Imagine navigating your daily commute using an app like Google Maps or Waze. These tools constantly analyse real-time traffic, accidents, and road closures to guide you on the fastest route from point A to point B. Now, picture an intermodal container terminal facing a similar challenge: optimising the movement and storage of thousands of containers. Just as your navigation app uses Artificial Intelligence (AI), an AI-powered Terminal Operating System (TOS) can choose the best container storage location, optimise yard space and ensure smooth operations across the terminal.
Artificial Intelligence (AI) has been making headlines for years—hailed as a revolutionary force by some, misunderstood as a distant or even dangerous concept by others. But while the hype around AI continues, something more grounded is happening in the world of logistics: AI is quietly becoming a practical, everyday tool. It’s not science fiction—it’s already here, improving decision-making, boosting efficiency, and simplifying complexity in intermodal terminal operations.
In fact, studies have shown that AI can reduce total costs by up to 55% and improve train capacity usage by more than 20%. These numbers aren’t just impressive—they’re vital for an industry under pressure to stay competitive. This article takes a closer look at how AI transforms terminal operations, clears up common misconceptions, and explores where its real value lies in daily logistics work.
How AI optimises operations
Think about how supermarkets manage their inventory: AI forecasts demand and predicts when supplies need to be replenished. This helps avoid overstock and empty shelves and ensures that goods are available where and when they’re needed. The same logic applies to terminal operations: Instead of products, we deal with containers, vehicles, and equipment. AI-based decision intelligence helps predict movements, assign resources dynamically, and make better use of space and time.
Another great example of AI in your everyday life is streaming services, such as Netflix or Spotify. Their AI suggests content based on your preferences and viewing history. Similarly, AI in intermodal terminals can make predictive decisions, determining the optimal storage location for swift retrieval or the best train slot for efficient routing. Instead of manually sifting through endless options, AI provides intelligent recommendations.
To fully grasp how AI functions, it is helpful to take a step back and look at a concrete definition. At its core, AI simulates human intelligence, enabling an AI-powered system to perform tasks that traditionally require human judgment. Looking at intermodal container terminal operations, this means that AI algorithms are provided with vast amounts of structured input such as terminal layout, domain-specific knowledge like recurring peak times on certain weekdays, transport schedules or yard space availability. The advantage lies in the speed at which this information can be processed, which no human could ever match. The outcome is an optimised decision for container placements, routing strategies, job combinations and double-cycling or other processes within the terminal.
Of course, the quality of the result depends directly on the quality and completeness of the data provided or on the possibility of cleaning up the raw data and engineering additional data stemming from it. Machine Learning (ML) adds another layer to this process. While often being seen as one phenomenon, ML is actually a subset of AI that uses self-learning algorithms. Instead of relying only on predefined algorithms for decision-making, the system learns from historical data, previous optimisation runs and additional external data sources, such as time of day, weather conditions, or fuel prices. Over time, it identifies recurring patterns and generates new operational knowledge. This knowledge can be reintegrated into the optimisation process, enhancing decision-making further and making the system more adaptive to changing conditions.
A TOS for intermodal needs
INFORM has a long history of applying AI to logistical challenges. Their journey began with operations research and extended to transportation, airports, and, ultimately, the container terminal industry. One of their early AI-driven projects in this industry was with HHLA Container Terminal Burchardkai (CTB), a highly automated terminal, in the early 2000s. This project demonstrated the importance of AI-powered optimisation software for efficient terminal operations.
Building on this experience, INFORM developed a modular and scalable intermodal TOS, recognising the unique needs of intermodal facilities compared to maritime terminals. This TOS is designed for different terminal sizes and automation levels and is suitable for brownfield (upgrading existing sites) and greenfield (newly built) projects. A key advantage is its integration of AI for optimisation across various terminal functions, including crane movements, yard space allocation, and multimodal transport coordination.
Real-world implementations showcase the impact of the company’s AI-powered TOS. At KTL, a brownfield implementation integrated AI-driven optimisation without disrupting ongoing operations, improving turnaround times and reliability through data-driven decision-making. The greenfield deployment at DGT, a major European inland port, established a digital-first approach from the outset, using AI to anticipate congestion, balance workloads, and enhance coordination across different transport modes.
Benefits of AI in intermodal TOS
The integration of AI into an intermodal TOS offers a multitude of benefits across various operational areas. These include:
- Booking module for the creation of transport jobs as well as conflict management, supporting the integration with external booking for consistent information flow
- Accurate billing via automated tracking of all terminal handling
- Simplified gate handling with automated handover or pickup position delegation for truck divers and integration with OCR systems
- Efficient and forward-looking rail control for managing train schedules, as well as loading and unloading, by considering available equipment
- Intelligent crane control to minimise empty travel and optimise handovers and to manage workloads between equipment
- Adaptive yard coordination for optimised allocation of yard positions considering various factors like dwell times and outbound transport proximity
- Dynamic vehicle management for efficient creation and management of internal job orders
- Optimised barge handling for managing incoming and outgoing ship voyages as well as vessel schedules
Practical Implementation of AI: A People-Centric Shift
Successfully bringing AI into intermodal terminal operations isn’t just a matter of deploying new technology—it’s about evolving how terminals work. AI offers clear advantages over traditional manual processes by enabling faster, data-driven decisions and automating routine tasks. This is crucial as terminals face growing volumes and operational complexity. But rather than replacing people, AI shifts the workforce’s focus. Human expertise remains central, with staff taking on more supervisory, analytical, or decision-support roles. This shift requires reskilling and upskilling—think operations planners learning to interpret AI recommendations or gate clerks adapting to smarter scheduling tools.
For AI to truly take root, standardised interfaces and seamless integration into existing systems are essential, but equally important is investing in terminal staff training. INFORM supports this transition by delivering explainable, user-friendly AI solutions while guiding terminals through process adaptation and workforce enablement. Clear, explainable outputs help build trust and understanding, while user-friendly design ensures AI tools complement rather than disrupt workflows. When implemented with both technology and people in mind, AI becomes a powerful tool for sustainable, long-term efficiency.
The future is now
Waiting for others to lead the way is no longer a viable strategy. Digitalisation and automation are already reshaping terminal operations, and those who delay risk falling behind in an increasingly competitive environment. Now is the time to invest in smarter systems, not once the majority has already moved ahead.
However, change doesn’t have to be disruptive. Transformation works best when all stakeholders are involved early and supported throughout the process. With modular solutions, even smaller terminals can take manageable first steps and scale gradually as their needs evolve. This makes scalable optimisation achievable without the need for a complete system overhaul.