Use explicit rules for decisions with clear inputs and deterministic outcomes. Consider AI for variable information tasks where the business can define evaluation, review, and acceptable failure handling.
Prepared with AI assistance. These are practical scoping recommendations; examples are illustrative, not client results.
Classify the task
Routing a request by a known account code may need a rule. Interpreting an unstructured description may benefit from a different approach. Do not add AI merely because a process is manual. First identify whether the difficulty is missing information, unclear responsibility, or genuine variation in the input.
Define the output boundary
Specify what the AI may suggest or produce and what it may change directly. A draft summary and an approved customer commitment have different consequences. Keep permissions and human review aligned with the action, rather than granting broad tool access because the system is called an agent.
Build an evaluation set
Use representative examples, including ambiguous and incomplete inputs, with expected outcomes or review criteria. Assess the task itself rather than relying on a general model demonstration. Record where the system should ask for help instead of presenting an uncertain result as a completed decision.
Compare operating effort
Include review, monitoring, corrections, and ongoing evaluation in the decision. A rule-based process with a clear exception queue may be more suitable than an AI workflow for some tasks. Choose the approach that supports dependable work within the agreed boundaries, and revisit it when actual operating evidence changes.