As healthcare organisations prepare for broader use of AI, many discover that readiness is less about algorithms and more about whether they can trust the data that feeds them. Regulatory frameworks, clinical oversight and operational reality increasingly converge on the same questions: how was the data prepared, what assumptions were made, what changed over time and who is accountable once AI systems influence care.

This workshop focuses on the often invisible layer where trust is won or lost: data preparation and data hygiene. Participants explore how normalisation, harmonisation, cleansing and cohort definition decisions quietly shape what AI systems can learn and where they may fail. Rather than treating these as technical details, the session frames them as leadership and governance issues that directly affect risk, compliance and scalability.

Building on this foundation, the workshop connects data trust to AI readiness. Through concrete scenarios, participants examine how organisations prepare staff, governance structures and oversight processes for AI in production. The emphasis is on practical awareness, informed decision-making and identifying gaps early, not on technical implementation or legal interpretation.