From AI adoption to readiness: Preparing Indian higher education for the AI era
The October 2026 FICCI and EY-Parthenon report, “From AI adoption to readiness: Preparing Indian higher education for the AI era,” follows up the 2025 baseline study and argues that India’s campuses have moved from experimenting with AI to living with it, without institutions keeping pace. Drawing on interviews and focus groups across more than 23 institutions and over 61 stakeholder interactions, plus secondary research, it finds that student adoption is nearly universal: about 23% of students use AI daily, 43% several times a week, and only 1% never. Most of that use sits in a “gray zone” where policies are unclear, so students set their own norms, raising concerns about dependency, weaker critical thinking and polished work that hides gaps in understanding. Institutions are described as following four trajectories (implementation, announcement, informal adoption and stalled adoption), and the report warns of “performative adoption,” where workshops and policies exist but classroom practice and assessment barely change. Policy momentum has grown, including the DPDP Rules of November 2025, the YUVA AI for ALL literacy programme and the Bharat EduAI Stack. Faculty are responding by moving towards vivas, projects and in-class evaluation. Employers, meanwhile, want more than tool fluency: they value judgment, verification and sound fundamentals, and an estimated 37% of entry-level tasks in India are already performed by AI. Benchmarking against global universities, the report finds that leaders translate policy into practice through clear decision rights, institutional tools and AI fluency as a core graduate attribute, while Indian progress is uneven. It proposes redefining academic integrity around intellectual ownership rather than technological purity, a national readiness framework built on the “light but tight” principle with a minimum baseline for every institution, and a phased 2026–2030 horizon. Its framework rests on five pillars: democratizing AI, deepening adoption, shared digital infrastructure, stronger industry-academia collaboration and learning from global practice. Detailed playbooks for students, faculty, leaders, employers and policymakers keep equity central, since unequal access to private tools widens existing advantages. The closing vision is a “Human + AI” campus rather than an AI-driven one, where success is measured by whether AI strengthens learning, critical thinking and equitable opportunity, not by the number of licences, pilots or policies.
Key Takeaways
- AI adoption has outpaced institutional readiness: AI is already embedded in Indian campuses, but widespread use should not be mistaken for preparedness.
- Student use is near-universal: about 23% of students use AI daily, 43% several times a week and only 1% never.
- Most student use falls in a “gray zone” with unclear policies, so students are defining their own norms.
- Key risks include dependency, weaker critical thinking, eroding confidence in independent work and polished outputs that mask learning gaps.
- Institutions follow four trajectories: implementation, announcement, informal adoption and stalled adoption.
- “Performative adoption” is a concern, where policies and workshops exist but classroom practice and assessment remain unchanged.
- Assessment is the key area of change, with faculty shifting towards vivas, projects, reflective work and in-class evaluation.
- Academic integrity should rest on intellectual ownership and accountability rather than on whether AI was used.
- Employers value judgment, verification and strong fundamentals over prompting skills alone; an estimated 37% of entry-level tasks in India are already performed by AI.
- Policy momentum is growing, including the DPDP Rules (November 2025), YUVA AI for ALL and the Bharat EduAI Stack, but implementation remains uneven.
- Leading global universities turn policy into practice through clear decision rights, institutional tools and AI fluency as a core graduate attribute.
- A national readiness framework is needed, following the “light but tight” principle, with a minimum baseline for every institution and a phased 2026–2030 horizon.
- The foreword outlines a five-pillar framework: democratization of AI, depth of AI adoption, shared digital infrastructure, industry-academia collaboration and global best practices.
- Equity is central: unequal access to private AI tools widens existing advantages, and equal access alone does not ensure equal capability.
- The 2030 vision is a “Human + AI” campus, measured by stronger learning and critical thinking rather than the number of licences, pilots or policies.