Overview: According to the September 2026 issue of the Lufthansa Group Policy Brief, SEER, an AI application jointly developed by Lufthansa and Fraport, the operator of Frankfurt Airport, is being implemented on the apron. It automatically identifies key ground handling processes through video footage and provides real-time corrections. Meanwhile, LHIND, Lufthansa's IT service provider, released its first-half AI and data analysis trend report on February 3, providing a reference for technology selection. Lufthansa's AI-based maintenance solution was also nominated for the 2026 "AI Impact Award.
According to a policy brief, the turnaround of an aircraft involves a series of interconnected ground support processes – from passenger boarding bridge connection to baggage handling, refueling, and catering. Any delay in one of these processes will have a cascading effect. SEER, jointly developed by Lufthansa and Fraport, uses video analysis to identify key support steps and timestamp them, generating a real-time panoramic view of the apron operations. This helps stakeholders quickly identify delays and take corrective action. As a result, ground support becomes more predictable, and on-time flight performance improves.
This approach of combining “video footage with timestamps” essentially transforms previously fragmented progress information, held separately by various operational units, into shared, real-time data. While passengers may not directly perceive the change, it provides crucial input for the operations control center, enabling better decision-making regarding delay predictions, gate assignments, and crew scheduling – all of which are based on this type of data.
According to LHIND’s (Lufthansa Industry Solutions, located in Norderstedt, Germany) official website on February 3, the company released its AI and Data Analytics Trend Radar for the first half of 2026, designed to help businesses categorize technological developments and make strategic choices. An Dang, LHIND’s AI and Data Analytics Consultant, explained, “2026 is a turning point: AI is moving out of the isolated experimentation phase and into a phase where it can be deployed for production. At the same time, automation and governance are becoming decisive competitive factors.” She also suggested that companies need to invest in areas such as agentic architectures and orchestrated machine learning pipelines.
When viewed within the context of the aviation industry, this statement carries a very specific meaning: in recent years, most airlines' AI projects have been limited to isolated experiments. Starting in 2026, the competition will focus on which airline can scale these experiments into full-fledged engineering projects, and integrate them with data governance and process automation. This "technology radar" serves as an internal tool to facilitate this "selection and prioritization" process.
According to Porsche Consulting, Lufthansa has been nominated in the production and supply chain category for the 2026 “AI Impact Award” for its AI-driven aircraft maintenance solution. The award recognizes solutions that make production, logistics, or supply chain processes more efficient, safer, or more sustainable. In an interview, Philipp Krusemeyer, Senior Director of Digital TechOps at Lufthansa, discussed the operational and regulatory challenges faced by the team, the origins of the AI solution, and the tangible results achieved so far. Maintenance is a relatively inflexible component of an airline’s cost structure, and using AI to optimize scheduling and working hours directly translates into quantifiable financial returns.
The value of the Lufthansa model lies in its parallel implementation of “two approaches”: SEER addresses coordination and on-time performance on the apron, while Trend Radar focuses on decision-making and governance in technology selection – one practical, one strategic. Another detail worth noting is that, according to Future Travel Experience, partner Fraport is also promoting the AI@Fraport program at Frankfurt Airport, using its intelligent scheduling agent (IDA) to optimize transfer coordination. The fact that the airport and airline are jointly developing AI tools for the same scenario indicates that the sharing and integration of apron data may be the real bottleneck in improving transfer efficiency; even the most advanced unilateral deployment will still be hampered by delays in cross-organizational processes, negating its benefits.