Start with the business decision
AI work becomes useful when it improves a decision, removes operational drag, or creates a new capability that the organization can actually adopt.
Before investing, leaders should be able to name the decision, workflow, or customer moment that will improve. If the value cannot be explained in plain business language, the opportunity is probably not ready for a build phase.
Check whether the data can support the use case
Most AI initiatives fail earlier than teams expect because the data is scattered, inconsistent, sensitive, or owned by teams that are not part of the project.
A useful readiness check asks whether the data exists, whether it can be accessed responsibly, whether quality issues are known, and whether the organization has a plan for keeping it current.
Define the workflow owner
AI systems do not create value in isolation. They need a workflow owner who understands the operating context, the exception cases, and the success measures.
If no one can own adoption, feedback, and iteration, the organization may be better served by a smaller prototype or advisory phase before committing to implementation.
Decide how risk will be managed
Security, privacy, accuracy, hallucination risk, vendor lock-in, and operational dependency should be discussed before a pilot is launched.
The goal is not to eliminate all uncertainty. The goal is to decide which risks matter, who owns them, and what guardrails need to exist before the system touches real work.