The best AI automation opportunities are repetitive enough to map but variable enough that rules alone struggle. Common examples include classifying requests, extracting document data, drafting responses and routing work to the right team.
Score value, feasibility and risk
Estimate current volume, handling time, error cost and delay. Then check whether inputs are available, decisions can be evaluated and actions can be reversed. Start with high-value work where mistakes are detectable and recoverable.
Separate deterministic and AI steps
Use normal workflow logic for validation, routing, calculations and system updates. Reserve models for language, interpretation and fuzzy judgement. This keeps the automation predictable and easier to audit.
Design the exception path first
Every production workflow needs timeouts, retries, duplicate protection and a clear queue for human review. The exception experience determines whether teams trust the automation when real-world inputs get messy.
Improve with operational evidence
Track straight-through completion, human correction, exception types, processing time and cost. These measures show whether to improve the model, the workflow or the source data.
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