The pattern
Your AI pilot works. The demo is impressive. The results are promising.
Eighteen months later, it still hasn’t shipped to production.
This isn’t a people problem. It’s a structure problem. The AI pilot was built to demo capability – not to integrate with production systems. There’s no integration layer. Nobody built it.
The AI pilot ran on a laptop with access to sample data. Production requires the AI to access real data – your CRM, your database, your existing API.
The integration layer that connects the AI to real data is usually 80% of the work. Most AI pilots don’t account for this. Most AI project proposals don’t either.
The five blockers we see every time
Blocker 1: The AI doesn’t have access to real data.
AI pilots run on sample datasets. Production requires real data from your CRM, your database, your existing API. The integration layer that connects the AI to real data is usually 80% of the work.
Blocker 2: The AI decisions are a black box.
Internal stakeholders can’t adopt what they don’t understand. If your team can’t see how the AI reached a decision, they won’t trust it. If they don’t trust it, they won’t use it.
Blocker 3: Integration requires heavy enterprise infrastructure.
The AI pilot worked on a laptop. Production requires authentication, authorization, rate limiting, monitoring, logging. None of that exists for the pilot.
Blocker 4: No way to verify ROI.
Your CFO wants to know if the AI is “working correctly.” But there are no metrics because nobody built the measurement layer.
Blocker 5: Compliance delays launch.
Legal says the AI can’t access that data without a review. The review takes 3 months. By the time it’s done, the AI is stale.
How to be in the other 40%.
Build the integration layer first. Don’t start with the AI. Start with the data flow. What data does the AI need? How does it access it? Who authorizes access? What happens when the data changes?
Define “working correctly” before you build. Not after. “Working correctly” means specific, measurable outcomes. If you can’t define it before you build, you won’t be able to verify it after.
Treat compliance as a design constraint, not an afterthought. If the AI needs access to customer data, design the access control into the system, not around it.
Budget for the integration layer. This is 80% of the work. Budget for it.
We built the integration layer for AI.
That’s what ProvenanceOne does. We take your AI pilots – the demos that worked in isolation but never shipped – and build the integration layer that makes them production-ready.
Which of these is your biggest blocker? Let’s find out.