Why 60% of AI Projects Fail (And How to Be in the 40%)

60% of AI projects never make it to production. After years of shipping AI systems, I've seen the patterns that kill projects—and the practices that save them.

The Three Project Killers

1. Starting with Technology, Not Problems

"We need an AI strategy" is the wrong starting point. "Our support team spends 40% of their time on repetitive questions" is the right one.

Every successful AI project I've seen started with a clear business problem and worked backward to the technology.

2. Proof of Concept Purgatory

The POC worked in the demo. Six months later, it's still not in production. Why?

• No plan for integration
• No ownership after the pilot
• Success metrics that don't match production needs

Fix: Define production requirements from day one. Build the POC on production-ready infrastructure.

3. Perfectionism Over Progress

Teams wait for 99% accuracy before shipping. Meanwhile, competitors launch with 85% and iterate in production.

The best AI systems improve with real usage data. You can't get that data without shipping.

What the 40% Do Differently

Start with high-impact, low-risk use cases. Internal tools before customer-facing. Augmentation before automation.

Ship in weeks, not months. Two weeks to prototype. Four weeks to MVP. Iterate from there.

Measure business outcomes, not model metrics. Accuracy is a proxy. Revenue, cost savings, and time saved are the real KPIs.

Build for iteration. The first version is never the last. Design systems that can be updated, retrained, and improved.

The Uncomfortable Truth

Most AI project failures aren't technical. They're organizational. Unclear ownership, misaligned incentives, and fear of imperfect launches kill more projects than model performance.

The companies that succeed treat AI like any other product: ship early, measure results, iterate fast.

What We Do Differently

At RooxAI, we don't just advise—we build. We ship prototypes in 2 weeks and production systems in 4. We stay until it's working.

If your AI initiatives have stalled, let's talk. Sometimes an outside perspective is all you need.