Your best dispatcher is an undocumented system.
They know which delivery provider goes soft in the rain, which one says 20 minutes and means 40, which one you can hand a Sunday to and which one you can’t, and none of that exists anywhere except in their head.
It’s the most valuable thing in your stack, and you have no backup for it.
Which is fine until volume shows up. Four hundred orders in a day, and the person who knows everything becomes a bottleneck, working one order at a time while the other 399 wait their turn to get thought about.
So you go looking for a platform, and the question gets muddy, because everyone sells automated dispatching and almost nobody means the same thing by it. The best last-mile platform for automated dispatching is the one where an AI agent makes the assignment itself and then keeps watching that order until it’s delivered. Assignment on its own is just a faster button.
Agentic delivery AI is software that decides and acts on its own across the delivery lifecycle, and dispatch is the obvious first job for it: a coordination problem that repeats a few hundred times a day, never gets easier, and has never once been improved by adding another dashboard.
Gartner has 40% of enterprise apps carrying task-specific agents by the end of 2026, up from under 5% the year before, which tells you roughly how fast the rest of your software is going to stop waiting for instructions.
How Agentic Delivery AI Runs a Single Order
Take an order that needs a car instead of a van and has to land inside a two-hour window.
Your dispatcher reads it, remembers the vehicle problem, prices it across a couple of tabs, scrolls for whoever’s free, and picks someone they trust. Call it 90 seconds, and their pick is probably better than what the software would have chosen, because they know things about that provider that haven’t made it into any data set yet.
An agent reads the same order, then does what a person can’t: checks live capacity across your drivers and every provider at once, weighing reliability, price, proximity, and vehicle fit, before the second tab loads.
What matters comes after. Your dispatcher assigns and moves on, because 300 more are waiting. The agent doesn’t. It watches for the pickup that never happened, the driver off route, the ETA sliding past the window.
An agent is a worse dispatcher than your best person and a better shift than anyone works. That loop, with nobody in the middle, is automated dispatch, and how AI runs last-mile delivery operations.
Why Rules-Based Dispatch Runs Out of Room
The sensible response is to get that knowledge into the system, which is why every operation past a certain size has a hundred rules. ZIP 21208 goes to provider B. Over 10 miles go to the fleet. Rules are cheap, auditable, and better than nothing.
What they can’t do is reprice when a provider’s surge lands at five, or notice the driver assigned eight minutes ago went dark. Nothing gets learned. A rule holds one variable forever. Your dispatcher holds six and gets tired. A peak day holds hundreds of thousands of small calls.
Agentic delivery AI doesn’t outthink your dispatcher. It just never runs out.
Last mile now runs 53% of total shipping cost, up from 41% in 2018, and Supply Chain Management Review’s 2026 read has AI moving from optimization tool to operational backbone.
Past a few hundred a month, the replans became the job.
Preventing Failures Beats Logging Them
Go back to that assignment your dispatcher made and stopped watching. Somewhere in the next two hours, it stalls. Nobody finds out, because the system they work in reports what’s already happened, which is too late to be worth much.
That lag is the expensive part, and it’s the half that vendors never demo. Where-is-my-order tickets are 20% to 40% of ecommerce support volume and past 50% at peak, at $5 to $25 apiece, and a failed first attempt reruns the whole cost twice over.
Most operations book that as reshipping and stop counting. The number that moves everything else is how long it sat before anyone noticed. A stall caught early costs a phone call; caught late, it costs the order, the refund, and a customer who might not be back until next Mother’s Day.
Exception recovery is where agentic delivery AI earns its line item.
Dispatching Owned Drivers and the Network in One Decision
A second leak runs the whole time, and it’s structural. Your vans sit at 60% while you pay an outside provider for a zone your driver is already parked in, because the fleet and the delivery providers live in two systems that don’t know each other exist. In grocery, on a Saturday, that’s the same money every week.
One engine has to see both at once, or agentic delivery AI automates the easy half and leaves you the expensive half. Take the cheapest viable option regardless of whose driver it is, fall back automatically when the first pick can’t perform, and the fleet becomes capacity in the same pool as everybody else’s.
Which is why signing another provider fixes nothing. It gives your dispatcher one more tab. eMarketer frames 2026 as a build-versus-partner call, and most land on both. They still have to resolve to one assignment, in one place.
How to Judge a Platform’s Automated Dispatching
By now, that’s 400 assignment calls, 100 stale rules underneath them, an order that stalled while everyone was busy, and two systems that don’t talk. Before your dispatcher runs it all again next season, four things are worth making a vendor demonstrate rather than describe.
- Full-Loop Ownership: The agent assigns, supervises, and recovers without a person steering it, which is the difference between an operator and an interface. If your team spots the trouble first, you bought a dashboard.
- One Decision, Both Fleets: Your drivers and your provider network should resolve into one assignment rather than two systems reconciled later. Ask to watch an in-house van and an outside provider compete for the same order.
- Failures Caught In Flight: Exceptions have to surface while they’re still fixable, not in a month-end report about money you already spent. Ask what happens at minute twelve of a stalled pickup, and whether anyone has to notice first.
- Provider Neutrality: The pick should follow the best option for that order, not a parent company’s book of business. It gets asked about the least and decides the most, because it’s what keeps your brand on the tracking page.
Deloitte has 30% of retailers on AI for supply chain visibility, headed to 41%, and the AI-enabled last-mile market tracks toward $1.8 billion. None of that obligates you to move. It just means whoever does won’t be running this out of browser tabs.
Where Automated Dispatching Goes From Here
Sixty-eight percent of retail executives in that same Deloitte survey expect agentic AI across core operations within two years, and dispatch is a reasonable place to start, because the decision repeats all day. You’ll know inside a week whether it worked.
We’ll say the part most vendors skip. None of this replaces the person who knows which provider goes soft in the rain. Burq built Pulse AI to take the tabs off them and to stay awake for the stalls they never see, so the judgment you’re truly paying for gets spent on the 10 orders that need it instead of the 390 that don’t.
Its Dispatch Agent runs on the Burq platform. It selects the provider or driver, applies item requirements, batches orders, and builds routes in real time across your in-house fleet and hundreds of delivery providers in the U.S. and Canada. Then it keeps watching. Teams running it see up to 90% less dispatch planning time, 40% to 60% fewer where-is-my-order inquiries, and 60% to 80% of potential failures prevented through proactive rerouting, at a 99%+ delivery success rate.
Come see it work firsthand and book a demo with Burq.
