Is your organisation ready to put AI into production?

A named executive owns the outcome and the budget for this initiative.

The initiative is tied to a business KPI, and that KPI has a measured baseline today.

You have agreed in advance how you'll decide to scale, change or stop it.

The data this initiative needs is identified, accessible and has an owner.

Data quality is measured, not assumed.

Data is refreshed as often as the process needs it.

A secure cloud environment (accounts, identity, logging, network controls) is already in place.

Infrastructure is defined as code and deployed through automated pipelines.

Cloud costs are tagged and visible per workload.

Rules define which data may be used with which AI models and services.

Legal, security and risk have reviewed this specific use case.

There is a defined way to monitor output quality and deal with wrong answers.

Your team can run and change the solution after go-live, without depending on a vendor.

The people whose work will change have been involved in the design.

Time has been allocated to this work, not just goodwill.

Monitoring, alerting and incident handling are defined for production.

There is a support model (internal or managed) with agreed response times.

Changes to models and applications follow a controlled release process.