The last mile: it’s one of the most data-rich environments in the supply chain, yet much of the industry still relies on aging technology that traps data in isolated systems. As we move into 2026, however, the industry is hitting an inflection point. Last-mile providers can no longer compete using legacy point applications: interoperability, intelligence and the ability to act on unified data in real time are the new differentiators that will distinguish the leaders from the laggards. For organizations seeking to handle growing demand, volatility in volume patterns and intensifying expectations for delivery accuracy and transparency, four trends are emerging as transformational.
Integrated technology ecosystems are a must
For many last-mile operators, legacy systems continue to dominate the tech stack. These ‘generation-one’ apps were engineered to do a single thing, as opposed to connecting broadly across routing, visibility, order management and other systems. Today, siloed data is not only an inconvenience that drains productive time for resources; it also directly restricts last-mile performance and compromises customer and driver satisfaction.
Innovations in artificial intelligence (AI) and advanced analytics exacerbate the problem. Without connected data streams between systems, AI tools are operating, in a sense, in the dark, unable to fully deliver the predictive capabilities and insights that modern operations depend on. They cannot, for example, forecast when more vehicles are needed on the road, when flex fleets are required, or when peaks (not just the more predictable seasonal ones) may hit. For urban environments where congestion complicates routing, unified data becomes even more critical to drive agility and high standards of fleet performance.
AI and advanced analytics are becoming a baseline expectation
AI has rapidly become part of consumers’ everyday lives, from virtual assistants and chatbots to generative AI for creative and lifestyle applications, and uses in diverse fields such as health, education and more. In business-to-business environments, however, organizations need to move beyond treating AI as another point solution to harnessing its power as a more holistic engine for enterprise decision-making. In last-mile operations, this shift is happening now as leading technology companies innovate with AI.
For fleet operators, technology is most valuable when it moves beyond isolated route planning and execution to evaluating all operational data – orders, routes, stops, deliveries, delays, traffic patterns, networks and customer behaviors – in near real time. This makes delivery bottlenecks visible sooner, helps teams deploy resources faster and gives fleet managers new forecasting power over volatility. Equally important, this power compounds over time: with every new data point, AI models improve, enabling more precise predictions and dramatically reducing manual intervention.
Route-planning model starts with execution data versus assumptions
Traditionally, last-mile implementations have started with route-planning tools: deploy route-planning software and, over time, feed in real-world execution data to improve route-planning accuracy. The problem? This level of refinement can take 12 months or more. Forward-thinking fleet operators are now actually flipping the model and building highly accurate route plans in two or three months rather than in year two. How? Instead of ‘digitalizing assumptions’ to operationalize route-planning tech, they’re capturing route execution data at the outset of a deployment.
This improves route-planning accuracy and minimizes the waste (e.g. mileage, fuel, driver productivity, emissions) that occurs when planning tools overestimate route durations. Execution-first data collection reveals critical nuances, like the fact that delivering multiple parcels to an apartment building takes longer than delivering to a single‑family home, or that urban parking realities can change stop-time assumptions. Some fleet managers have discovered they were carrying more than 30% excess route time simply because service time assumptions were outdated or imprecise.
Digital twins are becoming table stakes
Digital twins – virtual but dynamic replicas of the full delivery network – are rapidly becoming indispensable in the last-mile space. With a digital twin, companies can monitor fleet performance, predict the impact of changes and identify areas for improved productivity or customer service. They can also test and validate the impact of proposed changes by creating what-if scenarios, such as around fleet composition, new product lines, additional customers, electric vehicle adoption or micromobility strategies in dense cities.
Most fleet operators want to be more agile but are slowed by the validation process. With a digital twin, the ability to clone, tweak and run models against operational data helps organizations more quickly and accurately assess cost, service and other operational impacts of potential changes. In addition, AI-based technology can provide configuration options that consider scenario-based business objectives, helping companies shift away from reactive problem-solving to predictive optimization for delivery networks.
Parting thoughts
In 2026, competitive advantage in the last mile will not be about being the lowest-cost provider. Instead, successful companies will be those focused on data as a strategic asset. Organizations that unify fleet operations data, apply AI holistically and use predictive modeling will be able to deliver more efficiently, with greater accuracy and complete end-to-end visibility.
About the author
Cyndi Brandt is the VP of fleet solutions at Descartes, aligning go-to-market strategy in the routing, mobility and telematics space with industry needs. She has spent the last 23 years in transportation technology, spanning final-mile optimization, dispatch and tracking, telematics and ELDs, video-based safety and data analytics. Having spent time with last-mile and over-the-road companies, Cyndi understands the differences and similarities of the spaces. Previously, she worked with Roadnet Technologies, UPS, Omnitracs, Solera and Matrix iQ across the spectrum of go-to-market activities. Cyndi is an active board member in her passion project, the non-profit Mid-Atlantic Women’s Motorcycle Rally.