The bottleneck for most AI initiatives is not model quality — it is data quality. Here is how to audit your data infrastructure and fix the foundations before building AI on top.
We have reviewed hundreds of AI project proposals and the number one failure point is the same every time: the data is not ready.
Organisations want AI-powered forecasting, intelligent search, or automated classification — but their data sits in disconnected silos, has no consistent schema, is poorly documented, and has never been audited for quality.
The data readiness checklist before any AI project: Can you access the data programmatically? Is there a schema and data dictionary? What is the null rate for key fields? Is there historical data with ground truth labels? Who owns the data and can approve access?
If you cannot answer all five questions, your AI project will struggle. The good news: fixing these problems usually takes 6-8 weeks and delivers enormous value even before any AI is built.
The modern data stack we recommend for AI readiness: a cloud data warehouse (BigQuery, Snowflake, or Redshift) as the foundation, dbt for transformation and documentation, and a data catalogue for discovery. This stack makes AI projects dramatically faster and more reliable.