The first AI workflow is a product decision
2 min read
The first AI workflow should have an owner, inspectable inputs, and a result someone can accept or reject. “Automate operations” is too broad to reveal whether a pilot has helped.
Compare candidate tasks by input access, review effort, integration dependencies, and the cost of a wrong result. The AI workflow readiness guide turns those questions into concrete launch requirements.
Choose work with a clear finish line
Start with a recurring task whose inputs and outputs you can inspect. A draft that a person reviews is often easier to evaluate than an open-ended promise to automate a department.
Write down what a useful result looks like and what would make it unacceptable. Include the person who currently owns the work in that conversation.
Inspect the whole handoff
Map where the input comes from, who has access to it, and what happens after the output is produced. A model call is only one step in that sequence.
Keep exception handling visible. If a document is missing or a source is out of date, the workflow should help the operator resolve the problem.
Run a bounded pilot
Use a limited set of tasks and a named group of operators. Review outputs against the original task and record correction effort alongside completion.
Decide in advance what evidence would justify continuing, changing direction, or stopping the pilot. Keep the result of that decision in the delivery notes.
Put this into practice
Explore the related work and delivery scope.
- Related project: KIMEO
Inspect the published product workflow and delivery context.
- Explore the relevant service
See the engagement scope and start a specific project conversation.
Choose a first workflow with a clear boundary
Share candidate tasks, current systems, and the person who would own the pilot.
Discuss an AI pilot
