A campus can put AI tools in a lab, add a module to a syllabus, and still leave students guessing about what counts as competent work. That is the uncomfortable gap behind the new readiness conversation in Indian higher education. EY’s page on “Building AI readiness in Indian higher education” puts the emphasis in the right place: not whether AI appears on campus, but whether institutions can turn exposure into capability.
For learners, that distinction matters because the labor market is already allergic to vague AI claims. “Used ChatGPT for assignments” is not a portfolio. A stronger signal is a documented workflow: what problem you scoped, what data or source material you used, how you checked outputs, what you disclosed, and what you improved after review.
EY’s readiness signal is bigger than tool access
EY’s “Building AI readiness in Indian higher education” is useful because it names readiness as the problem, not just adoption. That may sound like a semantic shift, but it is the difference between buying software and changing how people learn, assess, and govern work. Indian HEIs that stop at access risk producing students who know the vocabulary of AI but not the operating habits around it.
This is where credential inflation sneaks in. A short AI certificate can teach terms, but it does not prove that a student can evaluate model output, protect sensitive information, or explain when not to automate a task. If universities want their graduates to stand out, the evidence needs to move from badges to artifacts: projects, policies, evaluation notes, and faculty supervised use cases.
Faculty readiness is part of the career pipeline
Sanskriti Rawat and Robert Linton Tavis Ashton-Bell of Christ University make the faculty side explicit in “Responsible AI-Readiness in Higher Education,” published in the Journal of Information Technology Education: Research in 2025. Their study validates a responsible AI framework designed to strengthen AI readiness among higher education faculty in India. That is a career story as much as an education story, because faculty practice shapes what students learn to treat as normal.
If instructors receive uneven support, students receive uneven signals. One class may require disclosure of AI use, another may ban tools without teaching alternatives, and a third may allow AI generated work without verification. The result is familiar to anyone reading entry level résumés: lots of AI language, not enough evidence of judgment.
Governance is becoming a workplace skill
The OECD frames artificial intelligence as a governance challenge: how to apply effective governance to harness benefits and mitigate risks. For students and early career workers, that is not abstract policy language. It is the difference between being someone who can use a tool and someone who can be trusted with one in a real organization.
This is especially important for nontechnical roles. A student in commerce, law, design, education, or public policy may never train a model, but they may still need to review outputs, document assumptions, spot bias risks, and know when human review is required. The practical upskilling path is not “become an AI engineer” by title sprawl. It is learning enough AI literacy to work responsibly inside your domain.
Readiness should be measured, not performed
GivingTuesday’s India AI Readiness Survey Report 2024 offers a useful definition outside the university setting. It describes AI readiness as measuring who is already using AI, how it is being used, and which organizational traits are linked to adoption. Indian HEIs can borrow that discipline: map actual use, name barriers, and stop treating scattered pilots as a strategy.
That also gives students a clearer way to choose where to invest time. Look for programs that require projects with evaluation, not just prompt lists. Ask whether faculty receive AI training, whether assessment rules are clear, and whether students learn governance alongside tools. The next hiring signal will not be the loudest AI label on a résumé. It will be the ability to show careful, repeatable work when the tool is only one part of the job.