A workflow can contain several fast automated steps and still take days to finish. The delays often sit between systems and owners: missing context, unclear approvals, duplicate actions, and exceptions that nobody owns.

This session uses an illustrative enterprise workflow spanning work tracking, repositories, messaging, and internal APIs to examine where AI should interpret information and where conventional software should control state and transactions.

We will map handoff boundaries, define the record of truth for each transition, design validation and retry behavior, and route exceptions to an accountable human. The measurement model separates active work from waiting time and tracks rework, intervention rate, service-level breaches, and cost per completed workflow.

Attendees will leave with a handoff-mapping template, an exception ownership model, and a practical way to compare a baseline workflow with an AI-enabled pilot without confusing local task speed with end-to-end improvement.

Audience: Operations leaders, enterprise architects, product managers and automation practitioners. Format: 30–45 minute talk. No coding prerequisites. Examples are illustrative and do not claim measured client results.