From pilot to product: the gap that breaks AI projects
A language model works beautifully in a demo - and falls apart in real use. I look at where the gap is and how to bridge it.
In 2023 many companies launched pilots with language models. It is easy to do: take an API, write a few prompts, get impressive results on test examples. The presentation goes well. A decision to expand the pilot is made.
And then something goes wrong.
I see this scenario repeating. I think the gap between pilot and product is the most underestimated problem in AI projects right now.
Why a pilot looks better than it is
A pilot is tested on examples that were selected or at least reviewed by the team. This creates the illusion that the model performs well on "real" data. But real data in production is more varied, dirtier, and contains edge cases that nobody thought to test.
The second factor: a pilot typically has no requirements around latency, scalability, logging, monitoring, or error handling. All of that appears when moving to production and requires significant work.
The third factor: a pilot has no operational load. Nobody is waiting for a response in two seconds. Nobody is querying the system a thousand times a day.
What concretely breaks when moving to production
The first thing: quality on real requests. Users phrase questions differently than the development team expected. The wording is different, the context is different, the language is different. The model starts making errors that did not appear during testing.
The second thing: cost. Language model APIs cost money. A pilot on 100 test requests gives no idea of real costs with thousands of requests per day.
The third thing: reliability. What happens when the API is unavailable? What happens when the response arrives with a delay? What happens when the model returns a response in an unexpected format?
The fourth thing: security and control. What happens when a user asks a question outside the intended scenario? Can they get information they should not have access to?
How to close the gap
The first rule: test on real data with real variety as early as possible. Not on curated examples, but on what actually comes in from users.
The second rule: define a quality metric before launching the pilot. Not "does the answer look good", but a concrete, measurable definition of an acceptable result.
The third rule: build monitoring capability into the architecture. Every request to the model and every response should be logged in a form that allows quality analysis and degradation detection.
The fourth rule: plan for human oversight during the transition period. Full automation is not the first step. The first step is helping a person do the work faster. The second step is removing the person from the loop where quality and reliability have been confirmed.
The gap between pilot and product is real and bridgeable. But it needs to be planned for, not discovered mid-project.