Summary

Lately, it feels like everyone’s talking about AI. Whether it’s automating customer service, predicting sales trends, or just making things “smarter,” AI has become this big, exciting promise for businesses—especially in the SaaS world.

But here’s something I’ve come across again and again that no one likes to talk about: a huge number of AI projects don’t work out. In fact, some studies suggest that up to 90% of AI SaaS projects fail.

That’s a pretty sobering number, right?

And the truth is, it’s not because the tech doesn’t work. It’s usually because of how these projects are planned and executed. So, I wanted to share a few common things I’ve seen go wrong—and what can be done to get it right.

First, Let’s Talk About the Failure Rates

There’s been a lot of research recently showing that AI projects fail much more often than regular software projects. It’s not necessarily because AI is harder—it’s just that it’s different. It has its own challenges, and many companies underestimate them.

And with more companies trying to build AI into their products or services, the number of projects that quietly fizzle out or get shelved after the pilot stage is going up.

So Why Do So Many AI Projects Fall Apart?

From what I’ve seen, it usually comes down to a few key issues:

1. No Clear Business Goals

This one’s big. A lot of AI projects start out because someone said, “We should be doing something with AI.” But if there’s no clear idea of what the project is actually supposed to achieve for the business, it usually ends up being a cool demo that doesn’t go anywhere.

2. Data Problems

AI runs on data. If your data is messy, incomplete, or hard to access, the AI just won’t work well. I’ve seen teams spend more time fixing their data than building their models—because without clean data, even the best AI can’t help.

3. Too Much Hype, Not Enough Reality

AI is powerful, but it’s not magic. I’ve seen projects crash because the team expected the model to solve problems it just wasn’t designed for. It’s important to stay grounded and realistic about what AI can (and can’t) do.

4. Skills Gaps

Another big one. AI isn’t just plug-and-play—you need people who understand the tech, but also how it connects to the business. And finding the right mix of skills—data science, software, domain knowledge—is tough.

5. Weak Project Management

AI projects don’t follow the same path as traditional IT projects. There’s a lot of testing, learning, and adjusting along the way. If a team tries to manage it with a rigid, waterfall-style approach, things can fall apart quickly.

6. Deployment is Harder Than It Sounds

Getting a working model is one thing. Getting it to play nicely with your existing systems, work at scale, and stay up-to-date? That’s a whole other challenge. A lot of AI projects stall out here.

What We’ve Learned (The Hard Way)

I’ve seen some projects that looked great on paper but failed because the team didn’t involve end users, or because the output wasn’t interpretable enough to use in real life.

One team I worked with built a really accurate churn prediction model. But no one used it—because they hadn’t talked to the marketing team, and the insights weren’t tied to any actual action they could take. Lesson learned: AI has to fit into real workflows.

So, What Actually Works?

Here’s what I’ve seen make a real difference:

1. Set Clear, Measurable Goals

Know what you’re trying to achieve from the start. Is it reducing support ticket response time? Improving sales forecasts? Make it measurable, and tie it to real business outcomes.

2. Clean Your Data (Early and Often)

Invest time upfront in organizing, cleaning, and understanding your data. It’s not the most exciting part—but it pays off big time.

3. Build a Well-Rounded Team

AI projects need more than just data scientists. You need business folks, engineers, product managers—people who can connect the dots between the tech and the business.

4. Take an Iterative Approach

Start small, test often, and be ready to pivot. AI is unpredictable by nature, so flexibility is key. Frameworks like CRISP-DM or Agile AI really help here.

5. Think About Deployment From Day One

AI projects don’t follow the same path as traditional IT projects. There’s a lot of testing, learning, and adjusting along the way. If a team tries to manage it with a rigid, waterfall-style approach, things can fall apart quickly.

6. Deployment is Harder Than It Sounds

Ask early: how will this model fit into our existing systems? Who’s going to use it? What happens when it needs updates? Planning for integration and scaling early saves a lot of pain later.

Final Thoughts

AI can be a game-changer—but only if it’s done thoughtfully. The tech is powerful, but it’s not the whole picture. The real challenge is making sure AI fits your business, your people, and your systems.

If you’re thinking about starting an AI project, my advice is this: start with the problem, not the technology. Be clear about what you’re trying to solve. Bring in the right people. And stay flexible, because things will change along the way.

And most importantly—learn from the ones that didn’t go as planned. Those lessons are gold.