Why Data Maturity Matters More Than AI
Whenever AI comes up in business conversations, there’s a rush to get in the game. Leaders hear about competitors using with AI with massive ROI, or read headlines about companies saving millions, and they feel pressure to do the same.
But here’s something I’ve learned on my own AI journey: if your organization isn’t mature in how it manages and uses data, AI will likely disappoint you.
The Hard Truth: AI Is Only as Good as Your Data
You’ve probably heard the phrase garbage in, garbage out. That’s never been more true than with AI.
If your customer data is inconsistent, if your sales numbers don’t reconcile, if different teams are using different definitions of the same metric—feeding that into an AI system won’t magically fix it. In fact, it will make the problems worse. The AI will confidently produce answers, but those answers will be based on bad inputs.
My “aha” moment in learning AI was exactly this: the quality of your data is the ceiling for the quality of your AI.
What “Data Maturity” Really Means
Data maturity doesn’t mean you have the fanciest tools or the biggest databases. It means you’ve built trust in your data across the organization. A mature data culture usually has:
Consistency – Everyone uses the same definitions for core metrics.
Quality – Data is accurate, complete, and regularly maintained.
Accessibility – People can actually find and use the data they need.
Governance – Clear processes for how data is managed and who is responsible for it.
When those basics are in place, AI can add real value. Without them, AI projects often end up being expensive experiments that fail to return ROI.
Analogy: Building on Solid Ground
Think of AI like a skyscraper. The taller you want to build, the stronger your foundation has to be. If you try to build on sand—or worse, on shaky ground—the structure won’t hold.
Data maturity is that foundation. AI is the skyscraper. Skip the foundation, and you’ll spend more time repairing cracks than enjoying the view.
A Real-World Example
I came across a case where a financial services company wanted to use AI to predict customer churn. On paper, it was a great idea. But when the project got underway, they realized customer data lived in five different systems. Some accounts were duplicated, others had missing contact information, and the definitions of “active customer” varied across teams.
Instead of getting clean predictions, the AI model kept producing confusing and contradictory results. The problem wasn’t the AI—it was the data.
Once the company took the time to centralize and clean its customer data, the AI project became far more effective. But it required fixing the foundation before the skyscraper could go up.
Why This Matters for Business Leaders
Executives and managers often feel pressure to “do something with AI.” But if your team is still struggling to answer basic reporting questions, chasing AI is like trying to run before you can walk.
The smarter move is to invest in data maturity first. Get your house in order. Build processes for reliable, consistent, accessible data. Once you trust your data, then AI can amplify its value.
Final Thought
AI may be exciting, but data maturity is the prerequisite for success. Without it, AI will frustrate more than it helps. With it, AI can become a powerful extension of the solid foundation you’ve already built.
In other words: don’t skip the fundamentals. The best AI strategies begin with great data strategies.
👉 Next up in this series, I’ll dive into how AI compares to the human brain—and why that comparison often creates more confusion than clarity.