Rohit Laila has spent decades navigating the intricate layers of global supply chains, building a career at the intersection of traditional logistics and cutting-edge technology. As an expert in supply chain automation, he has witnessed the transition from manual ledger tracking to the complex, AI-driven ecosystems that define modern freight. Today, he focuses on the critical “accuracy gap” in spatial reasoning that prevents artificial intelligence from fully taking the wheel in transportation. This conversation explores why general AI often fails in the physical world and how the industry is building more grounded, reliable systems to bridge the gap between back-office planning and real-world execution.
General AI models often struggle with basic spatial reasoning, sometimes hitting an accuracy wall around 55%. From your perspective, why is this specific failure so detrimental to the logistics industry right now?
When you consider that general-purpose large language models only get basic direction questions right about 55% of the time, you realize we are dealing with a coin flip in a high-stakes environment. In logistics, a 45% error rate isn’t just a minor inconvenience; it means a truck getting stuck under a low-clearance bridge or a driver wasting hours searching for parking that doesn’t exist. These models are brilliant at natural language, but they lack a “mental map” of the physical world, leading to dramatic hallucinations when asked to navigate complex freight routes. Without grounding these agents in real-world constraints, we cannot move beyond simple chatbots into truly reliable agentic systems that can handle the rigors of the road.
There’s a lot of talk about how location intelligence needs to be the foundation of logistics AI rather than an afterthought. How do datasets containing hundreds of thousands of truck-specific points change the way these agents perform?
The difference lies in the granularity of the data, such as the 800,000 truck-specific data points that cover everything from tolls and road restrictions to permitted hazardous material routes. If you simply “bolt on” location data later, the AI is essentially trying to fix a broken plan after it’s already been generated. By embedding this intelligence at the core, the AI understands the physical reality of the road—like bridge heights and weight limits—before it even suggests a route. This creates a geospatial model that prevents the AI from hallucinating impossible scenarios, turning a generic language tool into a precision instrument for fleet management.
The disconnect between back-office dispatchers and drivers on the road is a classic logistics pain point. How can agentic AI create a more dynamic orchestration between these two worlds?
We often see dispatchers building what looks like an optimal route on a screen, only for the driver to encounter a closed terminal or unexpected congestion that makes the plan obsolete. Agentic AI allows us to move away from static planning and toward a dynamic, assisted journey where a continuous feedback loop is established between the office and the field. By capturing real-time driver input and routing it back to the planners, the system creates a learning pattern that prevents the same mistakes from occurring twice. This synchronization ensures that when conditions change on the ground, the office and the driver are working from the same intelligence rather than in isolated silos.
You’ve used the term “transportation intelligence” to describe a trade-off engine for logistics. How does this system help operators balance conflicting priorities like cost, safety, and sustainability?
Transportation intelligence is about moving beyond a single point-to-point calculation to a sophisticated engine that optimizes across multiple KPIs simultaneously. An operator might need to prioritize cost on one shipment but focus on strict compliance or carbon footprints on another. Our goal is to provide proactive suggestions where an operator can dial in their specific service-level agreements and see the immediate downstream impact of their choices. For instance, if you decide to relax a distance constraint to save time, the system will instantly show you how that affects the total operational cost and your overall risk profile.
While the technology is advancing rapidly, there seems to be a strong emphasis on keeping human judgment at the wheel. Why is it important to view this as an “assisted journey” rather than a fully automated one?
Despite the leaps in AI capability, human judgment remains indispensable because logistics is often about managing the exceptions that data cannot always predict. If a planner decides to adjust a specific constraint, they need to see the ripple effects on the entire network, but the final decision to implement that change should stay with the human. We aren’t looking at fully automated logistics systems just yet; instead, we are empowering the workforce with better insights to make more informed choices. Keeping a human in control ensures that common sense and ethical considerations are applied to the raw efficiency that the AI provides.
What is your forecast for the evolution of multi-agent collaboration in the logistics sector?
The next major frontier is the move toward multi-agent collaboration where intelligence flows seamlessly across organizational boundaries. We are heading toward an ecosystem where our agents can talk directly to customer systems and third-party agents without the need for deep, custom software integrations on every side. This will effectively eliminate the fragmentation that currently plagues the industry, allowing different workflows to exit their silos and operate as a unified whole. As these agents learn to work together, we will see a significant leap in efficiency that finally matches the speed and complexity of modern global trade.
