From claims to evidence: Why your university's AI readiness needs to be measurable

“AI-ready” is quickly becoming a familiar phrase in higher education. Universities are launching new courses, setting up AI classrooms and labs, training faculty, and announcing new tech initiatives. These are important steps, but they also raise a question: how much of this actually changes the experience of a student?

This is where measurement becomes important. ET AI-Ready brings this conversation into focus by giving universities a structured way to assess their preparedness rather than relying only on what they claim to be doing.

Consider a university that has invested heavily in AI infrastructure. It may have the computing resources and the latest tools, but if students have limited access to them, the investment doesn't say much about their learning experience.


The same applies to curriculum. Having one AI elective or a specialised programme does not necessarily mean students across the university are gaining the skills needed to work in an AI-enabled economy. A business student, designer, lawyer or a management graduate may encounter AI very differently from a computer science student. Readiness therefore, has to be visible in the broader academic experience.

Faculty presents another important consideration. A university can provide access to new technology, but its impact ultimately depends on whether educators are comfortable using it in the classroom. Faculty who understand how AI can support teaching, research and problem-solving can make technology part of learning rather than something students encounter separately.

This also changes how university leadership can think about investment. Measurement can reveal whether spending on technology, curriculum development and faculty readiness is working together or happening in separate pockets. It can help leadership identify gaps before they become larger problems.

There is another reason this matters. As AI becomes part of conversations around employability and higher education, students and parents will increasingly want clearer answers about what makes a university AI-ready. A university that can demonstrate its preparedness has a stronger story to tell than the one that simply uses the term. For universities, the objective is therefore moving from making AI-readiness claims to building evidence behind them.


Read more like this: The AI-Ready University Index: What defines readiness in higher education today?

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This is the purpose of ET AI Ready, a certification programme designed to help universities assess, benchmark and demonstrate their AI readiness. The programme evaluates universities across three core dimensions:

Curriculum Integration: How AI is incorporated into academic learning and programmes.

Faculty Adoption: How prepared faculty members are to use and integrate AI into teaching and learning.

Infrastructure Readiness: Whether the university has the technology, tools and resources needed to support AI-enabled education.

Together, these dimensions provide a clearer picture of what AI readiness looks like in practice.

As AI continues to change higher education, universities that can measure where they stand will have a clearer path to where they need to go. ET AI Ready gives universities an opportunity to turn that readiness into something they can demonstrate.

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