Measure the work before automating it
Start with frequency, handling time, wait time, error rate, exception rate, and the people involved. This reveals whether the problem is expensive repetition, unclear ownership, missing data, or a process that should be removed entirely.
A workflow that runs twice a month may not justify custom engineering. A five-minute task repeated hundreds of times with frequent handoff errors might.
Look for stable boundaries
Strong automation candidates have a clear trigger, accessible data, repeatable rules, and an observable result. Ambiguous work can still benefit from AI assistance, but judgment-heavy decisions need review gates and accountable owners.
- High volume or costly delay
- Consistent input and output
- A manageable exception rate
- System access through APIs, webhooks, or controlled data exchange
- A safe manual fallback
Design the failure path first
A demo shows the happy path. A production workflow must handle timeouts, duplicate events, partial data, revoked access, changed formats, and services that are temporarily unavailable.
Retries, idempotency, alerts, logs, and a clear recovery queue are part of the product—not optional operations work.
Keep people where consequences are high
AI can classify, summarise, draft, or prioritise without being given final authority. Confidence thresholds and approval steps let teams capture speed while protecting customer relationships, money, access, and compliance-sensitive decisions.
Calculate value after operating cost
Compare time saved and errors avoided against build cost, subscriptions, maintenance, review time, and the cost of exceptions. Review the result after launch; if the expected value is not appearing, fix the workflow or stop expanding it.