With a career spanning decades at the heart of the logistics industry, Rohit Laila has seen firsthand how a single unexpected event can send shockwaves through the entire supply chain. From his work with giants like Amazon and The Home Depot to his current focus on pioneering new technologies, he has dedicated himself to solving the industry’s most complex and costly problem: the “middle mile.” We sat down with him to discuss how a new kind of artificial intelligence could transform our freight networks from fragile and reactive to resilient and predictive, learning to anticipate chaos rather than just endure it. The conversation explored the cascading nature of delays, the intricate challenges of the middle mile, and a future where intelligent AI agents work together to keep goods moving seamlessly, no matter what disruptions lie ahead.
Current freight logistics often involve separate, sequential planning for routing, dock scheduling, and sortation. Could you walk us through a real-world example of how a single delay cascades through this system and what the typical manual response entails?
Absolutely. It’s a scene I’ve witnessed more times than I can count. Imagine a truck carrying high-priority goods is scheduled to arrive at a major sorting hub at 2 p.m. But an unexpected snowstorm hits a hundred miles out, delaying it by two hours. In our current system, that’s not just one problem; it’s the start of a domino effect. The dock that was reserved for that truck is now sitting empty for two hours, an expensive waste of space, before becoming double-booked when the late truck finally arrives. The packages on that truck miss their scheduled sortation window, which means they won’t make it onto the outbound trucks for their next leg. Suddenly, everything downstream is thrown into chaos. The response is a frantic, manual scramble. A logistics coordinator gets an alert, then spends the next 30 to 60 minutes on the phone, juggling spreadsheets, and manually re-booking docks and rerouting other drivers to try and patch the hole. By the time they have a new plan, the damage is already done.
You’ve identified the “middle mile” as the most complex part of the supply chain, where significant costs and failures occur. What specific consolidation challenges make this segment so difficult, and where do today’s largely reactive management tools fall short?
The middle mile is the chaotic heart of the entire network. It’s not just about moving a box from point A to point B; it’s about orchestrating a massive convergence. Picture hundreds of trucks, each carrying packages from different origins, all descending on a single fulfillment center at the same time. All that cargo has to be unloaded, sorted, and then re-consolidated onto a different set of trucks heading to hundreds of unique destinations. It’s an incredibly complex dance of timing, space, and resources. This is where the costs really get tied up and where things most often break. The tools we use today are fundamentally reactive. They’re good at telling you that a problem has occurred—that a dock is overbooked or a truck is late—but by then, the cascade has already begun. They don’t have the foresight to see the pieces moving on the chessboard and predict the checkmate that’s three moves away. We’re constantly playing catch-up in a game that demands we be one step ahead.
Your solution involves agentic AI, with smaller agents that reason locally but coordinate globally. Can you describe how these agents would “talk” to each other to solve a problem, and what different roles would agents responsible for routing versus warehouse resources play?
The beauty of this approach is that you break an impossibly large problem into manageable pieces. Right now, we have these powerful, dedicated models for routing, for scheduling, and so on, but the critical issue is they don’t talk to each other without a person acting as a translator. We want to enable them to communicate directly. So, a routing agent monitoring real-time traffic and weather data would notice that truck is hitting a major slowdown. Instead of just flagging it for a human, it sends a message directly to the warehouse agent: “Expect a 90-minute delay on this inbound load.” The warehouse resource agent, responsible for dock assignments and labor, immediately processes this. It might “talk” back, saying, “Understood. I’m reassigning that truck to a different dock that opens up later and shifting the unloading crew’s break to align with the new arrival time.” It’s a seamless, real-time negotiation between specialized AIs, each an expert in its domain, all coordinating to find the best global solution without that slow, manual handoff.
The goal is to move from reacting to disruptions to proactively preventing them. Using the scenario of an approaching snowstorm, can you detail the step-by-step process your three-tier AI framework would use to anticipate and mitigate the impact on the network?
This is where the system truly becomes intelligent. In our three-tier framework, the bottom layer is all about sensing the world. It’s constantly ingesting data streams—weather forecasts, traffic patterns, even reports on equipment health at a facility. It sees a major snowstorm is predicted for a specific region in 12 hours. This information is passed up to the middle layer, the planning agents. These agents don’t just see “snow”; they understand the implications. They use machine learning and optimization models to predict which routes will be slowed, which hubs will be impacted by labor shortages if people can’t get to work, and how many shipments will likely be delayed. Instead of waiting for the first truck to get stuck, the system proactively starts rerouting trucks around the storm’s path in anticipation of the event. It might pre-emptively shift volume to a different hub outside the storm’s reach. It’s the difference between bracing for a punch and gracefully stepping out of the way before it’s even thrown.
Your framework keeps humans in control at the top layer to oversee decisions and validate outcomes. What specific metrics or thresholds would the system use to determine when a problem is high-stakes enough to require human intervention, rather than being handled automatically?
Human oversight is non-negotiable. The AI is a powerful tool for optimization, but we must remain in the driver’s seat for critical decisions. The system would be designed with clear thresholds for escalation. For example, if a proposed solution to a disruption is projected to cost above a certain dollar amount—say, from needing to charter expensive air freight—it would automatically flag a human manager for approval. Another trigger would be the potential impact on a key customer or a high-priority shipment. If rerouting a set of trucks to avoid a storm means a critical delivery to a hospital will be late, the system won’t make that call on its own. It will present the options, the trade-offs, and its recommended solution to a person who can make the final, context-aware decision. The goal is to automate the thousands of small, everyday adjustments so that human experts can focus their attention on the truly high-stakes choices where their judgment is most valuable.
Your work with major companies like Amazon and The Home Depot must have provided key insights. Could you share an anecdote from that industry experience that crystallized the need for a more adaptive, intelligent approach to middle-mile logistics?
I remember one peak season where a single, seemingly minor equipment failure at a major sorting facility created absolute havoc. A key conveyor belt went down for just a few hours overnight. By the morning, there was a backlog of trailers waiting to be unloaded that stretched for miles. The manual response was heroic—people were working incredibly hard—but they were blind to the downstream effects. They were trying to solve the problem inside the four walls of the warehouse, but they couldn’t see that hundreds of other trucks, already in transit, were heading straight into this bottleneck. We spent the next 48 hours in a state of reactive chaos, rerouting shipments on the fly and paying exorbitant fees for team drivers and air freight to try and recover. That experience was a stark reminder that the problem wasn’t a lack of effort; it was a lack of foresight. The system itself was blind. We need a network with reflexes, one that can sense a problem and start adapting across the entire system instantly, not just in the one place where the fire started.
What is your forecast for the freight industry?
I believe we’re on the cusp of a profound transformation, moving from an industry built on rigid plans and manual reactions to one defined by intelligent, autonomous adaptation. Within the next decade, I forecast that agentic AI systems will become the standard operating system for any large-scale logistics network. The conversation will shift from “How do we recover from disruptions?” to “How did our network proactively adapt to that event before it even became a disruption?” This won’t eliminate the need for skilled logistics professionals; it will elevate them. Their roles will evolve from being firefighters who react to problems to being strategists who oversee and fine-tune these incredibly smart, self-healing systems. The result will be a supply chain that is not only vastly more efficient and cost-effective but also fundamentally more resilient, capable of absorbing the shocks of an increasingly unpredictable world.
