The Trust Gap: Why Enterprise AI Fails Beyond the Prototype | Databricks
PushButton AI Team ·
The Trust Gap: Why Enterprise AI Fails Beyond the Prototype | Databricks
You bought the AI tool. It worked in the demo. Now it collects dust at $200/month.
You're not alone — and you're not the problem.
Here's what the research actually shows: AI fails most often not bec
You bought the AI tool. It worked in the demo. Now it collects dust at $200/month.
You're not alone — and you're not the problem.
Here's what the research actually shows: AI fails most often not because the technology is bad, but because it was never built for real-world trust. It works beautifully in a controlled test. Then it hits your actual business — your messy data, your skeptical team, your specific workflow — and quietly falls apart.
The gap isn't between "AI works" and "AI doesn't work." The gap is between a polished demo and a tool your team actually relies on every day.
This is why so many business owners feel burned. The consultant showed you a prototype. Nobody told you the hard part comes after.
One practical move you can make this week: before evaluating any AI tool, ask the vendor for two customer references who have been using it for over six months in day-to-day operations — not a pilot. That single question filters out 80% of the hype.
What's the biggest reason an AI tool failed to stick in your business — the tool itself, team adoption, or something else entirely?
But as organizations push AI into production, a critical gap is emerging: what works in experimentation often fails under enterprise demands for trust ...
