Production Deployment
The point at which what a prototype proved is built for daily use, with oversight, logging, escalation and the moment a person takes over designed in. It is step five of the audit, Implement and Scale, and it happens one proven workflow at a time.
What it means in practice
The audit methodology says scaling happens one proven workflow at a time, each returning to step one for the next process rather than rolling one result across an operation never mapped for it. The running cost article lists monitoring, model updates, prompt changes and user training as the ongoing work once a workflow is live. The task and decision article names a common way projects stall: a step scoped as Recommend treated as Execute once the demo looked good.
How we use it
What a prototype proved on the organisation’s own work is what gets built for production. The production article says finding a gap at scoping costs a conversation, while finding it six weeks into a build costs a redesign, which is why the earlier steps come first. Each live workflow gets a named owner, as the AI ownership page sets out.
FAQ
Questions about Production Deployment
What predicts whether an AI Project reaches Production?
The production article names three organisational traits: a named executive sponsor, a defined success metric tied to a real business outcome, and at least one internal champion. It also cites findings that a structured assessment before launch correlates with a 45% higher success rate on a first production deployment, and that projects pairing internal specialists with outside expertise succeed at 67% against 22% for internal-only builds.
What has to be in place before a Workflow goes live?
The audit methodology says oversight, logging, escalation and the point at which a person takes over are designed into the build, not added after something goes wrong. The organisation names an internal owner for each workflow and sends the people who will run it through training, so the operation belongs to the organisation rather than to whoever built it.
What it means for a Business
A workflow is not finished when it goes live. The ownership article says an unowned AI tool does not fail on launch day but months later and quietly, so an owner matters more after launch than before it.
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