In Part 2 of our From Data Chaos to AI Readiness series, we explored Phase 2: Institutionalise — the process of turning a successful AI data corridor into a repeatable capability through clear ownership, ongoing data-quality monitoring, practical oversight and measurable outcomes. But what happens once those practices are established? How can an organisation expand its AI initiatives without recreating the same data challenges every time a new use case emerges? The answer is