerate progress by incentivising academia–industry collaboration, ensuring that AI education aligns with India’s broader development agenda.
With the right long-term vision, India’s immense STEM graduate base can be molded to not only meet domestic needs but also shape the global AI workforce. But if they remain narrowly trained, India risks producing a generation of coders who are easily replaceable by automation, rather than adaptive professionals who can lead.
This article is authored by Anna Marbut, professor of practice, Applied Artificial Intelligence & GB Singh, academic director, Engineering Management & Leadership, University of San Diego.
