How must a university transform to keep creating value in the AI era?
From incremental change to systemic transformation
Reports from EDUCAUSE, UNESCO and the OECD converge on the same diagnosis: incremental change is insufficient. AI has made visible weaknesses that already existed in assessment, curriculum, strategy and institutional culture.
The answer is not to add more initiatives — another pilot, another workshop, another licence — but to review the whole architecture: purpose, educational model, curriculum, assessment, faculty, technology, data and engagement with the territory.
The macro–meso–micro architecture
Real transformation demands articulating three layers that usually operate in isolation:
- Macro (direction): purpose, institutional identity, educational model, AI strategy and governance, value proposition, territorial impact.
- Meso (system): graduate profile, curriculum, competency progression, assessment, faculty development, technology, data and external engagement.
- Micro (experience): challenges, projects and cases, activities, roles, feedback, rubrics, metacognition and evidence.
How I work
Every process starts with an institutional diagnosis across the three layers and ends with installed capabilities, not a report. The engagement combines strategic direction, co-design with internal teams, measurable pilots and progressive scaling.
I have lived through faculty resistance, technological limits, curricular tensions and multinational rollouts. That experience makes it possible to anticipate what happens when a university genuinely tries to transform — not just how it should look.
Results you can prove
Transformation is measured: faculty adoption, effective curricular change, quality of learning experiences, competencies developed and longitudinal evidence of the graduate profile. Without measurement there is no transformation; there is communication.