Every front-door outcome traces back to decisions made at three different time horizons: structural decisions made months in advance, recurrent decisions made weeks ahead of time, and real-time decisions made in the moment. Last-mile AI earns its place when it improves the right decisions at the right time.
Going beyond decision-making and into day-to-day operations, the supply chain industry seems to have reached a consensus on where last-mile AI belongs. That's a problem.
Routing AI investments are saturated
AI adoption in routing and visibility already sits above 70% according to Bringg research in The 2026 Last-Mile Performance Outlook. The report surveyed 150 retail and logistics executives at companies with more than $1 billion in annual revenue and found that routing and visibility are the most invested-in, most discussed, most demo'd AI application in last-mile delivery. And it's valuable. Route optimization improves on-time rates, reduces windshield time, and tightens delivery windows. These are all critical, but when an entire market concentrates AI investment in one function, the question changes. It's no longer, does routing AI work? But, what are we not fixing if we’re all fixing the same thing?
To answer that, start with the decisions themselves. The chain of decisions—structural, recurrent, and real-time—doesn't just explain how front-door outcomes form. It reveals where AI has concentrated and where it hasn't. Routing AI is a real-time decision, and that decision inherits every structural and recurrent decision made before it, anywhere from one week, one year, or even a decade ago.
The operational decisions routing AI doesn't touch
Take recurrent decisions, for example. Every week, a planner builds the next week's carrier allocation against projected volume. In most operations, that means pulling last week's delivery data, cross-referencing carrier rate cards and SLAs, and manually adjusting zone assignments. The system doesn't model tradeoffs between cost and service for each scenario. The planner does that in their head, or in a spreadsheet, using a combination of experience, gut feel, and formulas. If volume spikes unexpectedly or a carrier underperforms, the weekly plan breaks, and dispatchers spend the rest of the week compensating in real time for a plan that didn't work.
Results from The 2026 Last-Mile Performance Outlook found that the following mission-critical operations are still largely manual:
- 48% of billing and invoice reconciliation
- 42% of carrier management
- 39% of exception handling
These aren't back-office curiosities. They're the workflows where operational cost compounds and where the people closest to end customers spend time on tasks that don't improve customer experience.
The market isn’t looking for revolutionary AI. It’s looking for proof that AI delivers measurable last-mile performance gains.
The AI blind spot
Almost three out of four executives (68%) plan to make additional investments in routing and planning AI despite already being the most-adopted workflows. The investments follow visibility: routing produces dashboards and surfaces in operational reviews, so it’s easy to point to when leadership asks what AI is doing.
The workflows at the bottom of the investment list don't produce dashboards. Billing reconciliation, carrier management, and exception handling happen in the background, but that's where operational cost compounds. A billing discrepancy that goes unreconciled leaks margin on every carrier invoice. A carrier allocation built on last week's data and gut feel sets the cost floor for the entire week before a single route runs. An exception that goes unresolved generates a support call and a loyalty risk, which show up in the P&L but never in a routing dashboard.
Yet, only about 14% of enterprise executives plan to increase investment in billing reconciliation and carrier management. These are the workflows most directly connected to the metrics where performance is weakest. Cost per delivery sits at only 36% overachievement, the lowest figure in the dataset and the metric executives rank first in severity. Operational efficiency trails by a similar margin. More routing AI doesn't touch either one. It improves on-time delivery, which is already the strongest metric in the dataset at 63% overachievement, and produces diminishing returns on a problem already largely solved.
The investment mismatch creates a competitive gap most organizations don't recognize. The weakest metrics sit in the decisions routing doesn't reach. That's exactly where AI investment runs thinnest. Every point of underperformance on cost per delivery and operational efficiency traces to decisions that remain largely manual.
Three questions last-mile AI should answer
The underserved decisions don't need more dashboards, they need a different kind of help; AI that can advise, act, and explain. Three questions separate AI that earns its place from AI that merely occupies it.
"What should we do?"
Some decisions involve genuine tradeoffs that persist beyond the moment; for example:
- Add capacity next week and costs rise, but on-time rates improve
- Hold headcount and the budget stays flat, but late deliveries increase and the customer service team absorbs the impact
- Shift volume between carriers and cost per delivery changes, but service levels shift unpredictably across zones.
These are the recurrent and structural decisions where a planner or operations lead needs to see projected outcomes before committing. AI that answers, “what should we do?”, serves as an advisor: it simulates scenarios, projects the cost-to-service tradeoff of each option, and explains its logic so the human can weigh factors the data doesn't capture. The planner still decides. The decision gets better because the planner saw three options with projected outcomes instead of building one plan in a spreadsheet and hoping it holds.
That addresses the decisions that require judgment. But not every task consuming a planner's time requires judgment at all.
"Are there more valuable uses of this person's time?"
The highest-manual-rate workflows show exactly where people spend time on repetitive tasks that don't improve customer experiences. A planner manually reconciling carrier invoices isn't evaluating whether next week's capacity matches projected demand. A dispatcher manually adjusting delivery communications for each exception type isn't managing the exceptions that actually require judgment, the ones where a customer relationship depends on what happens next.
AI that answers, "Are there more valuable uses of this person's time?", takes the routine off the plate: automated invoice matching against contracted rates, context-aware communication that adjusts based on delay type and customer history, carrier performance tracking that flags SLA drift before it compounds. Not replacing the human. Redirecting them toward the decisions where their judgment, experience, and relationship knowledge produce outcomes AI simply can't.
The first two questions look forward. The third looks back.
"Why did that happen?"
Every operation generates data, yet few generate explanations. Thursday routes ran 12% below efficiency targets. The dashboard shows the number but doesn't say whether the cause was traffic, driver behavior, warehouse departure delays, or a pattern building across weeks. AI that answers, “Why did this happen?”, works as an analyst for the people accountable for performance. It identifies root causes across disparate data, distinguishes signal from noise, and delivers explanations that inform the next decision.
The proof bar is changing
Over half (53%) of executives expect AI to deliver major performance gains. Only 9% expect it to be truly transformative. The market isn't looking for revolutionary AI. It's looking for proof that AI delivers measurable last-mile performance gains.
AI investments to-date aren’t wrong, they’re incomplete. Routing AI solved the most visible problem first. The less visible problems—recurrent and structural decisions that determine cost, capacity, and whether the operation can offer consumers what they actually want—are next.
The companies that close the gap will be the ones that point AI at the decisions that meaningfully reduce costs and improve front-door experiences, not just the ones who spend the most.
The final post in this series defines the specific metrics last-mile AI should improve, what the proof bar looks like, and how to tell whether AI actually earns its place or just occupies it.
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.
