Everyone wants to set sail on the AI journey right now. It is the gold rush of our decade. But before you push off from the dock, take a hard look at your vessel. If there is one thing that will sink your ship before you even leave the harbor, it is the state of your data.
The $12.9 Million Problem
Let’s cut through the noise and look at the bottom line. Gartner estimates that poor data quality costs organizations an average of $12.9 million annually. That isn’t just a technical glitch; that is a massive bleed of resources and lost opportunities.
We often see executive teams mandating AI adoption because they don’t want to be left behind. The directive comes down: “We need AI.” But often, there is no blueprint. The result? You might get impressive user numbers or a flashy pilot program, but absolutely no measurable business outcome to back it up.
Stop Building on Quicksand
Ronnie Sheth, a veteran in data strategy, points out a pattern we see constantly: companies jumping into AI before they are ready. Even as recently as 2024, organizations were struggling to deploy models simply because their data infrastructure wasn’t “even close” to ready.
If you feed a high-performance engine dirty fuel, it will seize up. The same applies here. You cannot build a predictive powerhouse on a foundation of messy, siloed, or inaccurate records.
The smartest founders are now flipping the script. Instead of asking, “How quickly can we launch an AI model?”, they are asking, “Is our data actually usable?”
The Practical Roadmap to Value
The fix is unglamorous but highly profitable. It starts with fixing the data. Once that foundation is solid, the ceiling for innovation effectively disappears.
- Step 1: Raw Data to Descriptive Analytics. Understand what happened.
- Step 2: Predictive Analytics. Understand what will happen.
- Step 3: AI Strategy. Automate and optimize based on trusted predictions.
As Sheth notes, “Once they fix their data, they can build as many AI models as they want… and they will get accurate outputs because now they have a strong foundation.”
The Era of “Pilots” is Over
For the last few years, business leaders have been in the playground phase—running experiments, doing pilots, and “innovating.” That time has passed.
This year is about getting practical. It is not about showcasing that you can use AI; it is about proving that your AI drives value. If you want to scale, stop chasing the shiny object and start polishing the data that powers it.
