What an AI discovery engagement should leave behind

Agree on the questions discovery must answer

Identify the uncertainties preventing an implementation decision. These might include whether a source contains the needed information, whether an API permits the required action, or whether employees agree on the process. Keep each question tied to a decision. A Tennessee organization with several departments may need to settle authority and workflow ownership before selecting a model or designing an employee interface.

Require evidence behind feasibility claims

Ask the partner to distinguish inspected facts, tested behavior, and assumptions. If a connection was not tested because access was unavailable, the report should say so and describe the remaining experiment. A small approved technical probe may be more useful than a broad architecture diagram. Define its boundaries in advance so discovery does not expand into an undocumented production integration.

Leave with a scoped implementation option

The recommended option should name the workflow, intended users, permitted actions, exclusions, and acceptance cases. Include dependencies on your staff and a path for handling exceptions. NIST’s AI risk framework is a useful reference for contextual assessment; our recommendation is to turn the assessment into specific delivery decisions rather than stopping at a general risk register that nobody owns.

Reference: NIST: AI Risk Management Framework

Make the outputs transferable

Request editable documentation and clear ownership of the material produced, with commercial terms reviewed through your normal procurement process. Agentix strategy work can prepare a build brief and adoption roadmap. Judge discovery by whether another qualified team can understand the evidence and next steps. If major questions remain, record them openly and decide whether a second bounded investigation is justified before committing further.

Reference: Agentix (publisher): Agentix services

Common questions