When AI outputs disappoint, the first instinct is often to try a different model or vendor. Sometimes that is the right call. More often, the underlying issue is that the AI is working from incomplete or inconsistent information about the business.
The same word, different meanings
Ask sales, finance and operations to define an active customer and you may receive three different answers. Each definition makes sense within its function. But an AI capability that spans those functions will either pick one silently or mix them, and neither outcome is trustworthy.
Agreeing shared definitions for core entities such as customers, orders, products and assets is unglamorous work. It is also one of the most valuable things an organisation can do before scaling AI.
Ownership, quality and lineage
Shared context needs owners. Someone must be accountable for each core entity, for the quality checks applied to it, and for deciding who may read or change it.
Lineage matters too. When an AI capability produces a recommendation, the organisation should be able to trace it back to the data it relied on. This is essential for trust, for correcting errors and for meeting regulatory and audit expectations.
Governed, not centralised
Shared context does not mean moving all data into one place. It means a governed, common view that AI capabilities can draw on through controlled interfaces, with clear rules on what can be written back.
Organisations that invest here first tend to find that later AI initiatives are easier to build, easier to govern and easier to connect. The model becomes a replaceable component rather than the foundation.