The throughline of our conversation was his conviction that AI is not eliminating the need for semantics and governance. It is making them far more important. In his view, organizations will not get trusted AI outcomes until they fix the data layer beneath them: the business definitions, lineage, access controls, and open standards that allow intelligence to scale without collapsing under cost and complexity.

Catherine Brown: Why does AI put semantics and governance pressure on analytics in a way that legacy BI never had to deal with?

Nick Eayrs: Legacy BI was really a world of static dashboards and predefined reports. Business users had to navigate fairly complex interfaces, and if they had a follow-up question or wanted to explore something more deeply, they usually needed specialist support. There was very little true self-service.

The semantic layer underneath traditional BI was also relatively static and slow to change. If the business needed a new definition for revenue, churn, or customer lifetime value, that usually meant going back to IT or specialist teams to update the semantic layer and rebuild reports. It was a very predetermined model.