Core question
Do we have to clean up all our data before we start?
Short answer
No. You have to clean the data that the first project touches. Fix it a bit at a time, workflow by workflow. Waiting for enterprise-wide data perfection is how companies spend two years preparing and never start.
We worked with a client who was overwhelmed by years of inconsistent, duplicate, and incomplete business information. Before we touched AI, we spent nearly two weeks cleaning and organizing the foundation. Their reaction afterward said everything: "I can finally breathe again."
That was not because we deployed a flashy tool. It was because the business finally had clarity. Cleaner data meant faster reporting, fewer mistakes, less frustration, and more confidence in decision-making. The AI work that followed was easier and cheaper because of it.
The practical path is narrow and sequential. Identify your most-used business knowledge. Centralize it. Remove duplicates and outdated files. Standardize naming and formatting. Define who owns and updates the information. Then build on that one clean foundation and repeat.
A simple place to start is with four honest questions. Do different teams use different versions of the same data? Are employees manually fixing spreadsheets every week? Can you quickly find accurate information when you need it? Do you trust your reports? If the answer to any of those is "not really," start there.