We've all heard the stat: the last-mile accounts for roughly 53% of total logistics costs. No logistician or last-mile professional can ignore that. In fact, most focus deeply on it with explicit goals to reduce cost. And that makes sense.
But, the last-mile delivery is not just a cost problem anymore—it isn’t even the right starting point. Now, the problem starts (and ends) at the front door.
Delivery is the only part of the supply chain the consumer directly experiences. They don't experience the carrier procurement process. The warehouse or store pick and pack. The route planning. And so on. They do, however, experience the actual delivery. They select specific delivery windows, receive status notifications, and answer the door for delivery drivers. How reliably a purchase arrives at a customer’s front door deeply matters and this is where AI can drive the biggest impact.
Key takeaways
- Last-mile delivery is now a customer-lifetime-value problem, not just a cost problem. And that starts with the front door experience. Consumers decide where to shop based on delivery, pay more for reliability, and reward retailers who get it right with repeat purchases.
- Every front-door experience traces to a chain of operational decisions made at different time horizons, from quarterly capacity plans to real-time exception handling. Improve the right decision at the right time horizon, improve the experience.
- AI earns its place when it improves specific decisions at specific time horizons. Model network changes against real demand, simulate capacity tradeoffs before planners commit, or adjust real-time communication based on context rather than static logic.
Delivery drives loyalty
Research shows that delivery matters more than almost anything else when shopping online and, ultimately, determines whether a customer comes back.
The Bringg 2026 Delivery Experience Study makes this concrete:
- Before a transaction even happens, delivery impacts a consumer's decision: 71% consider delivery options before checkout. Among power shoppers, those who buy more than 10 times per month and spend the most, over half think about delivery before they even start shopping.
- After the delivery, reliability drives revenue: 65% will pay more to buy again from retailers who get it right.
- The downside compounds faster than the upside: Half of all shoppers have stopped buying from a brand solely because of delivery. And for power shoppers, that number climbs to nearly 70%.
Cost is absolutely a last-mile challenge. Of course it is. But now, every delivery is a brand moment, and consumers treat it that way. The result? Last-mile delivery is now a customer-lifetime-value problem.
Cost, experience, and AI: three problems at once
This puts last-mile leaders in a difficult position. They already face pressure to reduce delivery costs. Now, the data says they must focus on the delivery experience and ultimately own customer loyalty, or churn. On top of both, there's an enormous amount of noise about AI: what it can and can't do for logistics operations. Reduce costs. Improve customer experiences. And figure out where AI accelerates both.
These aren't sequential priorities; they're simultaneous. And most last-mile AI conversations don’t acknowledge that inherent tension. Is AI a feature list, or a new tool? Does it improve operations, decision making, or customer experience?
Sometimes an AI capability will be the right answer for a particular set of decisions. And the wrong answer for another. Sometimes the right answer won't involve AI at all. The only way to know the difference is to start from the outcome, not the technology. To start from the front door.
So what determines the front door experience? What actually produces the outcome the customer sees?
Sometimes the right answer won't involve AI at all. The only way to know the difference is to start from the outcome, not the technology.
The decisions before the front door
What happens at the front door is the result of a chain of decisions that cascade through logistics workflows. It’s not one decision, not one team. It’s a sequence of events, where each sets the conditions for the next on a different time horizon and impact level.
It starts with the decisions most removed from the customer. Once a quarter or once a year, someone decides how much capacity an operation needs. How many fixed assets? Which territories? What does the network look like? These big structural decisions have the longest time horizons and the largest financial consequences. When they're right, everything downstream gets easier.
When they're wrong, every team across the operation spends the next three to 12+ months compensating. Seasonal demands outstrip capacity because the forecast relied on trailing averages. Territory design that hasn't been revisited in two years while consumer behavior and order patterns shifted underneath it. Route density drops, cost-per-delivery rises, and on-time rates decline regardless of what actually happens each day. These structural gaps determine what the customer sees at checkout: available delivery windows, how flexible the scheduling is, and whether the delivery promise fits their needs at all.
Structural gaps determine what the customer sees at checkout…and whether the delivery promise fits their needs at all.
Those structural decisions impact recurring decisions across route planning, carrier allocation, workforce scheduling, delivery slot management, and more. They translate the quarterly and annual plans into the conditions that dispatchers and drivers face each day. When a territory is short-staffed on Thursday and late deliveries spike, that's not a Thursday problem. That's a planning decision made the previous Friday that didn't match capacity to demand. Every manual adjustment a dispatcher makes during the week is a signal that the plan was off.
Customers don't know, or care about, a missed quarterly forecast or an incorrect weekly plan. But they feel it. It shows up as fewer available delivery windows, longer time slots, less ability to reschedule and 61% of them abandon the cart. That revenue disappears before a delivery is even attempted.
Customers feel operational gaps
Customers also don’t connect limited delivery windows to a capacity decision made months earlier. But when the actual delivery experience doesn’t meet their expectations, they know exactly who to hold responsible.
Real-time decisions like dispatch assignments, driver routing, consumer communication, and exception handling impact customers immediately.
When a route falls behind and nobody communicates, that's an exception-management decision that didn't get made. When a customer can't get an accurate arrival window or receives a generic status update, that's a communication problem that relies on static logic, not dynamic conditions. So the consumer consequences of these real-time failures, the ones the delivery experience research covers in detail, are immediate and often permanent.
The consumer consequences of these real-time failures…are immediate and often permanent.
The decision cascade works in one direction: from structure to plan to execution to front door. A wrong quarterly call about capacity turns into a short-staffed Thursday, which turns into a late delivery, which turns into a churned customer. Every front-door outcome traces backward through this chain to a decision made hours, days, or months earlier.
But the problems don't always start at the top. Sometimes the quarterly plan was sound and the weekly schedule was right, but the dispatcher didn't have the information to adjust when a driver called in sick at 6 AM. Sometimes the route plan was fine, but the system sent every delayed customer the same generic notification instead of explaining why their specific order was late and what their options were.
The point isn't that all problems are either structural, time-based, or immediate. It's that every front-door outcome is connected to upstream decisions, and not every single one can be solved through a single approach.
Last-mile AI earns its place
This is where AI has a chance to earn its place. Not as a feature applied broadly across the operation, but as a capability matched to the decision where a specific problem actually occurs.
AI that helps operations leaders model network changes against real demand signals before they invest. AI that helps planners simulate next week's capacity scenarios to see cost and service tradeoffs before they commit. AI that handles the variability in real-time exceptions faster than any manual process and adjusts context-based communication.
The technology can deliver different decisions, different frequencies, different capabilities. All of which are then measured against the same things: did we reduce costs, and did the front door experience improve? When done well, the answer is “yes” to both.
The companies that get this right won't be the ones with the broadest or flashiest AI footprint. They'll be the ones that trace and link the chain from the front door back through the entire operation, identify where the decisions actually break down, and apply the right tool at the right level to fix them. Sometimes the solution will be AI. Sometimes it won't. The ones who know the difference will win.
The next post in this series goes inside last-mile operations, where the three types of decisions stand today, the questions every operator should ask before deploying AI.
About Yishay Schwerd
Yishay Schwerd is the Chief Product and Technology Officer at Bringg. He leads the engineering and product teams that power last-mile performance for the world's largest retailers and logistics service providers. Before Bringg, he served as Chief Technology Officer at Personetics, where he led the development of AI-driven solutions that transformed customer engagement in the financial services sector.
