AI in Business Operations and Workflows
What to know
AI creates operational value where work is repetitive, high-volume, or document-heavy. Candidates should recognise common patterns: classification and routing (tickets, invoices), extraction (pulling fields from contracts), summarisation, drafting, anomaly detection, and forecasting. Know the difference between automation (fixed rules) and augmentation (AI assists a human who decides).
Understand workflow design: map the process, identify the decision point, decide where a human stays in the loop, and define exception handling. Metrics matter - cycle time, first-contact resolution, error rate, straight-through processing rate, and cost per transaction. A pilot should have a baseline before launch, otherwise improvement cannot be demonstrated.
Traps
Questions reward the narrowest reliable scope, not the most ambitious. Beware answers that remove human review from high-stakes or regulated steps, or that automate a broken process without redesigning it. Another trap is measuring activity ("emails drafted") instead of outcomes ("resolution time reduced"). Also distinguish a pilot from a production rollout: pilots tolerate manual workarounds that will not scale.
Worked example
An accounts-payable team processes 4,000 invoices monthly. A sensible design: AI extracts fields, a rules layer validates totals against purchase orders, low-confidence extractions route to a clerk, and payments above a threshold always require human approval. Baseline error rate and cycle time are recorded first. Rule of thumb: automate the extraction, keep a human on the money.