AI Readiness Checklist: Is Your Business Software Ready for AI?

Adding AI to a product is not a strategy by itself. The strongest opportunities begin with a costly or slow decision, a repeatable workflow, and enough trustworthy data to improve that workflow. An AI readiness review prevents teams from funding a demonstration that cannot be governed or operated in production.

Define the decision and acceptable failure

Describe what the system will recommend, generate, classify, or automate. Identify the user who acts on the output and what happens when the output is incomplete or wrong. High-impact decisions need stronger validation, human review, auditability, and escalation than low-risk assistance such as summarizing internal notes.

Evaluate data and integration reality

Check data ownership, quality, representativeness, privacy, retention, permissions, and availability at the moment the feature runs. Document systems that provide context and systems that receive the result. If key knowledge lives in inconsistent documents or inaccessible tools, data preparation may be the first valuable project.

The NIST AI Risk Management Framework organizes AI risk work around four functions: Govern, Map, Measure, and Manage. These provide a practical structure for assigning ownership and evaluating risk throughout the lifecycle.

Run a measurable pilot

• Choose one bounded workflow and a baseline metric
• Create an evaluation set using representative cases
• Define privacy, security, and human-review controls
• Measure quality, latency, operating cost, and adoption
• Plan monitoring and fallback before wider release

Proceed only when the pilot demonstrates business value and the organization can own the resulting operational risk.

Reference: NIST AI Risk Management Framework