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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 applications in last-mile delivery. And it's valuable. Route optimization improves on-time rates, reduces windshield time, and tightens delivery windows. But when an entire market concentrates AI investment in one function, the question changes from, “Does routing AI work?” To, “What are we not fixing if we’re all fixing the same thing?” To answer that, start with the decisions themselves. Routing AI is a real-time decision that inherits every structural and recurrent choice 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. If volume spikes unexpectedly or a carrier underperforms, the weekly plan breaks, and dispatchers spend the rest of the week compensating as a result. The report found that some 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 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. An unreconciled billing discrepancy 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. 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 in this role acts as an advisor: it simulates scenarios, projects the cost-to-service tradeoff of each option, and explains its reasoning so the human can weigh factors the data doesn't capture. The planner still decides, on;y with better reasoning because the decision came with options instead of a single spreadsheet plan built on assumption. 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 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. 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. The goal is redirect—not replace—people toward the decisions where their judgment, experience, and relationship knowledge produce outcomes AI 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’s 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 built across weeks. AI that answers, “Why did this happen?”, and 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 according to the 2026 Last-Mile Performance Outlook. 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. 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. About Bringg Global retailers and logistics providers reduce costs and deliver differentiated customer experiences with Bringg Last-Mile Solutions. Through Bringg’s modular technology platform, integrated fleet network, and services suite, leading retailers automate processes, optimize order delivery, and invent new business models. Unlock flexibility at scale. Any order. Any fleet. Delivered. www.bringg.com