PRACTICAL FIELD GUIDE
Enterprise AI Readiness
What makes an organization ready for enterprise AI?
The short answer
Enterprise AI readiness means the organization has a valuable use case, usable data, accountable owners, appropriate controls, and a workforce prepared to change how work gets done.
The leadership decision
Ask this first
Which business problem is important enough to change before we add more AI capability?
Watch for this signal
A pilot has interest but cannot name one accountable owner, a credible outcome measure, or the work that must change.
Make this next move
Choose one contained workflow, name its owner and evidence of value, then use the diagnostic to create a 30-day plan.
Make progress visible
Choose evidence people can review together, then use it to adjust the work before calling the effort a success.
Four operating priorities
Business outcome before tool selection
Make this visible in the operating model, assign ownership, and connect it to evidence the organization can review.
Data quality, access, and ownership
Make this visible in the operating model, assign ownership, and connect it to evidence the organization can review.
Governance proportionate to risk
Make this visible in the operating model, assign ownership, and connect it to evidence the organization can review.
Adoption measured in changed work
Make this visible in the operating model, assign ownership, and connect it to evidence the organization can review.
The Human Signal Method
Learn
See the work as it is, including exceptions, delays, unofficial fixes, and the people affected.
Apply
Frame the decision, the desired outcome, and the smallest useful change before selecting or scaling technology.
Enable
Clarify ownership, information, governance, skills, and support so people can use the change with confidence.
Amplify
Review evidence, share what worked, repair what did not, and scale only when the conditions are durable.
A practical starting sequence
- Name the business outcome and the people affected.
- Map the information, systems, decisions, and permissions involved.
- Choose a contained use case with a measurable result.
- Set the review, escalation, and learning process before launch.
- Scale only after the evidence supports it.