I keep reading about AI strategies that stall. A company decides it needs AI, assembles a team, hires consultants, identifies use cases, builds a roadmap — and then somewhere between the roadmap and the first deployment, everything stops. It's become one of the most consistent patterns in tech, and the more I learn about it, the more I think the failures are baked in from the beginning.

The models work. The infrastructure scales. The data science teams are capable. But the foundational conditions for success were never established, and no amount of good engineering can compensate for bad foundations.

The Data Reality

The most common issue, from everything I've read and seen, is data. Not the absence of data — most organizations have plenty — but the quality, accessibility, and governance of that data. AI systems need clean, well-structured, consistently labeled data. What most organizations actually have is data scattered across systems that don't talk to each other, with inconsistent formats and unclear ownership.

This isn't glamorous work. Nobody gets excited about cleaning up a customer database or standardizing how product categories are labeled. But it's the foundation for everything else. I've encountered this even in smaller-scale projects — the data is never as clean as you think it is, and the time spent wrangling it always exceeds the estimate.

What's interesting is that the organizations that seem to do well with AI often invested in data infrastructure years before "AI strategy" was on anyone's agenda. Good data management turns out to be the prerequisite for everything AI promises.

Vague Problem Definitions

"We want to use AI to improve customer experience" is not a problem definition. Neither is "leverage AI for operational efficiency." You can't build systems against aspirations.

A useful problem definition looks more like: "We want to predict which customers are likely to churn in the next 30 days so we can intervene with retention offers." That's specific, measurable, and testable. You can evaluate whether the system works. But getting from the vague aspiration to the specific problem statement is harder than it sounds — it requires deep understanding of both the business domain and the technical possibilities.

As a student, I find this translation layer fascinating. It sits right at the intersection of business knowledge and technical understanding. It's the kind of skill that seems incredibly valuable and incredibly rare. And it's exactly the kind of skill that most curricula — whether business or CS — don't explicitly teach.

The Adoption Gap

Even with good data and a clear problem, AI projects can stall if the people who are supposed to use the system don't actually want it. I've seen versions of this firsthand: a technically successful project that nobody adopts. The model works, the predictions are accurate, but the team keeps doing things the old way.

Usually this happens because the project was championed by someone in leadership who believes in the technology but didn't secure buy-in from the people whose daily work it's supposed to change. It's a reminder that technology adoption is as much a human problem as a technical one.

Starting From Where You Are

The pattern I keep seeing in the projects that actually work is that they start small and honest. Instead of "what's our AI strategy?" they ask "what can we actually do right now, given our data, our people, and our tools?" The answer is usually less exciting but far more likely to produce something real.

One small project that delivers clear value does more for AI adoption than a comprehensive strategy deck that never gets executed. Proving value changes conversations in a way that roadmaps don't. I think about this a lot as I think about the kind of work I want to do — the unglamorous work of making AI actually function in a specific context, not just theorizing about what it could do in an ideal one.