The final mile—the last leg of the delivery process where goods are transported from a distribution center or store to their ultimate destination—is one of the most critical and cost-sensitive components of the modern supply chain. A package could end up at the wrong address, shipments could be late due to traffic, or a thunderstorm could damage a parcel left out in the rain.
Now AI and machine learning are playing a greater role in predictive analytics, helping companies anticipate delivery issues before they occur and proactively adjust. AI can design more efficient delivery routes, improve accuracy and the customer experience, and predict errors before they might happen, writes Material Handling & Logistics (July 22, 2025).
A new McKinsey report found that in the last decade, about $80 billion in venture capital went to logistics startups, with on-demand last-mile delivery platforms getting the greatest share of those funds.
Last-mile routes typically involve multiple stops and individual small packages — rather than one truck delivering pallets to a single warehouse — making this supply chain segment difficult to manage efficiently and expensive for the businesses involved. Last-mile delivery makes up an estimated 41% of all logistics costs in the supply chain.
AI can be used to plan routes based on factors such as traffic, delivery windows, estimated time per stop, and driver capacity, reports Business Insider (July 15, 2025). More efficient routes can lower fuel costs, improve density, and enable more deliveries in a day, increasing revenue for providers. Amazon just announced Wellspring, which uses AI to analyze satellite images, apartment building layouts, street imagery, consumer instructions, and photos from past deliveries. It can recommend which parking spot or apartment building entrance a driver should use to drop off a shipment.
AI can also forecast the likelihood of issues for specific routes or deliveries. Then it can make decisions based on the patterns, like moving packages to different facilities or increasing rates on a certain route, so drivers will be incentivized to pick them up earlier in the day. UPS created AI-based DeliveryDefense to analyze historic factors such as loss frequency and delivery attempts. The AI then spots areas that could be targets for porch pirates in the future.
Companies that can balance cost efficiency with delivery accuracy will be best positioned to thrive in today’s environment of volatility and heightened customer expectations.
Classroom discussion questions:
- How can AI be used in last-mile delivery?
- What are the complicating factors in last-mile deliveries?