Rohit Laila is a seasoned veteran in the world of logistics, possessing a career that spans decades of fundamental shifts in how the world moves goods and people. As a visionary who has witnessed the industry transition from manual ledgers to cloud-integrated supply chains, he has become a leading voice on the intersection of physical infrastructure and intelligent technology. In a period where global trade is being fundamentally restructured, his insights into the synergy between 5G, IoT, and Artificial Intelligence provide a roadmap for organizations looking to thrive in an increasingly digital economy. He views the current landscape not merely as an upgrade of tools, but as a total evolution of industrial logic, where data serves as the lifeblood of every port, railway, and highway.
The following discussion explores the strategic frameworks necessary for successful digital transformation, moving beyond the superficial adoption of technology to focus on high-value business scenarios. We delve into the “Four-in-One” philosophy—integrating scenarios, computing power, algorithms, and data—and examine real-world applications where AI is already delivering measurable gains in safety and efficiency. From reducing manual documentation errors in customs to achieving surgical precision in railway fault detection, this conversation highlights how the industry is building a high-quality, sustainable digital economic system.
Many organizations tend to prioritize the acquisition of new technology over a cohesive long-term strategy when adopting AI. How can leadership effectively restructure their systems to ensure technology serves the strategy, rather than the other way around?
Digital transformation is fundamentally about using new ways of thinking to restructure entire systems and reengineer processes from the ground up. Leadership must move away from the “technical mindset” and embrace a “strategic mindset” that views AI as a tool for sustainable, high-quality growth rather than a quick fix. By embedding digitalization into a long-term strategy, transport operators can ensure that every investment helps integrate physical and digital activities into a unified platform. We are seeing hub nodes like ports and airports evolve from single-purpose transit points into complex integrated platforms, such as port-industry-city complexes or station-city integrations. This level of structural change requires a vision that looks beyond the hardware and focuses on how information flows across different modes of transport to reduce overall costs and improve the service experience.
You have often described data as the “bedrock” of AI in the transportation sector. Why is this distinction so critical, and what steps are necessary to move from raw data to a data-driven decision-making culture?
Quite simply, without high-quality data, AI cannot exist in any meaningful capacity; it is the fuel that powers the entire engine of intelligence. In the transportation industry, we need comprehensive connections and real-time feedback loops that make our physical infrastructure and daily operations both visible and measurable. To reach a truly data-driven state, organizations must consolidate multimodal inputs—such as radar, GPS, and sensor data—into unified data lakes where they can be curated for trustworthy deployment. This ensures that the datasets we are using are specific to the unique challenges of transportation, allowing AI to deliver reliable and repeatable value at scale. When management decisions are backed by these standardized and secure data streams, the industry can move from reactive troubleshooting to proactive, predictive optimization that saves both time and resources.
Could you elaborate on the “Four-in-One” philosophy and explain why starting with high-value scenarios is the most effective way to drive AI into production systems?
The “Four-in-One” approach is a philosophy we developed to ensure that AI isn’t just a peripheral experiment but a core part of the production system, integrating scenarios, computing power, algorithms, and data. We always advocate for starting with high-value scenarios—those massive, repetitive, and complex operations that have a direct impact on the bottom line, such as port scheduling or urban congestion governance. By focusing on specific points like ship berthing, equipment maintenance, or signal control, we can prove the value of AI in a controlled yet critical environment before expanding it to broader systems. This problem-oriented approach ensures that we are solving real-world headaches, like reducing the manual workload for level-1 maintenance or automating signal transitions, which then multiplies the benefits across the entire network. When you solve a high-stakes problem at a single intersection or terminal, you create a blueprint that can be scaled globally, turning isolated successes into a comprehensive digital foundation.
Computing power is often invisible to the end-user, but you argue it underpins everything in modern logistics. How does a robust AI infrastructure impact mission-critical operations like port dispatching or road inspections?
Computing power is the silent engine that ensures real-time, mission-critical operations never falter, which is essential when you’re dealing with the sheer scale of modern ports and highways. A unified, secure, and reliable AI infrastructure allows us to issue critical alerts, such as ship detention warnings, within a mere 3 seconds, a feat that would be impossible with fragmented or legacy systems. In the context of road pavement inspections, high-performance computing allows for zero-interruption monitoring, meaning the infrastructure is being assessed and maintained without ever needing to halt the flow of traffic. This level of resilience is non-negotiable because our transportation networks are the arteries of global trade; any latency or failure has a massive ripple effect. By providing a dedicated AI development platform and toolchains, we ensure that the architecture is not only fast enough for today’s demands but also flexible enough to adapt to the rapid technological shifts we expect to see over the next few years.
In what ways are we seeing algorithms bridge the gap between traditional engineering knowledge and the predictive capabilities of modern AI?
The real magic happens when we fuse decades of traditional engineering expertise with the predictive power of AI to solve specific transport challenges like traffic signal optimization or railway fault detection. We are leveraging domain-specific models enhanced with AI agents that can coordinate decision-making across intersections, vehicles, and even vast stretches of railway track. For example, an algorithm doesn’t just see a “data point” on a rail; it understands the engineering tolerances of that specific vehicle type and can predict a failure before it occurs. This fusion allows us to turn human expertise into scalable, automated practices that can handle thousands of variables simultaneously. By using these specialized models, the industry can transition from simple perception—knowing something is happening—to execution, where the system actually assists in the decision-making and coordination of complex logistics flows.
Looking at specific sectors, what tangible impacts are these AI models having on the efficiency and safety of highways and ports right now?
The impacts are quite staggering when you look at the numbers, particularly in how traditional large-scale infrastructure is evolving toward intelligent operation and maintenance. On our highways, large-scale models trained on full-scenario datasets are now achieving more than 95% accuracy in predicting traffic flow, speed, and congestion, which is a game-changer for corridor economies. In the port sector, we’ve seen a 15% increase in small-sample accuracy and a massive 80% reduction in the workload required for data labeling, which allows for much faster deployment. These models are designed to be reused across different terminals, meaning a breakthrough in one port can be quickly adapted to another, making the entire global shipping network safer and more reliable. It’s no longer about just moving containers; it’s about using AI to create a seamless, highly integrated ecosystem where every ship and crane is synchronized for maximum throughput.
Safety is paramount in railways and aviation. How is AI specifically being used to detect faults and refine flight operations to prevent accidents and delays?
In the railway sector, we are using the TFDS, or Trouble of Moving Freight Car Detection System, which utilizes a large-scale railway model to detect over 430 types of faults across 70 vehicle types. This system operates with over 99.3% accuracy, ensuring that critical issues are identified without a single missed report, while simultaneously increasing image review efficiency by 200%. For airports, the surge in passenger and cargo traffic has necessitated the automation of 26 manual data collection nodes, which now operate with 98% accuracy and a time error of less than 30 seconds. This has led to a remarkable 80% drop in the abnormal rate of operations, meaning daily aircraft handling issues have plummeted from ten cases down to just two. By refining these operations through AI, we are not just improving efficiency; we are providing a digital foundation that ensures passenger and cargo safety remains the highest priority in an increasingly busy world.
Logistics has traditionally been a paper-heavy industry. How is AI tackling the persistent challenge of manual documentation and the inefficiencies of customs processing?
Documentation has been a critical pain point for decades because customs forms, packing lists, and shipping documents come in so many different formats, languages, and often include messy handwriting or stamps. We have implemented advanced AI-driven recognition technology that has pushed document recognition accuracy above 90%, which is a monumental shift for the industry. This has allowed us to reduce manual data entry by 90% and cut down processing times from hours or days to just a few minutes per document. This leap in efficiency has a direct, positive impact on supply chain fluidity, reducing the time goods sit in warehouses or at borders. It simplifies the customer service experience and allows logistics companies to focus their human talent on high-level problem solving rather than the tedious task of transcribing paperwork.
You’ve mentioned that talent is the “core” of this transformation. Why is it so essential to have cross-domain professionals who understand both transport operations and digital technology?
Technology is only as good as the people who design, implement, and refine it, which is why cross-domain talent is our most valuable asset. We need individuals who can speak the language of a port engineer and a data scientist simultaneously, bridging the gap between traditional business operations and cutting-edge digital tools. These professionals are the ones who ensure that a new AI algorithm actually makes sense in the context of a busy railway station or a congested highway corridor. Without this human core, we risk building sophisticated systems that don’t align with the practical realities of the field. Continuous innovation is driven by people who understand the “why” behind the technology, ensuring that every digital shift is actually solving a human or operational need.
What is your forecast for the future of global supply chains as these intelligent infrastructures continue to scale?
I believe we are entering an era of “diversified, highly integrated” transportation where the boundaries between different modes of transit will virtually disappear. My forecast is that within the next few years, we will see a complete realization of “one-order” and “one-container” intermodal transport, where AI seamlessly manages the transition between rail, water, road, and air. We are already seeing this scale with intelligent technologies powering over 100 ports, 200,000 kilometers of highway, and 210 airports globally, and that footprint will only grow. The ultimate goal is a world of convenient travel and smooth logistics where AI creates a resilient, green, and efficient foundation for global trade. We are no longer just dreaming of a digital future; we are actively building the arteries of a smarter, more connected civilization.
