An illustrative delivery-exception workflow

Imagine a logistics team receiving questions about failed deliveries. Staff look up tracking, contact the consignee, assemble recovery options, and ask a supervisor to approve a cost. This example shows how to evaluate an AI team: coordinated AI agents assigned distinct business roles, connected by a workflow to system actions and human decisions. It is fictional, with no customer results or promised savings.

The first objective is deliberately specific: prepare a useful exception case for the dispatcher. Do not start by promising automatic resolution of every delivery problem.

Understand the work before designing the worker

Review a representative sample of recent requests using your approved data-handling process. Record what staff needed to find, which systems they used, why they asked for help and how completion was confirmed. Include awkward cases as well as straightforward ones.

Use a short process brief: trigger, required information, permitted actions, human decisions, completion record and failure path. For this example, completion might mean a dispatcher has accepted the case with the information needed to decide—not merely that a summary has been generated.

Assign a clear role to each AI agent and person

  • Intake worker: gathers the shipment reference and reported problem.
  • Context worker: retrieves approved tracking information and flags missing or conflicting facts.
  • Recovery preparation: assembles permitted options for review.
  • Dispatcher: decides operational feasibility and accepts ownership.
  • Supervisor: approves cost or exceptions where your policy requires it.

These are proposed responsibilities. Confirm whether one worker or several is appropriate after examining the actual process. More workers are not an outcome in themselves.

Run a supervised evaluation

Test a routine delay, a missing tracking update, a mismatched shipment reference and an unavailable dispatcher. Include a repeated request so the team can see whether it creates duplicate work. Confirm the destination record and the receiving team’s view rather than relying on the worker’s summary.

Compare the pilot with the baseline using the same definitions: staff preparation time, missing information, rework, accepted handoffs and cases reaching the agreed outcome. Keep unresolved cases in the denominator and record reasons for failure.

Make an evidence-based expansion decision

At review, choose among improving the same workflow, expanding its scope or stopping it. Identify which results support the decision, who owns remaining issues and how the team will monitor changes. A useful first workflow can stay narrow until the evidence supports more.

Explore the delivery-exception blueprint, team design and operations scorecard. Use your own process brief when planning a pilot; timing and acceptance criteria should follow the work you select.