How do we connect AI to data the business actually trusts?
Data platform & AI enablement
Data platform enablement connects AI to the numbers the business already relies on: warehouse integrations, semantic-layer enablement, data contracts, reconciliation workflows, and AI-accessible reporting. The goal is that an answer produced by an AI system lands on the same definitions and figures as the board pack — because an assistant that quietly disagrees with the finance team is worse than no assistant at all.
The trust problem
The most common failure in enterprise AI is not a wrong answer. It is a plausible answer that disagrees with the official number by four percent, for a reason nobody can trace.
Once that happens twice, the system is dead — people stop using it, because checking its output costs more than doing the work manually. Trust is the actual product, and it is built at the data layer rather than the model layer.
What the work involves
- Warehouse integration
- Making Snowflake, BigQuery or the equivalent reachable by AI systems under the same access controls that govern everyone else.
- Semantic-layer enablement
- Exposing the business's own definitions — what counts as an active customer, a booked deal, a period — so a model inherits them rather than inventing its own.
- Data contracts
- Explicit agreements about shape, meaning and freshness, so an upstream schema change surfaces as a failed contract rather than a silently wrong answer.
- Reconciliation workflows
- Checks that an AI-produced figure agrees with the system of record, run automatically rather than trusted.
- AI-accessible reporting
- Reporting surfaces designed to be queried by a system, not only rendered for a human.
Why semantics matter more than retrieval
Most retrieval problems in enterprise AI are really definition problems. The model finds the right table and still produces the wrong number, because the business has three definitions of revenue and the model picked one.
Fixing that at the semantic layer fixes it for every use case at once. Fixing it in prompts fixes it in one place until someone writes a new prompt.
What you get
- Warehouse and semantic-layer integration
- Data contracts covering shape, meaning and freshness
- Reconciliation checks against systems of record
- AI-accessible reporting surfaces
- Documentation of definitions the AI layer inherits
Start here if
- AI answers disagree with the official numbers
- The business has multiple definitions of the same metric
- An upstream schema change silently broke a downstream answer
- Analysts are re-checking everything the AI produces
Snowflake · BigQuery · Semantic layers · Data contracts
Do we need a semantic layer before we can use AI on our data?
Not before, but you will end up building one. Without shared definitions, each use case encodes its own interpretation in prompts and queries, and they drift apart. Formalising the definitions once is cheaper than reconciling five accidental versions of them later.
Is this the same as building a RAG system?
No. Retrieval decides which documents or rows the model sees. This decides what the numbers in them mean and whether they agree with the system of record. A retrieval system on top of undefined data retrieves the wrong number faster.
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Last reviewed · Tayyeb Mahmud, 1AYM