Why 95% of AI Projects Fail (And How to Be in the 5% That Don't)

The Problem With Most AI Projects

It's easy to build something that "kind of" works.

A chatbot that answers some questions sometimes. A document processor that's 70% accurate. A system that's impressive in demos but breaks in production.

These are toys. They get funding. They get launched. They fail.

It's still hard to build something that actually works. That handles edge cases. That people trust enough to use every day. That delivers the ROI you need to justify the investment.

We see it through when it gets tough. We can do that because we're solving problems that are actually worth solving.

Case Study: 3 Weeks to 22 Minutes

A commercial furniture company was quoting large projects. Took 3 weeks per project. Designers spent most of their time interpreting floor plans and matching SKUs. They were losing deals because they were too slow.

We built a computer vision system that reads floor plans and generates draft proposals with budget-tiered options. Designers now review the final 20% instead of manually handling the first 80%.

Time-to-quote: 22 minutes.

One senior director: "It took me 22 minutes from when I started until it was done and it used to take that project team a couple of weeks."

That's not a toy. That's a system they use every day that changed how they do business.

Why Most Projects Fail

The projects that fail start with "we need AI" and figure out the problem later.

The projects that succeed start with a problem worth solving and figure out if AI helps.

The difference isn't the AI. It's whether anyone knew what they were building and why.

The 5 Questions That Predict Success

Before starting an AI project, answer these:

1. What problem are we solving? Be specific. "Quoting takes 3 weeks and we're losing deals" is a problem. "Improve efficiency" isn't.

2. How much does this cost us? If you can't quantify it, you can't measure success.

3. What does success look like in numbers? "Reduce quote time from 3 weeks to 1 day" is measurable. "Better customer experience" is not.

4. Who will use this? If nobody owns it, nobody will use it.

5. What happens if we do nothing? If the answer is "nothing bad," you don't need AI.

If you can't answer these questions, you're in the 95%.

Figure out the problem first. The AI part is straightforward once you know what you're building.

Next Steps

If you're thinking about an AI project, start with the 5 questions.

If you can't answer them, that's fine. Figure out the problem first.

If you can answer them, you're ready. Most companies aren't.

Want to discuss a specific problem? Reply to this email or schedule a free strategy session here. We'll tell you honestly if AI can help or if you need something else.

We'd rather turn down projects that won't succeed than add to the 95%.