Hot take: I'm a firm believer that 90%+ of AI projects fail. Not because the tech isn't ready. Because the approach isn't.
Here are the three failure patterns I keep seeing across deployments.
1. Wrong use case
AI gets thrown at shiny problems — the ones that look good on a slide — instead of real bottlenecks. The team that ships AI on the "demo" use case while the actual operational pain sits untouched is the team that wonders why ROI never materializes.
2. Wrong team
No subject-matter experts. Vibe-coded visions. No one on the project who actually knows how the work gets done day-to-day. AI deployment without process expertise is theatre.
3. Wrong method
AI needs a different kind of agile. Fast iteration. Human-in-the-loop testing. Prompt > backlog. If your AI project is being run like a 2019 SaaS implementation, you're already two cycles behind where it needs to be.
What the winners actually do
The companies getting it right aren't shifting their tools. They're shifting their approach — diagnosis before deployment, expert humans in the loop, faster cycles with smaller bets.
The tools matter least.