Most AI Projects Fail. Here's the Part Nobody Talks About.

Most enterprise AI projects fail.

That's not an opinion. That's what the data says. Consistently. Across industries.

But here's what's interesting - when you actually dig into why they fail, it almost never comes down to the AI itself. The technology works. The models are capable. The tools are there.

What's missing is everything around the AI.

No clear problem definition. No governance structure. No understanding of what success actually looks like. And an almost universal tendency to skip the hard thinking up front and just ... start deploying.

I've seen companies with million-dollar AI budgets fail for exactly the same reasons a 10-person startup fails. The scale is different. The root cause isn't.

The good news? Every one of these failure patterns is avoidable. That's literally what I wrote a whole book about.

If you want the full breakdown, check out https://www.amazon.com/AI-Enterprise-Lessons-Doing-Right/dp/B0H1WRNYRT

Have you seen an AI project fail up close? I'd love to hear what happened - drop it in the comments. No judgment. Just real talk.

And if this resonates, repost it. Someone in your network needs to hear this before they make an expensive mistake.

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