Operations leaders facing backlog pressure often default to hiring. That works until workflow complexity outpaces training capacity. AI automation earns investment when three conditions align: the workflow is document-heavy or rules-based, errors are measurable, and a human can validate exceptions quickly. Revni recommends starting with one workflow where cycle time is already tracked. Map handoffs, measure baseline cost, and define an accuracy threshold before any model reaches production.