1. Look for repeated friction, not flashy ideas
The best opportunities often involve copying data between systems, chasing approvals, preparing reports, classifying requests, or notifying exceptions. Collect frequency, time per case, error rate, and delay cost. A small process that occurs one hundred times a week can create more value than a sophisticated AI demonstration.
2. In sales, automate continuity and data quality
Good first cases include capturing form leads, validating fields, enriching records, assigning owners, creating follow-up tasks, and alerting when a deal stalls. AI can summarize a conversation or classify intent, but territory, priority, and consent rules should remain explicit.
3. In operations, make exceptions visible
Synchronizing orders, inventory, invoices, or tickets creates value when the workflow validates data and exposes what it could not resolve. Avoid automation that silences errors. An exception queue with an owner, cause, age, and retry option turns the workflow into an operable system.
4. Calculate value and maintenance cost
Estimate hours recovered, errors avoided, speed gained, and the effect on revenue or service. Subtract licenses, API usage, support, and expected changes to connected systems. Return is not measured only on launch day: review the workflow after 30 and 90 days using real data.
5. Launch with an owner and recovery playbook
Every workflow needs a business owner, technical owner, alerts, least-privilege credentials, and a procedure to pause, correct, and reprocess. Record which version is active and what changed. If nobody knows what to do when a provider fails, the automation is not finished.
- Frequency, time, errors, and cost of delay
- Stable process with a business owner
- Rules separated from AI steps
- Visible and recoverable exceptions
- Baseline metric and 30/90-day review
Primary references
Turn the guide into a concrete project