AI for Logistics Capstone Exercise — AI for Logistics

What you'll learn

Apply skills from across this course in a single realistic professional scenario
Produce a concrete, role-relevant deliverable using AI tools
Self-assess your output against professional quality criteria

Logistics leaders introducing AI rarely face a purely technical decision. The harder work is building a credible plan that names what actually went wrong last time, defines what is genuinely different now, and gives a skeptical executive team a way to judge readiness before volume, not after it. This capstone puts you in exactly that position, six weeks before peak season, with one prior automation rollout already behind you that did not go as planned.

You are the director of logistics operations at a mid-size distribution network. An AI-powered warehouse robotics rollout stalled earlier this year: strong pilot results at average volume did not hold once real order variability showed up, and the executive team is now cautious about approving further AI investment without a much more rigorous readiness case. Peak season is six weeks away. You will use AI to accelerate the drafting of a readiness brief while applying the domain judgment across forecasting, routing, warehouse operations, and risk monitoring that no AI tool can supply on its own.

Capstone Exercise

Build a Peak-Season AI Readiness Brief

Context

Your distribution network operates four fulfillment centers and a regional trucking fleet. Earlier this year, an AI warehouse robotics pilot performed well at average order volume but had not been stress-tested at peak-representative volume before a wider rollout was proposed; the executive team paused further automation investment as a result. Peak season begins in six weeks. You want executive approval to expand AI-assisted route optimization for the delivery fleet and to raise the automation level in one fulfillment center for the peak period, using the readiness principles from this course rather than repeating the mistake that stalled the robotics rollout.

Your Task

Use Claude or ChatGPT to produce a first draft of each of the following four sections of a readiness brief: (1) Current state and the specific lesson from the earlier stalled rollout, stated honestly rather than glossed over; (2) A four-dimension readiness assessment (data quality, process stability, organizational capacity, governance readiness) applied separately to the route optimization proposal and the fulfillment center automation proposal; (3) A peak-volume stress-test plan describing what would be tested, at what simulated volume, and what specific failure signals would trigger a scope reduction before peak; (4) A go/no-go recommendation with named conditions under which each initiative should proceed, be scaled back, or be delayed. For each section, write one annotation directly below the AI draft identifying the specific operational fact, historical detail, or judgment call that only you, not the AI, could supply, and that a skeptical executive would expect you to verify before presenting the brief.

Your notes (optional)

Deliverable

A four-section readiness brief of 600 to 750 words with one annotation per section identifying the human judgment or operational verification the AI draft could not supply. The brief demonstrates both where AI accelerates readiness planning and where the director's own operational judgment and historical knowledge remain irreplaceable.

Quick check

The capstone requires the four readiness dimensions to be scored separately for the route optimisation proposal and the fulfilment centre proposal. What does a single blended assessment hide?

Select one answer.

Key takeaways

A credible AI readiness case in logistics names the specific root cause of any prior failure rather than treating it as generic caution — the stalled robotics rollout in this capstone failed on peak-volume exception handling specifically, not on automation as a concept.
The four-dimension readiness assessment from Lesson 1 applies differently to different initiatives running in the same organization at the same time — blending them into one assessment hides exactly the variation a skeptical executive team needs to see.
A stress-test plan with named volume levels and named failure triggers is what turns "we learned our lesson" into a verifiable commitment an executive team can actually approve against.
The annotation discipline practiced throughout this course, marking what only human operational judgment can verify in an AI-accelerated document, is the habit that makes AI-assisted planning trustworthy enough to put in front of a skeptical audience.