AI Program Management
PoC to Production: Why AI Projects Die in the Pilot
Most of the AI projects I have seen born did not die from a model problem. They died in the space between the demo that thrilled the board and the system that was supposed to run every day in production. It is a step that gets underestimated almost every time, because the proof of concept is the part you show and production is the part you maintain. In this article I explain why that leap is so hard, and what I do, as an AI Program Manager, to design it from the start instead of discovering it once the pilot has already failed.
Why a PoC that impresses is not a system that holds
A proof of concept lives in ideal conditions: clean, hand-picked data, a narrow use case, no latency or cost-per-call constraints, and a person watching the result closely. Production is the opposite: real, messy data, edge cases the PoC never anticipated, reliability requirements, and a volume no one was watching when the model ran on twenty examples.
The demo answers the question "can this work?". Production answers a much harder one: "does this still work in three months, with data no one has cleaned and no one checking it by hand every morning?". Anyone who confuses the two funds a pilot and expects a product.
The three reasons a project dies in the pilot
In my experience the pilot almost always dies for one of three reasons, and none of them is the choice of model.
The first is data. A use case that is brilliant on paper but fed by incomplete data, or data never captured in the right format, does not get past the demo. It is the criterion that collapses most often during planning, and the one that kills the most pilots.
The second is ownership. A pilot that works but has no defined owner for the production phase is not a successful pilot: it is a deferred risk. When no one answers for the quality of the output after release, the project sits in limbo until it stops mattering.
The third is integration. A model living in a notebook is not a system: it becomes production only when it enters the processes and tools people already use. If it requires a second parallel system no one wants to maintain, the pilot stays an island.
What the Enel dubbing and the Toyota/Intesa Sanpaolo podcast teach
I have taken AI solutions to production that cleared this leap. The multilingual video dubbing with AI for Enel, which cut localisation work by 75%, worked in production because the pipeline automated the mechanical part (transcription, translation, syncing) but left people a review checkpoint before publication. The same holds for the AI-generated podcast service for Toyota and Intesa Sanpaolo: it held up over time because we had decided in advance who controlled the script, the consistency of the voice and the final listen.
In both cases the difference between demo and production was not the power of the model, but having decided upfront who owns the output, where the human control sits and what happens when the system gets it wrong.
How I design the move to production from the start
Before green-lighting a pilot I ask three questions, the same for every project: do the required data already exist in the right format, or are we imagining them? Who owns the output in production, not the prompt, the output? And what happens the day the model produces a wrong result? If I do not have an answer to all three, the project is not ready for production: it is still an experiment, and it should be called one in front of the board.
This step is also where the metrics get defined. A project that reaches production without knowing which KPIs to track to tell whether it works will judge itself in hindsight, when it is too late to correct course. The method I use to hold these choices together, from roadmap to delivery, is the one I describe in the guide to AI program management.
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