Everyone has access to the same AI tools. That was supposed to be the great equalizer, but we’ve had enough time now to see what’s actually happening, and it’s the opposite. The people getting real value from AI are the ones who already understood the work. They prompt better because they know what to ask for, they catch bad output because they know what good looks like, and they use AI to move faster along a direction they’ve already validated.
The data red herring
There’s an obsession right now with data being the bottleneck. Your AI initiative will fail because your data is unclean, unstructured, not “AI-ready.” Usable data matters, but it’s a small part of the puzzle and the fixation on it is missing the bigger problem entirely. A row of numbers in a database could be anything. GDP of the world’s richest countries, credit card numbers, random noise. That data is useless without someone who understands what it represents, why it was collected, and what questions it can actually answer. It’s a domain knowledge problem, and no amount of cleaning fixes a lack of understanding.
And data is only one piece of the story anyway. Most of what AI and computers should be helping with goes well beyond analytics. It’s connecting applications, moving information between systems automatically, handling the repetitive garbage that eats up people’s days, proactively surfacing things that need attention instead of waiting for someone to go dig for them. The real value is in eliminating busywork, not just analyzing spreadsheets.
Unfortunately, most of the software we use every day was never built to be integrated with anything. The APIs are incomplete, poorly documented, or just don’t exist. So even in cases where AI could provide immediate, obvious value (email management comes to mind), actually wiring it up means duct tape and bubble gum. You end up pointing Playwright at a browser and automating clicks like it’s a screen macro from 2003. It works, technically. Until the vendor changes a button label and the whole thing falls over.
The tool didn’t fail them. They didn’t have the context to operate it.
That’s the real garbage in/garbage out. Not dirty data. Lack of context, lack of proper interfaces, and a software ecosystem that wasn’t designed for the way things are headed. When someone without domain expertise sits down with an AI tool, they don’t know what to ask, they can’t evaluate what comes back, and they run with the first thing that sounds reasonable. The tool didn’t fail them. They didn’t have the context to operate it.
When the tools are commoditized
When every company is AI-first (and that’s where this is going), the differentiator won’t be the tools. They’re commodities. It won’t be the data. Everyone figures out the data part eventually. It will be the people who understand their problems deeply enough to point these tools in the right direction. The competitive advantage was never the technology. It’s the thinking that directs it.