Start with business outcomes
Strong AI opportunities usually connect to a clear business outcome: faster customer response, lower operating cost, better decision support, reduced manual effort, or improved risk visibility. If the use case cannot be tied to an outcome, it is probably not ready for investment.
Assess data and workflow maturity
AI depends on accessible information, repeatable processes, and people who understand the current workflow. Before scaling AI, review where data lives, who owns it, how accurate it is, and whether the workflow is stable enough to improve.
Include risk and governance early
Readiness also includes security, privacy, compliance, human review, and decision accountability. The goal is not to slow innovation. The goal is to make experimentation safe enough that teams can move with confidence.
Use a readiness assessment
A practical AI readiness assessment looks across goals, systems, data, workflows, risk, and leadership priorities. It should end with a short list of use cases, a governance baseline, and a roadmap for the next 90 days.