AI projects are failing—not loudly, but quietly and often

Last week, I shared some learnings from AI initiatives we've run over the past couple of years. These were not theoretical ideas. These were real projects, built for real businesses, by real teams.

Some succeeded. Some taught us what not to do.

Warren Buffett: "The first rule is: don't lose money." In the AI world, the first rule should be: don't let the project fail.

🔁 1. Chasing AI without a real business problem This is the #1 reason AI projects fail. The excitement is real, but the clarity is missing.

Too many initiatives start with, "We have to do something in AI. The Board/CEO wants it."

When you ask "Why?"—the answers get fuzzy. There's often no alignment with a meaningful problem, no defined outcome, and no plan for business value. You must start with a sharp, urgent problem. Ask: - Is it real and recurring? - Is it costing us time, money, or customers? - Is solving it a priority for leadership?

If the answer is lukewarm, drop it. Don't chase hype—solve pain.

📉 2. No data, but big ambitions AI needs fuel—and that fuel is data. Most companies don't even have decent dashboards, but they want AI to "think" for them.

You can't train models on instincts or opinions. AI needs history, decisions, edge cases, and volume.

Before even thinking about AI, get your data stack in order: - Start capturing what matters. - Structure and cleaning it consistently. - Build visibility through dashboards.

🧠3. Ignoring the role of context Even the best algorithms are clueless without context. What works in one scenario may totally fail in another. AI can't figure that out on its own.

Think of it like this: if I'm asked to speak at an event, I'll want to know the audience, their challenges, the format—otherwise, I'll miss the mark.

AI is the same. Without business logic, edge conditions, and constraints, its outputs are generic at best, misleading at worst.

âš¡ 4. Forgetting hidden and ongoing costs Many leaders assume AI is a one-time build. It's not. Even after a model is trained, there's hosting, fine-tuning, monitoring, guardrails, integrations, and more.

And the infra isn't free—especially if you're using Gen AI APIs. Today, a lot of this cost is masked by subsidies from big players. But like every other tech cycle, the discounts won't last.

🧭 So what should companies actually do? - Map where time and money are leaking internally. - Start capturing data in those areas—every day, every interaction. - Use dashboards and analytics before jumping to AI. - Identify where automation or decision support can create value. - Train your systems not just with data, but with your decision logic.

And make sure AI is embedded where work happens—not in some separate tab. If your team needs to "go to ChatGPT", they won't. The AI has to come to them—right inside their workflows.

🚶‍♂️ Crawl → Walk → Run The hype will make you want to run. But strong AI systems are built the boring way.