Is our data ready for the AI we want to run?

• Which use case defines readinessReadiness is proven per use case, not for the estate

• Copy or virtualizePerformance versus duplication and cost

• Domain ownership or central teamScale versus consistency

• Consolidate or federateOne platform versus respecting existing investments

• Boiling the ocean before the first use case

• A data lake nobody owns
• Leaving unstructured data out of scope

• Data products with no contract anyone is measured against

• Treating AI-ready data as one and done

The starting level. Nothing precedes it.

Nothing in another pillar. It builds on the level before it.

• Data products need clear ownership rules.Governance & Trust at Establishing

• A fast, unified estate needs controls built into the platform.Governance & Trust at Scaling

Only the next level in this pillar.

• A portfolio value model needs trusted baseline data.AI Value Realization at Scaling
• Semantic models sit on a central data platform.Enterprise Context Layer at Establishing
• Assistants embedded in a process still need a platform to draw on.AI in the Process at Establishing

• Shared definitions need owned data products underneath.Enterprise Context Layer at Scaling
• End-to-end agents need reliable data products.AI in the Process at Scaling
• Audit-readiness depends on lineage and ownership in the data.Governance & Trust at Compounding

Top of this pillar. Nothing else depends on it.