Your AI strategy won’t save you if your data is a mess. Gartner says 80% of digital organizations risk failure without modern data governance.
I keep having the same conversation with CTOs: “We want to implement AI.” My first question: “What’s your data quality score right now?”
Usually… silence.
Here’s the thing - you don’t need perfect data to start. You need reliable data. Data you understand. Data someone owns.
Three questions before any AI project:
Can you trust the numbers in your current reports? If analysts spend hours reconciling dashboards, your AI will learn from the wrong answers.
Do you know where your data lives? Not all of it - just the data this AI project needs. One source of truth for that scope.
Who fixes it when something breaks? Not “the data team.” A name. Someone accountable.
I used to think you needed enterprise-grade governance before touching AI. I was wrong. You need just enough governance for the use case you’re building.
The trap is waiting for perfect. The other trap is ignoring the foundation entirely.
Start small. Pick one AI use case. Make sure the data underneath it is solid. Then expand.
AI amplifies what you have. Make sure what you have is worth amplifying.