AI becomes useful when it is connected to a real workflow.
The tool matters, but it is rarely the whole answer. A useful AI pilot needs a clear service moment, a defined task, a human decision, a risk boundary, and a way to know whether the work is actually improving.
What I look for first
- What work is repeated often enough to matter?
- Where does the customer or employee experience break down?
- What decision does the team need to make?
- What evidence would show that the AI support is helping?
- What guardrails are needed before the workflow can be trusted?
That is why I prefer starting with the workflow rather than starting with a generic AI use case. The workflow shows where the value could be, where the risk sits, and what the team needs to learn next.
A practical starting point
Before building anything, write the workflow in plain language:
- the trigger
- the person or team involved
- the information needed
- the decision to make
- the action that follows
- the measure of improvement
Once that is clear, the AI question becomes much easier: where can an agent, assistant, or automation help this work become faster, clearer, safer, or more useful?