We created an AI routing system for a logistics company. It achieved 95% accuracy and had sub-second response times. It performed perfectly in demos.
However, after three weeks in production, their operations team stopped using it.
I observed them at work. Every time our AI suggested a route, the team lead would:
- Open their old planning tool
- Verify the AI's suggestion manually
- Compare it to his instincts
- Only then implement it
I asked why they did this. He replied, "Your AI is probably right. But if it's wrong and I miss it, we lose six figures. I can’t just trust it."
It became clear to me: we focused on accuracy when we should have focused on trust.
An AI can be 95% accurate, but if it can’t indicate when it’s unsure about that 5%, it’s not useful in high-stakes environments.
We redesigned everything based on "transparent autonomy" - the AI now:
- Clearly states confidence levels (0-100%)
- Shows the data points that influenced its decision
- Alerts when it needs human verification
- Explains its reasoning in simple terms
This was less impressive in demos. However, the deployment success rate increased from 30% to 85%.
The biggest challenge in enterprise AI isn't making it smart enough; it’s making it honest enough.
Is anyone else facing this gap between deployment and demos? How are you addressing it?
Sherin Joseph Roy
Co-Founder, DeepMost AI
Building AI systems that think with people, not for them
Bangalore | sherin-sef-ai.github.io
