University & artificial intelligence

The starting point: from isolated initiatives to institutional capability

During the first years of generative AI, many institutions have worked through licences, workshops, pilots and isolated policies. UNESCO observes very widespread AI use in higher education, with highly uneven levels of confidence and maturity.

The next stage requires connecting institutional purpose, governance and usage criteria, educational model, assessment, curriculum, faculty development, organisational processes, technology, data and impact indicators.

Governance before tools

An AI strategy starts with the criteria: what is delegated to systems, what is preserved as human activity, with which ethical and data safeguards, and how technology adoption is decided. Clear governance gives the whole institution coherence and trust, and lets every faculty innovate within a shared framework.

AI literacy: from obligation to capability

In Europe, Article 4 of the AI Act requires organisations deploying AI systems to promote AI literacy among the people working with them. Supervision begins in August 2026.

This turns institutional AI training into a sustained need. My approach goes beyond one-off workshops and builds verifiable organisational and professional capabilities, contextualised by prior knowledge, systems in use, risks and purposes.

From pilot to scale

Pilots scale when they have an institutional owner, success criteria and a connection to the educational model. I design roadmaps where every pilot is born with hypotheses, metrics, faculty support and an explicit scaling decision.

This line of work is executed through BeTalent, the institutional AI strategy consultancy of the ecosystem.