Author: Kevin

Congratulations. Your AI pilot worked. The demo went beautifully. The executives asked questions. The AI produced impressive answers. Someone nodded approvingly. Someone else started talking about how quickly this could be rolled out across the organization. Maybe somebody even used the word transformational. Wonderful. Now give it to actual users and see what happens. Because building…

Throughout this series, we’ve talked about becoming ready for AI. We’ve talked about all kinds of things: That wasn’t an accident. Because after all the conversations about models, agents, copilots, platforms, and automation, I keep coming back to the same conclusion: Your biggest AI advantage may have nothing to do with AI. It may simply…

When organizations decide to “do AI,” something interesting often happens. A steering committee is formed. Budgets are approved. Vendors are invited. Technology teams are assigned. And somewhere along the way, AI quietly becomes an IT project. That may be one of the biggest mistakes an organization can make. Because AI doesn’t transform technology. It transforms…

Throughout this series, we’ve talked a whole lot about what prevents organizations from becoming AI-ready. If you’ve recognized some of those challenges in your own environment, you might be wondering: “So… what does an AI-ready data platform actually look like?” It probably looks a lot less futuristic than you expect. In fact, many of the…

Every organization wants trustworthy AI. Reliable answers. Consistent recommendations. Confident decisions. But there’s a question that often gets overlooked: Who owns the information the AI is learning from? That sounds simple. In practice, it’s one of the most difficult questions in modern data environments. Because many organizations have invested heavily in technology while leaving ownership…

For years, metadata was treated like documentation. Important. Useful. Occasionally referenced. Frequently ignored. Most organizations viewed metadata as something that helped humans understand systems after they were built. A data dictionary. A wiki page. A lineage diagram. A catalog entry. Helpful, but rarely essential to day-to-day operations. That assumption is starting to break. Because AI…

Every organization wants to know if it’s ready for AI. The usual questions sound familiar: Those questions matter. But after years of working with data platforms, I’ve become convinced that they’re not the most important questions. Because most organizations don’t fail AI initiatives because they chose the wrong technology. They struggle because they skipped the…

Whenever an AI initiative struggles, the proposed solution is often surprisingly predictable. “We need more data.” The assumption is simple: If some data is good, more data has to be better. Unfortunately, that’s not how AI works. And in many organizations, more data simply means more confusion.                                                                                                               The Definition Problem Consider a simple business question:…

Most organizations assume their data warehouse is one of the safest places AI can learn from. After all, that’s where the “good data” lives. At least that’s the assumption. The reality is often much more complicated. Because many data warehouses contain years of accumulated business logic that nobody fully understands anymore. And AI has no…