Playbook
Enterprise AI Readiness Checklist
Enterprise AI readiness means the company can safely connect data, govern outputs, evaluate quality, manage cost, and route risky decisions to humans.
Best reader
Enterprise teams planning AI copilots, RAG, automation, or agent systems
Outcome
A readiness view that separates quick wins from AI work that needs deeper governance.
Use this sequence
Classify data by sensitivity and owner.
Define acceptable AI actions and blocked actions.
Plan evaluation, monitoring, and cost controls.
Design human review for high-impact outputs.
Set security and compliance requirements before build.
Data readiness comes first
AI systems fail when knowledge is stale, inaccessible, untrusted, or permissioned incorrectly.
Source inventory
Access rules
Update frequency
Retention requirements
Govern actions, not just answers
Enterprise AI often triggers workflows. The system must define what AI can suggest, draft, update, or execute.
Read only
Draft only
Approval required
Never allowed
Evaluate before scaling
A small evaluated launch is better than a broad rollout with no quality signal.
Golden test set
Human grading
Failure categories
Monitoring dashboard
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Frequently asked questions
What is the biggest blocker to enterprise AI readiness?
Data and governance are usually bigger blockers than model capability. Teams need clear access rules, review paths, and evaluation before scaling.
Do all AI projects need heavy governance?
No. Low-risk assistants need less control than systems that affect money, customers, compliance, or production operations.