Start with repetitive, rules-heavy work where inputs and outputs can be observed—not with a tool looking for a problem.
Look for queues, copying and predictable decisions
Good candidates often live between systems: re-entering form data, routing requests, preparing standard summaries, checking known conditions or assembling recurring reports. The task should happen often enough that reducing effort matters, and its successful output should be recognizable.
Document the current process before automating it. If no one can explain the inputs, exceptions, owner and definition of done, automation will make the ambiguity move faster rather than make it disappear.
- High frequency
- Stable inputs
- Observable output
- Manageable exceptions
- A clear human owner
Keep judgment and accountability visible
AI is useful when language or messy information prevents ordinary rules from working, but probabilistic output changes the risk. A draft, classification or recommendation can be helpful while still requiring review. The workflow must say who checks it and what happens when confidence is low.
Avoid silently automating decisions that materially affect people, money, safety or legal rights. In those cases, the system should support a qualified human, preserve context and make its actions traceable.
Pilot one workflow and measure the whole result
Estimate the current time, error rate, delay and rework before changing anything. Then run a bounded pilot with real examples, including awkward exceptions. Track not only time saved, but also review effort and the new failure modes the automation introduces.
A useful pilot ends with an operating decision: stop, revise or scale. If it scales, add monitoring, permissions, documentation and a named owner before the workflow becomes business-critical.
Automate a well-understood workflow, keep a human accountable and measure review effort as carefully as time saved.
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