A model is useful where the workflow contains ambiguity. If a standard rule can solve the task, AI often adds cost and new ways to fail.
Do not start with the model
Describe the event, input and expected action first. “We need an AI bot” says almost nothing. “Extract the goal, timeframe and budget from a message, then hand a record to a manager” defines a testable scenario.
Find the ambiguity
AI earns its place when fixed rules cannot reliably parse the input: free text, images, conversation context or variable classification. Moving an exact order number between two sheets does not need a model.
Design the failure
The system must know what to do with low confidence, incomplete data or an unavailable service. A sound AI workflow includes a handoff or retry path. “The model will always answer” is hope, not architecture.
Measure the decision
Evaluate the workflow outcome, not the elegance of the response: were the right fields extracted, did the task reach the right person, can an error be corrected? A demo must survive contact with real data.