Mid-sized companies: moving from AI trials to production

In many mid-sized companies, AI came in through trials: a proof of concept here, an assistant there. The demonstrations convince, then nothing goes into production. The phenomenon is well known, and it is not technical. Here is why projects stall, and how to see them through.

Why trials stall

1. The use case was chosen because it could be demonstrated, not because it was profitable.
2. No indicator was agreed before the trial: nobody can say whether it is a success.
3. The trial data was selected: in real conditions, the exceptions arrive.
4. Nobody is responsible for what comes next: who maintains it, who approves, who pays for running it?
5. The rules are not written down: access, human approval, place of data processing. Each department raises its questions, and the project waits.

The five conditions for going into production

1. Choose the process for its measured value

Time spent, processing time, volume: quantified before choosing. A Domain Audit ranks the processes of a business domain by value and feasibility, on your real data; for a single complex process, the Process Audit quantifies what handing it over would bring.

2. Agree on the indicators before the trial

Two indicators at most, with their measured starting point. That is what turns a demonstration into a decision.

3. Check on real cases, exceptions included

Not on a flattering sample: on 15 to 30 real cases for a process, with the rules for handing over to a person when the agent does not know.

4. Write one governance shared by all agents

Who decides what, which actions are approved by a person, which access rights, which records, where data is processed. Shared by all agents, these rules avoid reopening the debate with every project. See Governance.

5. Organise what happens after delivery

An agent connected to your tools lives with them. You need a lead on the business side, regular monitoring, and a planned running budget. See Why does an AI agent drift without monitoring?.

When several agents arrive

The second and third agents raise a new question: consistency. Without the same rules, the same vocabulary and a shared memory, each agent answers in its own way. That is the subject of Making several AI agents work together and of the Company Brain, which is never a prerequisite.

A realistic order of march

1. One process chosen for its value, measured, in service within a few weeks: a Process Agent, preceded by a Process Audit if it is complex.
2. The whole business domain, when the next processes share its rules: a Domain Audit, required, then Business Agents, with its knowledge written down for the agents.
3. Shared rules and memory, if several business domains are involved.

Each step is decided on the result of the previous one.