How do we know if our data is actually ready for AI?

Most companies overestimate their data readiness because they look at the data warehouse and see petabytes. Volume is not readiness. The question is whether the right data, in the right format, is available at the right time for the decision that matters. Think of it like a restaurant kitchen: having a full pantry does not matter if the chef cannot find the salt when the order is up.

Four tests tell you if your data is ready for a specific AI use case. First, accessibility: can the AI reach the data it needs in sub-second response time? Second, quality: is the data accurate and consistent, or does your team spend hours cleaning it before analysis? Third, lineage: can you trace every number back to its source? Fourth, ownership: do you know who is responsible for data quality in each system?

Here is the honest truth most vendors will not tell you: you do not need perfect data to deploy AI. You need clean-enough data for your first workflow. Start there. Use the AI project itself to drive data quality improvement. Waiting for perfect data is the most expensive form of procrastination in enterprise AI.

The INSIGHTS assessment scores your data readiness as one of seven dimensions. It tells you exactly which workflows your current data can support and which ones need infrastructure work first.