Short answer
What matters most
Choose a repetitive, rules-led workflow with enough volume to matter, accessible data, a clear owner, and a safe human handoff. Avoid starting with the most sensitive or ambiguous decision; begin where mistakes can be caught before they become expensive.
Start with friction, not novelty
A workflow is a good automation candidate when people repeat the same steps, wait on the same information, or copy the same details between systems. The opportunity is not that AI can do something interesting. It is that a real bottleneck keeps taking time from people who could be doing more valuable work.
Walk through the process as it happens today. Note where work starts, who touches it, what information is missing, and where someone has to stop and check the result.
Use five filters before you build
A short evaluation prevents an exciting demo from becoming another system your team has to maintain. Score the workflow against these questions before choosing a tool or model.
- Volume: does the workflow happen often enough for improvement to matter?
- Repeatability: are the steps similar enough to define a reliable path?
- Data: can the system access the documents, messages, forms, or records it needs?
- Ownership: is one person accountable for the workflow and its outcome?
- Handoff: can a person review uncertain, sensitive, or expensive cases?
Good first workflows
The safest first project usually improves an existing process rather than replacing a judgment-heavy role. Examples include turning incoming documents into checked records, routing new enquiries, preparing a grounded answer from internal knowledge, or sending a follow-up when a known condition is met.
- Document intake with field checks before a record is created.
- Lead or customer follow-up that uses clear timing and escalation rules.
- Internal knowledge lookup that shows the source of an answer.
- Status updates that keep a CRM, spreadsheet, or operations system in sync.
Workflows to leave for later
Do not make the first project a high-stakes decision with unclear rules, missing data, or no accountable owner. If the business cannot describe what a good result looks like, it will be difficult to evaluate the automation honestly.
The same is true when a wrong result cannot be detected before it reaches a customer, creates a financial commitment, or changes a sensitive record. Start with a process where review and rollback are practical.
Prove the workflow before expanding it
A useful first release should make one part of the process easier to observe and improve. Define the input, the expected output, the checks that must pass, and the cases that go to a person. Then run it alongside the current process long enough to find the exceptions that were invisible on paper.
- Measure completion and escalation, not just whether a model produced text.
- Keep the original record available for review and correction.
- Log the reasons a person changed or rejected an output.
- Expand the workflow only after the rules, data, and handoffs are clear.
The practical next step
List the three workflows your team repeats most often. Pick the one with the clearest owner, the most accessible information, and the safest review path. That is usually a better starting point than the workflow with the biggest promise.