In this guest article, Jan Willem Kappes, CCO of Assaia, discusses how AI is transforming baggage handling operations.
If you’ve travelled by air even a handful of times, you’ll know that receiving your bag in the terminal is a crucial part of the journey. I was reminded of this recently while waiting at a baggage carousel at a major airport hub. Next to me was a family who had just arrived from a long international flight and were now trying to make separate domestic connections. While most of them received their luggage relatively quickly, one family member was still waiting for their bag. They were (trying to be) calm, but appeared stressed, shifting from foot to foot, checking both the carousel and their watch every few seconds.
I could overhear the conversation starting to take on a different tone: should they leave now and let them wait alone? Should someone take the bag later and courier it to them? Would they even make the check-in desk in time if the bag arrived in the next few minutes? Happily, there were shouts across the hall as the suitcase finally appeared, and the family disappeared into a near sprint to catch their connecting flight.
It was a reminder that baggage handling is never just about luggage. For passengers, it is often the final impression of a journey, and when something goes wrong, the effects can quickly ripple far beyond the baggage hall. Missed connections, disrupted plans, and unexpected costs can leave passengers with a lasting negative impression of an airline or airport. In an industry where customer loyalty is hard won, these moments matter.
Baggage handling is central to the turnaround
From a passenger perspective, the expectation is simple: step off the aircraft, make your way through the terminal and find your luggage waiting at the carousel. But behind that expectation is an intense, time-sensitive operation that takes place while the aircraft is being prepared for its next departure – known as the aircraft turnaround. While passengers are making their way through arrivals, ramp teams are unloading bags and cargo, cleaners are preparing the cabin, catering and fuel are being replenished, and boarding preparations are already beginning for the next flight.
Within this tightly coordinated sequence of activities on the ground, baggage handling plays a particularly influential role because it is one of the first processes to begin once an aircraft arrives at the stand. The unloading of passenger baggage immediately sets multiple processes in motion, and very often, the pace of that process sets the tone for everything that follows. If baggage offload starts late, it can create a bottleneck that pushes back subsequent tasks. As the turnaround progresses, these delays become harder to isolate as processes overlap and begin to influence one another.
While the industry has made major progress in recent years, disruption still affects millions of travellers annually. According to SITA’s 2025 Baggage IT Insights report, 33.4 million bags were mishandled globally in 2024, including delayed, damaged and lost luggage, with delayed bags accounting for 74% of incidents. The report also estimates that mishandled baggage costs the airline industry around US$5 billion every year, highlighting just how operationally and commercially significant baggage performance has become.
Delivering bags faster starts with better visibility
Many airports aim to deliver the first bag within 20 to 30 minutes of arrival, but achieving this consistently remains difficult. In many cases, the issue is not a lack of effort from individual teams, but rather a lack of shared visibility across the turnaround operation, where multiple stakeholders are working on different tasks at the same time and under intense time pressure.
Berlin Brandenburg Airport (BER) tackled this challenge head-on. Faced with an ever-increasing complexity of sharing real-time operational information between airlines, ground handlers and airport teams, BER introduced Assaia’s AI-powered software solution, ApronAI, across 49 stands.
Using AI and camera technology to monitor and analyse apron activity in real time, the system helps everyone work from the same live operational picture, something that becomes especially important when timing is tight and small delays start to build.
The results are measurable. BER reduced its average ground delay per flight by 2.4 minutes and cut overall ground delays by 69%. At the same time, baggage delivery efficiency improved significantly, increasing from 80% to 95%, with more than 98% of first bags now delivered within 30 minutes as reported by the airport.
How was this achieved? Alongside a series of operational process improvements introduced by BER, the use of computer vision and real-time operational data played an important role in delivering these results. Assaia’s AI-powered technology continuously monitors baggage loading and unloading, alongside the other activities happening around the aircraft, and automatically flags delays against timestamps. Ground teams and Berlin’s Resilience team can track progress and identify delays as they happen, enabling them to respond before small disruptions cascade.
The future of baggage handling will be built on predictability
Passenger expectations around the flying experience, including how their baggage is handled, are changing quickly. Today, people are used to tracking almost everything in real time, from food deliveries and taxis to online purchases arriving at their front door. Airports and airlines have responded by introducing self-service check-in, automated bag drop and increasingly personalised passenger communication throughout the journey. But when it comes to baggage delivery, the experience is still far less connected. Increasingly, passengers will want the same level of predictability and communication around when and where their bag will arrive.
Meeting these expectations will depend on more predictive airport operations. Traditionally, turnaround management has relied heavily on static target times and manual updates, but AI systems are helping airports and airlines forecast key milestones across the turnaround, including the completion of baggage unloading. These predictive insights can feed directly into Airport Collaborative Decision-Making (A-CDM) systems – the shared operational platforms used by airports, airlines, ground handlers and air traffic control to coordinate aircraft turnaround activities and keep flights running on schedule – and help teams react earlier and communicate better with passengers.
