22 June 2026
Why I stopped doing POCs — and what I do instead
AI POCs often create the illusion of progress. What turns an idea into a useful product is a smaller, real-world experiment designed for production from day one.
The POC trap that makes everyone feel safe
For a long time, I accepted POCs as the natural first step for AI projects. A sponsor wants to see whether the technology works. A team wants to learn. Leadership wants to reduce risk before investing. On paper, that sounds reasonable. In practice, I have seen too many POCs become strange artifacts: impressive enough for a meeting, but too disconnected from the real system to become a product.
That is the trap. A POC optimizes for demonstration, not usage. You choose a clean dataset, a happy-path workflow, a temporary integration, a minimal interface, and sometimes even a model that would be too slow or too expensive for production. Everyone knows it is not industrialized yet, but nobody knows exactly what is missing. The project appears to have moved forward, while the hard questions have mostly been delayed: who uses it, inside which workflow, with what data, under whose responsibility, at what cost, and with what level of acceptable error?
Why POCs die before becoming products
Most POCs do not die because the model is bad. They die because the proof they create is not the proof the organization needs to go to production. A demo proves that something can work in a controlled setting. It does not prove that the team can operate it, that users will trust it, that exceptions are handled, that data arrives in the right shape, that latency is acceptable, or that inference costs hold at scale.
There is also a political problem. The POC gives everyone permission to postpone difficult decisions. You can avoid naming the business owner, avoid deciding where human validation belongs, avoid connecting internal systems, avoid writing serious evals, avoid talking about security, because it is only a test. Then, when the POC is supposed to become a product, all those debts arrive at once. The production step becomes a second project: longer, more expensive, and less exciting than the original demo.
What a production-readiness mindset actually looks like
Today, before I say an AI project is ready to move forward, I look for very concrete signals. Can someone name the exact workflow we are improving? Can we describe the real user, their moment of use, and what happens when the AI is wrong? Do we have a baseline, even an imperfect one, on time saved, errors, volume, or quality? Have we defined what needs to be logged, reviewed, corrected, and measured? These questions are less seductive than a demo, but they are the difference between a toy and a system.
The best signal, for me, is the presence of a real constraint from the beginning. Real data, a real user account, a real validation process, a real cost target, a real operational owner. Not the entire final system, of course. But at least one piece of the real world. If the experiment never touches the roughness of the field, it mostly teaches the team how to succeed in a world that does not exist.
What I do instead: production-first, but smaller
I do not replace the POC with a bigger, heavier project. I replace it with a smaller experiment that is oriented toward production. The scope is deliberately narrow: one workflow, one user group, one data source, one measurable outcome. But the critical components are real from the start: authentication if it matters, logs if the team will need to investigate, human review if mistakes are sensitive, evals if quality must hold, and real deployment if adoption depends on daily access.
This changes the conversation. We stop asking only: can the model answer? We start asking: can the system help on Monday morning without creating more work than it removes? That is more modest, but much more honest. A small version in production, used by five people on a real case, teaches more than a polished demo in front of fifty people. It reveals friction, implicit rules, edge cases, and the moments when the user needs to take control again.
Two concrete examples that change the trajectory
First example: an internal assistant that summarizes and classifies business documents. The POC reflex would be to take twenty clean files, show a pleasant answer, and conclude that the model understands the domain. The production-first reflex is different: connect a limited but real document source, add a review screen, keep cited sources visible, track human corrections, and measure the rework rate. The result is less spectacular in a meeting, but it immediately answers the questions that will decide the future of the project: trust, control, traceability, and operational load.
Second example: an agent that helps a support team prepare replies. A demo can generate a perfect answer for three simple tickets. A useful experiment must include real categories, incomplete cases, tone rules, escalation paths, response-time expectations, and, above all, the right to say I don't know. When you build that from the first iteration, the project becomes less magical and much more solid. If your AI POC is stuck in circles, the right next step may not be a better demo. It may be a smaller, more real version that is robust enough to use. That is exactly the kind of framing I like turning into a concrete execution plan.