Why Nearly Every In-House AI Pilot Fizzles—And What’s Missing

The AI Hype vs. Reality Gap

Big promises, big failures: businesses have poured billions into AI pilots, but most don't go anywhere. MIT’s latest report found that a whopping 95% of generative AI pilots—especially those built from scratch—fall flat. Only 5% deliver tangible returns.

In-house builds suffer even more: companies developing their own tools typically see success rates of less than 5%, while those partnering with external vendors fare better, sometimes achieving 20–30% success.

What’s Actually Holding These Projects Back

• No clear goal: Many initiatives launch without asking “What real problem are we solving?”
• Data is broken: AI can’t fix messy, siloed, or outdated enterprise data.
• Integration hurdles: Pulling AI into busy workflows is harder than building a slick prototype.
• No production path: Pilots often stay stuck in labs because there’s no plan to scale or deploy them.
• Talent gap: It’s one thing to build a pilot, it’s another to have the right infrastructure and governance support for full rollout.

What The Future Holds

Playing with AI isn’t the same as profiting from it. Too many organizations see AI as a checkbox item—something flashy to announce, but not something to invest in deeply. For AI to work, it must solve real problems using real data and a clear path forward—not just hype.