The plan is agentic. The reality is a spreadsheet.
Boards have approved the AI ambition: agents that answer questions, decisions taken on evidence, work that runs itself. Then the first pilot asks a simple question — what is our revenue per customer this quarter — and three systems answer differently. The pilot stalls there.
Lack of AI-Ready Data Puts AI Projects at RiskThe largest single cause, and the one nobody owns. Ask who gets called when a critical AI decision turns out to be based on wrong data. If the answer does not come in five seconds, quality has no owner.
Sales, finance and operations each maintain their own version of revenue, customer and margin. An agent picks one at random and reports it with confidence.
There is no governed place for the data to live, so every use case rebuilds its own ingestion, its own copies and its own access rules.
Data was prepared by hand for the demo. Nothing about that dataset was refreshed, monitored or governed, so the second question exposes what the first one hid.
Clean. Contextual. Controlled.
AI-ready data is discoverable, trusted, governed, reusable and ready to run in production. In practice that reduces to three characteristics, each answering something an agent cannot supply for itself.
Quality-managed, consistent data with one definition per KPI. The test is whether CRM, ERP, support, contracts and telemetry resolve to the same customer, not whether the name is spelled correctly.
Because an agent does not question a number; it inherits every flaw and scales it.
OneLake as one tenant-wide lake in open Delta Parquet, Data Factory pipelines and mirroring, quality rules on the medallion layers.
Meaning, relationships and time awareness encoded in a semantic layer: definitions, business rules and organisational intent, written once by the domain owner and inherited by every consumer.
Because AI cannot invent what a term means in your organisation. Without a definition, the model guesses.
Semantic models over the same files, ontologies and Fabric IQ, plus a searchable catalogue of data products.
Governed access, lineage and reproducibility: metrics and queries resolve to the same answer every time, and you can prove where a number came from, how it was transformed and who accessed it.
Because when an AI-driven decision turns out to be wrong, you need to answer: which data, from whom, certified when?
OneLake security, sensitivity labels, end-to-end lineage, monitoring and audit at tenant level.
One platform. One copy of data. One governance layer.
Meaning and control become properties of the platform rather than of whichever team built the pipeline, so AI-ready data stays maintainable instead of becoming a one-off cleanup.
Structured, unstructured, real-time and graph data consolidated into one governed foundation.
Technical data translated into business metrics and dimensions, for consistent reporting and analytics.
Domain concepts, relationships and context that represent how the business actually operates.
BI, data agents, SQL and APIs on the same curated products — one foundation, many consumers.
Half a day gives you a shared read on quality, availability and governance readiness — and the two or three use cases worth building the foundation around first.