An AI infrastructure project should begin with a bounded task and an explicit definition of failure. Architecture choices become more useful when the team knows what the system must accomplish, what errors matter and who remains accountable.
Specify the input and output
Describe who initiates the task, what information the system receives and what it is allowed to produce or change. “Add AI to support” is broad; “draft a response from approved help content for human review” is a tractable first workflow.
Identify the source of truth and what happens when required information is missing. A model should not be expected to manufacture facts to complete the experience.
Rank failure modes
List inaccurate answers, unavailable sources, slow responses, excessive cost and inappropriate actions. Consider the consequence of each rather than treating all failures as equivalent. A minor formatting error and an unauthorized account change need different controls.
Decide which actions require approval and which can be safely automated within the agreed scope.
Establish an evaluation set
Collect representative examples, including difficult and ambiguous cases. Define how success will be judged and who can adjudicate uncertain results. Compare a simple baseline with more complex approaches before investing in unnecessary infrastructure.
Use task-level outcomes rather than a vague impression that the system feels intelligent.
Plan the operational path
Specify logging, access control, retention, monitoring and fallback behavior. Assign an owner for source updates and quality regressions. Estimate cost per successful task, not merely cost per model call, because retries and review effort affect the actual economics.
The discovery phase should end with a concrete implementation decision: build, simplify, postpone or test further. A small workflow that can be evaluated and operated reliably is a stronger starting point than a broad demonstration with no clear boundary between suggestion and action.