Enterprise AI Readiness Assessment

We have identified clear business use cases for AI, not just general interest.

An executive owner is accountable for AI outcomes.

We have an AI roadmap with measurable objectives.

Our key applications expose supported APIs or integration points.

Current architecture is documented well enough for a new team to work with it.

Technical debt does not block us from changing our core applications.

The data AI would need is available through a supported access path.

We trust the quality and consistency of that data.

Access controls on that data are well defined and enforceable per user.

We have a defined cloud architecture rather than ad-hoc resource deployment.

Landing Zone foundations — subscriptions, policy, networking, identity — are in place.

Development, test and production environments are properly separated.

Workloads authenticate with managed identities rather than shared keys.

Least privilege is applied and reviewed, not assumed.

Actions taken by systems are auditable end to end.

We have an acceptable use position for AI that teams actually follow.

We know which AI actions would require human approval.

We could monitor and, if needed, stop an AI system that behaves unexpectedly.

Deployments are automated and repeatable through CI/CD.

Infrastructure is defined as code rather than configured by hand.

Automated testing gives us confidence to change systems quickly.

Our processes do not depend on manual follow-up and status chasing to complete.

Critical work does not rely on email threads and spreadsheets to stay on track.