“Our AI prototypes work.
None of them made it to production.”
How do we actually get to production?
The business still runs exactly as it did before.
See if this should go to production
Not every prototype should become a product.
The first job is working out which ones deserve to. A demo that impressed the room is not evidence that the thing behind it is worth running for the next five years. We would rather end a bet that has already failed than bill you through it, and we will say which one you have.
It was never going to be the model that stopped you
A prototype proves the idea can work. Production is the eighty percent nobody demoed, and it fails in three predictable places.
The data was curated
It cannot pass the review
Nobody owns it
A prototype does not keep
It is not sitting still while you decide. It is quietly expiring.
The budget was for a pilot
Production is a fresh ask, harder every quarter with nothing running to point at.
What you built on has moved
Models, SDKs and vendor terms have shifted. A year-old prototype is a migration first.
The context is leaving
Why a threshold sits where it does, which edge cases were deliberate. That lives in people.
Forget the money already spent. The problem the prototype was built to fix is still costing you the same amount it did before.
Three AI systems that are running, not demoing
Marketing technology, post-acute healthcare, medical devices. Client-owned, in production, with numbers their own finance teams recognize.
Price the hard part first, not the demo again
It stalled because nobody put a number on production. That is the first thing we do.
Some prototypes should not go to production, and we will tell you which. Billing through a bet that has already failed costs you more than ending it.
The code, the intellectual property and the roadmap are yours. That is in the contract, not the pitch.
Bob walks through this whole problem for forty minutes
Someone builds a working demo in a weekend, it gets shown to the board, and then it stops. The session goes through what the ones that ship did differently, using five real systems we took to production rather than slideware. Drawn from healthcare work, but the gap is the same anywhere.
“Building got easy. Shipping didn’t.”
From Pilot to Production: Why 95% of Healthcare AI Never Ships
Recorded 4 August · free, no registration
Bob Klein, CEO
Reading for the argument you have to make internally
Start with a conversation, then a small piece of work
Nobody should commit to a production build to find out whether it can be one. Each stage earns the next.
Readiness review
What production will demand, and whether it should go at all.
Blueprint
The architecture and a real number, so it becomes a fundable decision.
Build it for real
A senior team in your operation, building the system the prototype sketched.
Run it
Support and monitoring for as long as you want us. A clean handover when you do not.
Tell us what you built and where it stopped
Thirty minutes. Walk us through the prototype and what is in the way, and we will tell you what we think is hard about it.
Four other things we hear
“We finally have our roadmap defined. Leadership signed off. Engineering is booked.”
How do we deliver more this year than engineering can on its own →
“Every time we grow, we add another FTE.”
How do we do more without hiring more →
“We make the short list. We keep losing the deal. Nobody can tell us why.”
How do we stop losing demos →
“We pay for every system we run. We own none of them. We still can’t make sense across them.”
Where does our IP actually live →
Or start from all five.