Five signals that a workflow is worth assessing
Look for repeated work before you look for AI products.
Map the workflow before choosing the tool
Write the trigger, each major step, the systems touched, the output and the person accountable. That reveals whether the problem is actually AI-shaped or whether integration, templates or rules-based automation would solve it more cheaply.
Rank opportunities on fit, value and risk
A high-value workflow is not automatically a good first AI project. Strong first pilots combine reasonable AI fit with enough economic value and a bounded failure mode.
| Position | Action | Why |
|---|---|---|
| High fit + low risk | Pilot quickly | Good first candidate |
| High fit + high risk | Pilot with stronger controls | Keep approvals explicit |
| Low fit + high value | Improve process first | Do not force AI into it |
| Low fit + low value | Deprioritize | Spend attention elsewhere |
A simple first-week plan
- List ten recurring workflows that consume visible time.
- Pick three with clear volume and owners.
- Measure frequency and minutes for a week.
- Score data readiness and consequence of error.
- Pilot the highest-fit reversible workflow first.
Frequently asked questions
What business tasks are best for AI?
Tasks with repeated inputs, pattern recognition or drafting, reviewable outputs, accessible data and enough volume to justify implementation are often stronger candidates.
Should I automate the most expensive workflow first?
Not necessarily. A lower-value workflow with cleaner data and lower risk can be a better first pilot because it produces evidence faster.
What if a workflow has many exceptions?
Treat high exception rates as a warning. Standardize the process or keep the AI boundary narrow before attempting broader automation.