Can AI Make Same-Day Delivery Smarter?
- Hypr Delivery
- 5 days ago
- 5 min read
New research points to opportunities for better routing, fleet optimization, and operational efficiency — but AI’s benefits come with important limitations and ethical considerations.
Unsurprisingly, artificial intelligence has become one of the logistics industry’s most closely observed technologies. But while most of the public conversation focuses on autonomous vehicles, drones, and robots, some of AI’s most immediate applications are considerably less futuristic.
In a Nutshell:
AI is increasingly applied to last-mile logistics through route optimization, forecasting, scheduling, and decision support systems.
Same-day delivery is particularly well suited for optimization because narrow delivery windows create complex routing and fleet-management challenges.
The most practical role for AI may be helping logistics professionals make better decisions — not replacing the people who operate the last mile.
Today, AI has a wide array of applications in the logistics industry.
Delivery providers are primarily using automated and assistive technologies to analyze extensive operational data, which means better insights into demand forecasting, vehicle scheduling, and route optimization. It also means that these companies can identify patterns and respond to changing conditions in real-time.
For retailers, these capabilities can influence everything from operating costs to delivery reliability.
The opportunities of AI are particularly significant in same-day delivery. Unlike traditional parcel networks, same-day operations often involve increasingly tighter delivery windows, changing order volumes, and sometimes, geographically dispersed customers.
In any case, same-day delivery involves decisions that must be made quickly.
Researcher Shahryar Sorooshian and colleagues conducted a recent literature review to examine the growing role of intelligent technologies in last-mile delivery. The authors divided these technologies into two broad categories: intangible technologies, like AI-based decision support systems and operating platforms, and tangible technologies, including robots, drones, and autonomous vehicles.
What they found is that intelligent technologies have the potential to make last-mile delivery both more productive and sustainable. That said, they also identified significant challenges surrounding implementation, ethics, policy, and workforce impacts.
The distinction is important. AI does not necessarily need to physically make a delivery to improve the delivery process. In many cases, its most immediate value comes from helping people determine what should happen next.
AI’s Most Immediate Opportunity May Be Better Decision-Making
Last-mile delivery produces enormous amounts of operational information.
Orders create data about destinations and timing. Vehicles generate information about location and availability. Drivers contribute performance data. And traffic systems provide information about congestion and everchanging road conditions.
With that in mind, the challenge is turning all that information into useful decisions quickly enough to matter.
Sorooshian and colleagues describe AI as a data-driven decision-making tool capable of using customer, traffic, geographic, and driver-performance information to support forecasting and operational planning. Specifically, their review identifies applications such as route selection, scheduling, predictive maintenance, and real-time adjustments when traffic or road conditions change.
AI can analyze customer, traffic, geographic, and driver-performance data to support better delivery decisions.
Any of this rich data could make a significant difference in same-day delivery, in which customers increasingly expect faster fulfillment.
Consider a retailer receiving several same-day orders within a relatively small geographic area. A conventional routing system may generate a route based primarily on distance and predetermined delivery parameters.
An AI-enabled system, however, can potentially incorporate a broader set of variables — traffic conditions, delivery windows, vehicle capacity, historical patterns, changing demand, etc. — to identify a more effective solution.
In this case, the goal isn’t necessarily to find the shortest route. It’s to find the route that best satisfies the operation’s priorities.
Same-Day Delivery Is an Optimization Problem
Same-day delivery is no easy endeavor. It requires providers to balance tight delivery windows with available drivers, vehicles, routes, and operating costs.
However, those qualities make it a natural application for optimization tools that can evaluate multiple variables while identifying more efficient ways to manage a delivery network.
In a 2025 study, Marjan Mosslemi and colleagues examine the transportation impacts of same-day delivery using an integrated modeling and optimization framework.
The researchers combined shopping-behavior modeling, activity-based travel modeling, and vehicle-routing optimization to estimate how same-day delivery could affect vehicle travel. Their case study focused on part of Irvine, California.
Delivery itself does not guarantee efficiency. The way delivery networks are designed matters.
They modeled same-day delivery as a multi-vehicle pickup-and-delivery problem with time windows and used Google OR-Tools to optimize fleet routing. Importantly, they did not simply assume that replacing shopping trips with delivery would automatically create transportation savings. Their framework accounted for factors including shopping behavior and trip patterns.
That methodological detail matters.
The literature reviewed by Mosslemi and colleagues show that the transportation effects of delivery can vary considerably depending on how the delivery network is designed.
For example, research by Jaller and Pahwa used travel-behavior modeling for San Francisco and Dallas and estimated a 7% reduction in vehicle miles traveled under a behavior-informed scenario, substantially lower than estimates from studies assuming complete substitution of shopping trips with delivery.
Delivery itself does not guarantee efficiency. The way that delivery networks are designed and managed matters.
That’s where AI and advanced optimization can become particularly valuable.
Where AI Can Create Practical Value
For retailers and delivery providers, the potential applications extend well beyond route planning.
AI can help forecast orders volumes, anticipate periods of higher demand, and determine how vehicles and drivers should be allocated amid changing conditions.
Sorooshian and colleagues specifically identify data-driven forecasting, dynamic decision-making, real-time tracking, smart scheduling, and route optimization as applications within the last-mile delivery. Research cited in their review also provides examples of what optimization can accomplish
For example, the review reports that DHL’s Greenplan dynamic tour-optimization system produced a reported 20% reduction in delivery costs, while Tesco’s AI-powered routing and scheduling system reportedly saved 11.2 million miles and 8% fuel per order.
DHL's Greenplan reportedly reduced delivery costs by 20%. Tesco's AI-powered routing reportedly saved 11.2 million miles and 8% fuel per order.
These examples illustrate optimization can address more than delivery speed; it can also influence resource utilization and operating costs.
Industry adoption is moving in a similar direction. Supply Chain Dive recently reported on alternative parcel providers using AI tools for functions such as customer service, delivery verification, fulfillment, batching, and routing.
The report highlights SpeedX’s AI-powered customer-service capabilities and Veho’s MaestroAI platform as examples of how newer delivery providers are using technology to improve their operations.
For smaller or regional providers, these developments are especially relevant. AI tools can potentially give organizations access to sophisticated analytical and optimization capabilities without requiring every operational decision to be handled manually.
The Opportunity — and Responsibility — of AI in the Last Mile
The benefits of AI are compelling, but greater reliance on algorithms also introduces new responsibilities.
Sorooshian and colleagues identify several challenges surrounding intelligent last-mile technologies: transparency, auditing, accountability, legal issues, fairness, equity, data security, workforce displacement, data-sharing reluctance, and a lack of internal AI expertise.
Data is particularly important.
AI systems depend on information, and last-mile operations can generate extensive data about customers, delivery locations, traffic patterns, and driver performance. According to the review, collecting and using this information must be done in ways that are both effective and lawful, while differences in data regulations can complicate implementation.
There is also the question of accountability.
If an algorithm recommends a route, assigns a driver, or evaluates delivery performance, who is responsible when that recommendation is wrong? AI should not become a black box that makes consequential decisions without human oversight.
Luckily, the logistics industry does not need to choose between technology and human expertise. The more immediate opportunity is to combine the two.
The value of AI in the last mile will not be determined by how much technology a company deploys. It will depend on whether that technology produces better decisions, more efficient operations, and a better delivery experience while maintaining appropriate human oversight.
For retailers and logistics providers, the question shouldn't simply be, “How can we use AI?”
It should be: “Where can AI help us make better delivery decisions — and how do we make sure those decisions are responsible?”
That is where the real opportunity for an AI-enabled last mile begins.
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