The prototype won over the executive committee. Six months later, it is still running on a developer’s machine: nobody uses it, and nobody dares touch it. That does not mean the idea was bad. It means the real work has not started yet.
During the demo, the assistant answered questions about supplier contracts in seconds, cited its sources and phrased its answers elegantly. The CEO asked when the procurement team could use it. The CTO said, “Soon.” That was in March.
By September, the prototype had not left its test environment. It runs on a manually extracted copy of the contracts, ignores access permissions and was built by a developer who has since left the company. Nobody knows what it would cost with two hundred users, or whether its answers are accurate beyond the ten examples shown in the demo.
Your POC probably does not need to be thrown away: it proved that a use case was feasible. But it did not prove that it could become a service. Taking an AI POC over means closing that gap.
Why do so many AI POCs never make it to production?
Gartner forecast that at least 30% of generative AI projects would be abandoned after proof of concept by the end of 2025. The causes it identified — poor data quality, inadequate risk controls, escalating costs and unclear business value — largely concern what happens after the demo.
The underlying reason is structural. A POC answers a simple question: “Is it feasible?” It is therefore built quickly, using selected data and without all the constraints of a real system. That is not a flaw; it is its purpose.
The problem arises when this answer is confused with a much more demanding one: “Can our teams use it every day, with real data, safely and at a controlled cost?”
A POC proves that an idea is possible. It never proves that it is operationally viable. Bridging the gap takes engineering: integrating with tools, managing permissions, measuring quality, monitoring the service, controlling costs and documenting how it works. Companies that underestimate this gap get stuck in “POC purgatory”: the prototype looks too promising to abandon, yet is too fragile to deploy.
What doesn’t an AI POC tell you?
A prototype for an assistant, agent or document-extraction tool often performs well on a few selected examples. In production, it also encounters incomplete or poorly scanned documents, unexpected questions, users with different permissions, higher volumes and costs that add up over time.
It may not reveal how the solution behaves when it does not know the answer. Yet an assistant that confidently invents a contract clause is more dangerous than one that says, “I can’t find that information in the available documents.” This behaviour must be designed, tested and measured.
A POC also says nothing about its own longevity. Model providers release new versions and retire old ones. Documents change; a prompt update may improve one case and degrade ten others. Without a way to detect these shifts, quality can silently drift.
What are the signs that it’s time to take over your POC?
Four situations come up frequently. First, the prototype gets positive feedback, but nobody uses it. It sits outside business tools, without authentication or up-to-date data, and forces users to leave their working environment. It needs to be integrated into the teams’ actual workflows.
Second, answers are unpredictable. Without an evaluation set, there is no way to tell whether a change improved or degraded the solution. Third, costs or response times are drifting: uncontrolled model calls, slow responses or expensive document processing. The architecture needs to be reviewed before scaling up. Fourth, the team that built the POC is no longer available, the code is undocumented and nobody can explain the technical decisions.
For executives — Ask your team three questions: how many users actually use the prototype each week? How do we know its answers are correct? What would it cost per month if it were opened up to the whole company? If none of the answers is backed by figures, your POC needs a review and takeover.
Should you take over, rebuild or stop the POC?
Not every POC deserves to be industrialized. The initial assessment helps choose between three options.
The first is a takeover: the code and architecture are sound enough to keep, restructure and complete. This is often the case when a capable team simply ran out of time.
The second is a partial rebuild. Some components, such as document-search logic or prompts, are valuable, but the technical foundation will not handle the load or cannot be secured. We retain the lessons and what works, then rebuild the rest on a solid foundation. This is common with prototypes built quickly using no-code tools or AI-assisted code, as we explain in our article on taking a vibe-coded project from prototype to production.
The third option is to stop — or, more precisely, change the approach. Conventional search or deterministic automation may meet the need better at a lower cost. Recommending against industrializing a POC is also part of the assessment.
How does an AI POC takeover work?
The process has four stages. Each produces deliverables for you to approve before moving on to the next.
Assessing the POC
We review the code, data, prompts, models, results and observed limitations. Where possible, we speak with the people who built the prototype and with its intended users. You receive a severity-ranked list of risks, a clear recommendation — takeover, partial rebuild or change of approach — and a prioritized, costed plan.
The evaluation set and success criteria
Together with your business teams, we define reference examples and expected thresholds. This evaluation set becomes the project’s source of truth: every change is measured against it rather than judged by impressions. It is retained and enriched throughout the service’s life.
Industrialization
Architecture, testing, security, tool integration, monitoring and documentation: this is where the prototype changes in nature. Changes are delivered and demonstrated in stages, so your teams can follow progress in concrete terms.
Production launch and follow-up
The service is rolled out gradually: first to a pilot group, then more widely once the indicators have stabilized. Monitoring covers answer quality, errors, processing times and usage. User feedback informs the next improvements.
How can you measure AI quality beyond “it seems to work”?
This is one of the clearest differences between a POC and a production service. In a prototype, quality is judged by eye after a few tests. In production, it must be measurable, repeatable and comparable over time.
With your business experts, we build a representative set of examples: common questions, difficult cases, ambiguous documents and requests the solution should refuse because the information is unavailable or the user is not authorized to access it. For each example, we define what a good answer looks like. The evaluations are then rerun automatically whenever the model, prompt or data changes.
Without an evaluation set, every improvement is a gamble. With one, it is a decision. You can, for instance, see correct answers improve while spotting regressions in refusal cases. The discussion between technical and business teams becomes fact-based, and user trust rests on something tangible.
How can you control the cost of AI in production?
The costs of an AI service are variable and proportional to usage. Every question or processed document may generate calls billed by the model provider. A prototype used by three people therefore gives no reliable indication of the bill for a service used by three hundred.
Several levers can make costs more predictable. Choose the right model for each stage: a lightweight model to classify or route a request, and a more powerful one only where quality requires it. Limit the context sent to models by providing only useful passages rather than entire documents. Cache results where possible. Finally, track consumption closely and set alerts for unexpected increases. These optimizations can significantly reduce unit costs without degrading measured quality.
For CTOs — Ask for a cost per request and per active user, broken down by processing stage, along with projections for three usage volumes. This is a sound basis for choosing between models and presenting the project to finance.
What security blind spots do prototypes have?
Security is rarely the priority of a POC, whose purpose is to prove feasibility, not to launch a service. But it must be addressed before any deployment. The blind spots are usually the same.
Authentication may be missing or rely on a shared password. Access permissions are sometimes ignored: the prototype queries data using a technical account that can see everything, which may allow a user to retrieve information they cannot access in the original tool. Secrets such as API keys may be hard-coded.
Protection against malicious instructions is often missing too. A document or email may contain text designed to hijack the model — a critical risk as soon as the solution can act on systems. A takeover addresses these issues systematically and adds logs of model calls, which are also useful for audits and compliance. For more on these issues, read our article on AI risks for businesses and the precautions to take.
Can you take over a POC built by another provider?
Yes, and it is a common situation. The POCs we take over were built by in-house teams, other providers, interns, no-code tools or AI-generated code. Their origin matters little. What matters is access to the code, the data used, the prompts and, if possible, the results of previous tests.
We keep what works, restructure what will not handle the load and add what is missing. The assessment identifies what can be retained, what must be rebuilt and the work required to reach production. If the takeover raises broader architecture questions, a technical audit or CTO as a Service support can complement the work.
What do you get at the end of a takeover?
A successful takeover is more than a service that works. It leaves you with a system that can be understood and handed over: the assessment and takeover plan, the evaluation set and measured results, the code, configuration and operational documentation. These assets belong to you and allow any capable team — including your own — to take over the service.
If the scope is clear and stable, the work can be delivered as a fixed-price project, with a budget and deliverables defined in advance. If the service needs continuous development, new data sources or new use cases, a dedicated team of French-speaking engineers in Madagascar and Mauritius, managed from France, can handle its development and maintenance over time.
The applications we operate ourselves, such as our assistant connected to our ERP and our medical-record summarization agent, follow this same approach. If your project is not yet at the prototype stage, our page on enterprise AI solutions explains how to frame an initial use case.
The next competitive advantage won’t come from another demo
Every company now has access to the same models. Building an impressive prototype has never been easier — and that is precisely why it no longer sets anyone apart. The gap is widening between organizations that accumulate demos and those that know how to turn a prototype into a measured, secure service that people use every day.
Your POC may already have completed the most visible part of the journey. The question is whether you are ready to do the part that really matters, or whether you would rather give it a place of honour among the projects that might have changed something.
Explore our AI POC takeover service or get in touch with our team to assess what should happen next with your prototype.
AI POC to production: frequently asked questions
What is an AI POC, exactly?
An AI POC (proof of concept) is a prototype that demonstrates the feasibility of an AI use case on a few examples. It validates an idea, but usually does not include the integration, security and operations needed for real-world use.
How long does it take to move an AI POC into production?
It depends on the state of the prototype, data access and required integrations. An initial assessment helps define a phased recovery plan and estimate the work involved.
Can code written by an intern or a no-code tool be kept?
Yes, if the assessment finds that some components are sound and useful. Code, prompts or search logic may be retained, while a fragile technical foundation is restructured or rebuilt.
Who defines the success criteria for an AI solution?
Business and technical teams define them together, using representative examples. They specify what counts as a correct answer, which cases are difficult and when the solution should abstain.
Should every working prototype be industrialized?
No. The assessment may find that conventional search or deterministic automation meets the need better, at lower cost and risk.
