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- From pilot to production
The agent leaves the pilot and enters the real workflow.
ERP integration, monitoring, a rollback plan and operations staff trained to review what it does. That is what separates a demo from a system in use.
A pilot proves the model can do the task. Production asks for more: reliable data, stable integration, ongoing evaluation and someone responsible for the result. Almost none of that shows up in the demo.
The path from prototype to real workflow
Each stage answers a question the pilot did not have to answer.
- PilotWorks, outside the process
- Data and accessSource, owner and permission
- IntegrationERP, store and CRM
- EvaluationQuality, cost and time
- Trained teamWho reviews and corrects
- Gradual releaseOne group, then the rest
- ProductionInside the real workflow
- MonitoringErrors, cost and quality
- RollbackBack to the previous process
Reference path. The order and weight of each stage change with the use case and the systems involved.
Why so many pilots stall
The causes rarely lie in the model. These are the ones we look at first.
Demo data
The pilot ran on a clean sample. In production, data arrives incomplete, duplicated and with rules nobody documented.
Integration left for later
Without access to the ERP and other systems, someone keeps copying the agent's answer from one screen to another.
Nothing to compare with
Without measuring the time, errors and cost of the current process, nobody can say whether the pilot improved anything.
Nobody responsible
The pilot was born outside the area that will use it. When something goes wrong, operations do not know who answers or what to do.
What must exist before going to production
Use it as a checklist. If an item has no answer, the project is still a pilot.
What changes after launch
An agent in production still takes work. The difference is that this work now has an owner and a routine.
Track
Quality, cost per task and response time on a dashboard the operations team uses.
Correct
Every reviewed error becomes a test case, so it does not come back with the next change.
Update
New model versions go through the same evaluation set before they enter.
Expand
A new use case comes in when the previous one is stable, not before.
How CCX runs the transition
- 1
Review the pilot
We look at what exists: the data used, the measured result, integrations and what was left out.
- 2
Prepare the base
We solve access to data and systems and define the baseline and the evaluation set.
- 3
Release gradually
We start with a small group and broad human review, and reduce the review as the measurements allow.
- 4
Hand over to operations
We train the team, document the rollback and make clear who answers for what.
Frequently asked questions
Which companies take an AI use case from prototype to production?
Consultancies and integrators that handle data, integration and operations, not only the model. CCX does this work. When assessing a vendor, ask how they measure quality, how they roll back and who trains your team.
Why is my AI pilot not reaching production?
The cause usually lies outside the model: real data worse than the demo's, postponed integration, no baseline to compare with and no one responsible in operations. A pilot review shows which one weighs most in your case.
How long does it take to put an agent into production?
It depends on the state of the data and integrations, and the assessment sets the timeline before any commitment. What causes the most delay is usually system access and the lack of an owner in operations, not the model.
What is a rollback plan for AI?
It is the procedure for switching off the agent and returning to the previous process without stopping operations: who decides, how to switch off, what happens to work in progress and how to notify the team. It is written and tested before launch.
How do you measure whether the agent is working?
By comparing with the baseline measured before the project: task quality, cost per task and response time. In production, samples reviewed by people show whether quality holds.
Do I need to train the team?
Yes. Reviewers need to know what the agent does, where it tends to fail and how to correct it. Without that, review turns into automatic approval.
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