How do we move from isolated AI initiatives to an institutional capability?
The problem: AI as a collection of initiatives
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. Without governance, every faculty improvises and the institution loses coherence and trust.
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 die when they have no institutional owner, no success criteria and no 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.