Interest in AI is high across institutions, and with good reason: there are real opportunities to reduce manual work and improve decisions. But most AI initiatives that disappoint do so for the same reason. The data they depend on is scattered across systems, inconsistently defined and owned by no one in particular. Before choosing models or tools, it is worth looking at the foundation.

Agree what the numbers mean

If two departments calculate revenue, headcount or outstanding balances differently, any dashboard or model built on top will inherit the disagreement. A short list of agreed definitions for key measures, with a named owner for each, is one of the most valuable data investments an institution can make.

Consolidate before you automate

A structured reporting environment, such as a data warehouse fed by the operational systems, brings information together in a consistent form. It allows leadership reporting and business intelligence to draw from the same source, and it gives analytics and AI work a reliable starting point.

Get reporting right first

Clear, consistent executive and operational reporting often delivers more immediate value than advanced analytics. It also exposes data quality issues early, while they are still inexpensive to fix.

Choose practical use cases

  • Document intelligence: extracting and organizing information from invoices, forms, contracts and correspondence.
  • Forecasting where historical data is reliable, such as demand, collections or cash flow.
  • Anomaly detection on transactions or operational data to highlight items for review.
  • Assisted reporting that helps teams find and summarize information faster.

Good candidates have a clear owner, measurable benefit, sufficient historical data and a human review step where decisions matter.

Govern access and use

Data governance clarifies who may access which information, for what purpose and under which controls. This matters even more when AI tools are involved, because sensitive information can otherwise flow into places it should not.

Turn institutional data into information people can act on.

Start small and measure

A focused pilot with defined success criteria shows whether a use case is worth scaling. Measuring time saved, error rates or decision quality keeps the work grounded in operational value rather than technology for its own sake.