Our leadership approved AI investment but our data infrastructure is a mess. Where do we start?

This is the most common situation we see: leadership is excited about AI, budget is approved, and then someone looks at the data infrastructure and the enthusiasm dies. The data is spread across 12 systems. Half of it is in spreadsheets. Nobody agrees on which numbers are right. It feels impossible.

Here is the reframe: you do not need to fix all of your data to deploy AI. You need to fix the data for one workflow. That is a dramatically smaller problem. If your first target is invoice processing, you need clean invoice data and a clean vendor database. You do not need to fix your HR data, your CRM data, or your marketing data. Not yet.

The sequence that works: run the INSIGHTS assessment to identify which workflow has the best ratio of impact to data readiness. Clean the data for that one workflow. Deploy AI on it. Measure results. Use the ROI from that deployment to fund data cleanup for the next workflow. Each project improves your infrastructure incrementally.

The companies that succeed at AI are not the ones with perfect data. They are the ones who start small, prove value fast, and use that momentum to improve the infrastructure over time. The companies that wait for perfect data are still waiting.