Need to think systemically to build AI skills for our students

This article is authored by Kamlesh Vyas, partner, Deloitte India.

India's Gen Z and millennials rank among the more Artificial Intelligence (AI)-confident young workforces anywhere. Deloitte's Global Gen Z and Millennial Survey, in its 15th edition released in May 2026, found that 85% of Indian Gen Z respondents and 91% of millennials feel confident applying AI in their work, and 93% to 95% use AI tools regularly on the job.

Artificial Intelligence

That competence is mostly self-built.

Just 32% of Gen Z respondents and 35% of millennials have completed any formal AI training. A good part of what they know, they picked up through online tutorials, trial and error, and peer learning at work, not through a programme designed to get them there. That is a strong starting instinct in a young workforce, and it is not the same as a formal and structured learning path with hand holding that builds AI capability, at scale, in its broader context and for everyone rather than for the self-directed people amongst them.

This gap in structure shows up downstream too. Deloitte and NASSCOM projects that India's AI talent pool will grow from roughly 600,000 to more than 1.25 million professionals by 2027. That is real growth, and the AI market itself is expanding faster still, at an estimated 25% to 35% a year over the same period, according to industry estimates. Supply is growing. Demand is growing faster. Closing that gap needs structured, formal pathways at scale, not individual initiative and confidence alone.

The World Economic Forum's Future of Jobs Report 2025 found that nearly 40% of core job skills are expected to change by 2030, and that 63% of employers already cite skill gaps as the biggest barrier to using AI effectively inside their organisations. That shift has implications for higher education institutions, and it calls for the same response employers are building: structured reskilling, planned, budgeted and repeated, not a one-time orientation.

Much of the current AI capacity building efforts treat AI as a separate vertical: A standalone elective, a certificate module added alongside the regular curriculum, a workshop outside the syllabus rather than inside it. These are reasonable first moves, and they work better as a beginning than as the whole strategy. The stronger approach integrates AI into the core courses and modules students already take, so that using AI well becomes part of how a subject is taught and assessed, not a separate skill picked up on the side. A student whose main exposure to AI is a bolt-on elective has not yet learned to apply it inside their own discipline.

Needless to say, AI literacy building efforts need to be complemented with key skills like critical thinking. Collaboration, communication and with domain depth.

The formal and structured path with hand holding and institutional backing for students is just as necessary for the faculty members.

Faculty are the multiplier, not the constraint. Give them the same structured runway and the benefit reaches every classroom immediately.

Competence compounds inside an institution much as it does inside a country: Each faculty cohort that gets this structured support early makes the next round of adaptation easier even as new tools come up and obsolete technologies make for new ones.

Student and faculty capacity-building combined with structural initiatives in curriculum, assessments, pedagogy, and industry collaboration can create large impact and prepare our students for the AI era.

Just including AI into current curriculum is sub-optimal, we would need to rethink what we teach with greater emphasis on multi-disciplinarity and thinking with AI rather than just using AI tools.

As the role of universities evolves from places where knowledge is disseminated to where students and faculty come together for collective learning and building, pedagogies and assessments need to change. Greater focus is required on projects, start-ups, debates, deliberations, mentoring, peer learning and co-curricular activities.

A curriculum cannot outpace the people who deliver it. Therefore, there is a need for structural pairing of every revision with a matching faculty development cycle and giving departments the time to redesign assessments, pedagogy and teaching-learning materials.

Efforts in building AI-ready graduates also need a stronger and closer industry-academia collaboration.

National Education Policy (NEP) 2020's curriculum and credit framework encourages flexibility, interdisciplinary study. That flexibility is exactly what integrated, faculty-supported AI readiness needs, and it gives institutions room to rebuild how a course is taught, not just what it covers.

The whole-of-the-institution efforts to build AI capacity need leadership buy in and participation. Therefore, leadership orientation to leveraging AI for higher education could be a good place to begin.

Every past wave of technology rewarded the institutions that built structured pathways for all their people early, not just the ones that adopted fastest. India has the policy, intent, resources, talent and capacity achieve it.

(The views expressed are personal)

This article is authored by Kamlesh Vyas, partner, Deloitte India.