How to choose the first AI workflow
The best first deployment is rarely the most ambitious idea in the company. It is a workflow that happens often, has a clear owner, and produces an outcome the team can already measure.
Five questions before we build
Does the work happen often? Does it consume skilled time? Can the input be reached? Can a person review the output? Can the business observe the outcome? A workflow that passes four or five of these is worth a closer look.
What a good candidate looks like
- It runs at least several times each week, so there are enough examples to learn from.
- A specific operator or team owns it and can approve a change to how it works.
- The inputs already live in digital systems, documents, or messages.
- The output can be reviewed before the system earns more autonomy.
- Delay, error, cost, conversion, or throughput can be measured today.
What should wait
Work whose goal is stated as “do something with AI” rather than a named outcome.
- High-consequence decisions that would be automated before evaluation exists.
- Processes that depend on data the company cannot access or use responsibly.
- Company-wide rollouts attempted before one workflow has been proved.
“Bring the process, the systems, the pain, and the measure leadership cares about. That is enough to tell you whether it is a good first deployment.”
Conclusion
Choosing well is most of the work. A narrow, frequent, reviewable workflow with an owner and a baseline gives a deployment somewhere to stand. The ambitious version comes second, once the first one is holding up in daily use.
