Why Do Most GenAI Projects Fail - and What Sets the Successful Ones Apart?

A recent article claims that 95% of Gen AI projects fail to meet business expectations. That stat is going viral—and it resonates with what we've seen.

At Be Customer Smart, we've worked on dozens of AI initiatives (not just Gen AI) across industries. Here’s what we’ve learned separates the winners from the rest:

1️. Start with the Right Reason

If your goal is simply to “do something with AI” to look innovative, don’t expect meaningful results. Successful projects begin with a clear business need—cost savings, speed, better customer experience—and explore how AI can solve it.

2️. Aim with the Right Ambition

AI should be applied where it can truly move the needle. One utility company built a complex AI system with real-time triggers and multiple ML models. It looked impressive—but only delivered 11 extra deals in a year. They pivoted to solving real problems instead.

3️. Use Relevant Metrics

You can’t prove impact without measurement. Too often, teams track performance after AI is deployed—but lack baseline metrics to compare against. Without that, ROI remains a mystery.

4️. Rethink Processes, Not Just Tech

AI alone rarely delivers value. It must be embedded into reimagined business processes. One client used AI to predict customer behaviour—but still sent the same message to everyone. The tech was smart, but the process wasn’t.

5️. Think Beyond the Box

Don’t just optimize existing workflows—ask what new opportunities AI unlocks. Gen AI can make previously unprofitable customer segments viable thanks to its cost efficiency.

6️. Embrace Continuous Improvement

AI won’t transform everything overnight. One media client saw an 11% lift in conversion from a recommendation engine pilot. It was a solid start—but they expected more. Big gains come from iterative experimentation, not one-shot miracles.

7️. Build a Focused Team

AI attracts attention—everyone wants in. But early-stage success depends on a small, dedicated team. Involving too many people too soon dilutes focus and slows progress.

8️. Choose the Right Tool for the Right Problem

Sometimes AI is overkill. Many challenges can be solved with existing data and BI tools. Before jumping into AI, ask: Have we truly leveraged what we already have?

9️. Test and Learn

 Just like any major change, rigorously test your Gen AI solution with the target audience before launch. But don’t stop there—once it’s live, actively monitor performance and user feedback.

10. Start from the Customer/User Perspective

This applies to all innovation—not just AI. The best solutions are built around the needs of the end user, not the excitement of the technology.

What’s your experience with Gen AI projects? Have you seen similar patterns—or different ones? Let’s share and learn together.