Every other article and LinkedIn post is about AI projects failing to deliver.
The culprit of this failure typically isn’t the technical aspect. (It’s actually shocking how easy it is to spin up a model these days if you have the time and resources!)
The real issue is not having the tangential elements of your data strategy in order. You can’t just simply insert a new AI initiative into your roadmap without evaluating the strategy that supports it.
This month, we have three questions to pressure test your data strategy and its readiness to support AI.
📌 3 Questions to Pressure-Test Your Data Strategy for AI
It’s smart to get your feet wet with AI. Try out different tools, run proof-of-concepts, and see what’s possible. But before you scale beyond tinkering, take the necessary step to update your data strategy.
1. What specific business problem or decision is your AI project solving?
AI only works when it’s aimed at a real business problem. Think predicting churn to hit retention targets, automating financial reporting to cut cycle time, or spotting where margins are leaking. If your AI project isn’t designed to move the business forward, you’ll see a dead end sooner or later.
The advice: Update your data strategy to (1) define how your AI initiative supports the decisions leadership cares about, and (2) map out the talent, architecture, and process needed to achieve your AI goals.
2. Are AI and BI using the same definitions?
Every company has its own version of this fight: finance defines revenue is one thing, sales says it’s another, and marketing has a third definition altogether. Now layer AI on top of that mess. If your models and dashboards are pulling from different glossaries and data dictionaries, the outputs won’t line up. Model outputs contradict each other, trust erodes, and everyone starts building their own spreadsheets on the side.
The advice: Revisit business definitions in your data strategy so BI and AI pull from the same source and departments are speaking the same language. That alignment builds trust, eliminates conflicting reports, and lets leaders act on data with confidence.
3. If AI recommends an action, what governance guardrails exist to decide whether to act on it or override?
Imagine this: an AI model flags a customer segment as “unprofitable” and suggests pulling back support. Solid advice on paper — until you realize those customers are part of a strategic partnership the CEO has been nurturing for years.
That’s the blind spot. AI is great at spotting patterns, but it doesn’t know the politics, regulations, or long-term bets baked into your strategy. If you don’t spell out when to trust the output — and when to hit pause — you’re asking for trouble.
The advice: Update your governance standards in your data strategy to define who reviews AI recommendations, what thresholds require human approval, who owns model updates, and how exceptions get documented.
Want one more pressure test? Here's a bonus question...
Have you paid attention to your relational data model lately?
Our CEO David Fussichen explains why the relational data model is still the “unsung hero” and absolutely necessary for AI.
