Data platform & AI enablement

Once an AI answer has disagreed with the official number twice, people stop using the tool, because checking its output costs more than doing the work by hand. Trust is the product, and it is built at the data layer rather than the model layer.

A system nobody uses returns nothing, which is why our guide to AI ROI treats adoption as an assumption to measure after launch rather than a given.

• 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.

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.

The exception is an archive in two languages, where retrieval fails on its own account. Arabic and English documents need Arabic-aware search and a test set that runs in both directions, and our guide to Arabic-English enterprise search sets out how to build and test both.

If you are not yet sure your data could support an AI system at all, the data and systems questions in our AI readiness assessment are a quick check you can run yourselves first.

The definition problem is the same when the numbers live in an ERP or CRM. The vendor's built-in AI and a layer you build both inherit whatever definitions exist, and our comparison of vendor AI and your own layer on an ERP or CRM, listed at the end of this page, sets out when each fits.

For the data review itself, our guide to data strategy for AI sets out what to check on access, quality, ownership and personal data for one use case, with a checklist you can copy.

Not every question put to that data is a language question. A forecast or a risk score is usually better served by a model trained on the same trusted history, and our guide to machine learning or generative AI sets out how to tell which one a problem needs.

For a bank in Qatar, the data behind an AI system is also evidence for the regulator. Under the Qatar Central Bank's AI Guideline, an entity using an AI system from a provider gives the QCB its own use testing, its data sources and its human oversight plan, and our guide to the QCB AI Guideline sets out every step the guideline puts in front of a launch.