In the race toward automation, most cargo leaders talk about replacing labour with machines. At Jettainer, the narrative is different. For CEO Dr Jan-Wilhelm Breithaupt, the real value of digitalisation and AI lies not in removing people from the equation, but in giving them sharper tools and clearer insight. The goal is smarter container management, driven by data, grounded in operations, and designed to bridge the gap between technology and the warehouse floor.

Since taking over as CEO in June 2024, Dr Jan-Wilhelm Breithaupt has accelerated Jettainer’s digital transition. “We massively pushing forward the digitalisation of Jettainer,” he said. “Our data warehouse is completely renewed, which is the basis for reporting and managing data. We recently introduced the new Jettainer NG platform in March and have already enhanced it.”
Jettainer’s latest platform upgrade adds the ability to drill down to the sub-location level. This is an important step, Breithaupt explained, in turning ULD tracking into a tactical tool rather than a passive feature. “We are constantly seeking further enhanced technologies beyond BLE. I expect that in autumn, we can report on that.”

The promise of smart container tracking lies in scale. “You can reduce the number of lost units by one percentage point on the total fleet,” he said. “That is significant.” Yet the real gains emerge in overall efficiency. “When we take over a fleet, we can achieve around 15 to 20 percent reduction in fleet size. In terms of usage, flight legs per month, we’re about 25 to 30 percent more efficient.”
Technology plays a role, but so does Jettainer’s human capital. “It’s a combination between very experienced ULD controllers, our well-developed NG platform, and the transparency from messages out of the airlines handling system enhanced with tracking technology.”

AI: Still a human puzzle

Artificial intelligence is starting to reshape container management, though Breithaupt is clear about its current limitations. With the support of AI, repetitive tasks in the management of ULD flows can certainly ease the workload of controllers. Additionally, as more data and interfaces are generated, there is further potential to make efficiency-enhancing deductions. “But the main thing is to improve the data we receive from different sources, then to interpret that data and increase transparency. For example, this gives us a better understanding of which repair shops the units are in and how long they stay there.”

Predictive maintenance, however, remains a work in progress. “There are a lot of influencing factors which cause damage, depending on how the ground handling agent treats the units,” he said. “It’s not like an aircraft engine where damage accumulates in a predictable way. With ULDs, a forklift driver can crash into a brand new unit out of nowhere.”

AI can model trends but not specifics. “You can predict damage on the scale of large numbers, but you can’t say anything definitive about a single container,” he explained. “The interpretation of what the AI tells us is relatively complex. We believe this will be a field where we’ll see significant advantages in the next 12 to 18 months. But currently, it’s not yet fully integrated into our systems.”
Automation where It fits

Breithaupt is sceptical of applying classic robots to air cargo handling (similar to those in the automotive industry) in the near term. “The shape of cargo is extremely different from one piece to another, barrels, large machinery, small items. If you work for an integrator, handling boxes of the same size and weight, robots make sense. In air cargo, it’s another story. On the other side I believe, that Automated Guided Vehicles (AGVs) and Autonomous Mobile Robots (AMR) will have a significant impact in the future of Air Cargo handling.

Automation will mainly evolve behind the scenes. “There will be a lot of digitisation in the background: e.g. administrative functions, data sharing, stock management” he said. “Handling the freight, though, remains mainly physical for at least the next ten years.”
Jettainer is already considering how roles can evolve alongside automation. Breithaupt envisions a more integrated presence at customer sites.

“Certain ULD management functions will be substituted by AI. But we are planning to shift some of our staff from back office to the shop floor of our customers,” he said. “They will help optimise ground handling processes and manage ULDs in real time.”
This is not downsizing. It is repositioning. “We expect these colleagues to have lean management experience and guide our customers through small workshops to improve efficiency,” he said. “It’s a transition over the years. Some staff will retire or shift roles. What remains will be a group of better-trained, more customer-focused professionals.”

Accountability at ground level

One of Jettainer’s biggest challenges lies in ground handling quality, particularly in the Americas.
“In some parts of the world, e.g. Americas, ULDs are treated relatively badly,” Breithaupt said. “Some ground handlers cut rubber doors with knives to get freight out faster. This leads to expensive repairs and removes containers from circulation for days.”

“In Europe and Japan, the damage rates are much lower. In the Americas, it’s sometimes even accepted to handle containers directly on the floor, which increases the damage rates significantly.”

This drives up costs and fleet size requirements for customers. “Any damaged container is waste in the system,” he said. “We want to give customers maximum transparency to negotiate better terms with their GHA or apply penalties.”
Training and accountability are central. “Rule number one: ULDs should never be stored or handled directly on the floor. Always use a rollerbed or slave pallet. If a hard steel fork crashes into alloy, it will cause damage—sometimes to the container, sometimes to the freight.”

Despite Jettainer’s training offers and guidelines, high turnover at some GHAs undermines continuity. “At a certain point, the ground handling agent must be capable of continuing to train their staff to ULD-related topics on their own,” he said. “When cutting becomes normal practice, the root problem is not at the floor level. It is in operations leadership.”

AI and the repair dilemma

To tackle inconsistent serviceability assessments, Jettainer is experimenting with AI in real-world contexts. “We were the first ULD management provider to send a team to a hackathon and also to sponsor an ULD challenge at a Hackathon,” Breithaupt said. “At the IATA One Record Hackathon, we challenged teams to develop an AI-based app to support ground handlers in assessing container damage.”

The winning app uses an AI-language model and image recognition to guide users through proper checks. “It retrieves the assessment instruction for a specific container type, prompts the user to take a photo of the damage, and uses image recognition to determine whether it’s severe enough to declare non-serviceable.”