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Air travel demand is soaring, but economic pressures are also on the rise – so airlines are competing to deliver market-leading passenger experiences. This year, generative AI has become a pivotal factor in how airlines are racing to increase their market share. Generative AI offers endless potential for airlines to deliver innovations that delight passengers and gain their loyalty.
However, despite the exciting possibilities of generative AI, airlines cannot afford to overlook the importance of their core resiliency. Disastrous consequences befall airlines with technology that cannot weather unexpected events, as proven in recent headline examples where airlines have fallen into chaos after tech outages. In every case, part of the root cause is outdated legacy systems that cannot handle the strain of disruption.
Airlines need to improve their operational efficiency across the areas that passengers do not see - like maintenance, flight planning, and crew management. It is these areas – below-the-wings activities - that shield passengers from disruptive events. These areas also heavily impact airlines’ sustainability efforts and direct operating costs.
Generative AI is not just here to improve passenger experiences – it can improve the core airline operations that keep flights running. Yet many airlines have a long runway of core transformation ahead before they can take off with this transformative tech. What are the major operational areas that can be improved with generative AI, and what foundations do airlines need to have in place before they can unleash the full potential of generative AI?
Reducing Direct Operating Costs
Within airlines, major revenue losses occur in hidden places. Aircraft operations and finance teams are usually stretched across complex manual process management – leaving no time to identify cost-sink inefficiencies. Just one example is within air cargo shipments. Ideally, each individual shipment should have an optimised cost management process. However, the scale, complexity and variability of air cargo makes this incredibly difficult. So, in many cases, excess spending across air cargo shipments can fly under the radar.
Data analytics in the hands of finance teams can provide the solution to these hidden problems. With inefficiencies spotlighted in real-time dashboards, airlines can proactively resolve revenue leakages. Data science capabilities - intelligent alert systems, real-time capital optimization and predictive cost management – forecast potential revenue risks before they affect the bottom line. In our earlier air cargo shipment example, revenue leakage is solved with analytics that show the discrepancy between forecasted and actual costs, so the finance team can avoid future overspending.
Taking it a step further, generative AI has the potential to improve airline cost management in novel ways. Alongside data science capabilities that connect disparate data to spot patterns and trends, generative AI can give airline teams real-time, actionable insights on how to reduce costs quickly. In today’s volatile aviation market, this is a game-changing prospect.
Optimizing Flight Prioritization
Airlines are moving toward net-zero ambitions by adopting sustainable aviation fuel and taking more efficient routes. However, real-time disruptions - anything from a storm to a strike - easily send flight plans into disarray. Flight delays damage sustainability efforts by wasting fuel and resources, but they also spike operational costs and ruin customer experiences. That is why real-time flight prioritization technology is critical for all airlines.
Flight prioritization depends on many factors – passenger demographics, connection timing, routes, and many additional data points. Airlines must extract this data from each source and transform it into insights for flight prioritization. Not only that: this needs to happen in real-time. Machine learning shines here – algorithms can cluster and rank this complex data into clear priority “scores”, enabling rapid prioritization. With a resilient flight prioritization platform that can handle unexpected disruptions, airlines can make accurate decisions quickly to ensure flight plans stay on track.
The use cases for generative AI in flight prioritization are still emerging - from rapidly diagnosing flight delay causes to assisting passengers with connections in real time. Airlines should focus on the use cases that reduce operating costs and improve sustainability margins. However, this is only possible for airlines that have a modern data platform that can power these rapid insights.
Improving Crew Management
As air travel demand grows, many airlines are hiring more crew members, and are seeking ways to help their crews operate more efficiently. Poor crew management has far-reaching consequences – risking everything from flight schedules to passenger safety.
High-performing crews need access to decision-grade insights. They need platforms that are resilient under disruption, user-friendly for in-the-moment decision-making, and connected to real-time data for accuracy. To enable this, airlines must build data platforms that can take information from global operations in real-time and transform it into actionable information.
A prime example is in crew rostering - a constant operational challenge for airlines. Rostering is driven by many data points such as working hours, duty limitations and rest periods. A single inaccuracy in how this data is used can muddle schedules enough to cause flight delays, crew fatigue and even legal implications for the airline. Despite this, airlines often resort to manual processes and siloed technology to manage schedules – opening the door to human error.
Solid crew scheduling depends on technology that can take huge data volumes and create adaptable schedules. The ideal solution is a central crew “portal” that connects back-end data from across the airline to give crews the insights they need (flying hours, training information, roster changes et cetera) in a user-friendly platform. Generative AI can improve on this by optimizing crew schedules with real-time factors like crew workloads, flight times, and even individual crew member preferences.
By improving crew management technology, airlines can transform their end-to-end flight operations for improved passenger experiences – faster service, efficient flights, and personalized touches. Better crew technology also leads to happier crews, and in a market where air travel demand is rising but resources are scarce, crew retention is vital.
Enhancing Predictive Maintenance
Maintenance, Repair, and Overhaul (MRO) operations are often complicated by uncontrollable factors. Right now, the global backlog of aircraft maintenance services is causing flight delays regularly – which leaves airlines with substantial operational costs, sustainability problems, and frustrated passengers.
Predictive maintenance is the ideal solution. With the power of predictive analytics, airlines can deliver advanced prognostics to forecast issues across areas like aircraft fatigue, defect propagation, and overall health.
Generative AI can take predictive maintenance to the next level. By going beyond rule-based analytics, it can improve problem forecasting to help airlines resolve maintenance issues earlier. It can even directly support maintenance teams to improve their overall efficiency with capabilities like personalized guidance and automated task management.
However, accurate predictions rely on accurate data, so airlines first need to build robust data platforms from which predictive insights can be generated. This must integrate data in real-time from many sources, from aircraft sensors to crew reports. Airline operational data often has anomalies, so it is essential to process and transform the data before it is used for interpretation.
With a robust data platform as its foundation, predictive maintenance can drastically reduce airline maintenance overheads.
Agility before AI
Airlines must keep flights moving throughout any event – and today, disruptions are constant. Therefore, before investing in technology modernization, airlines must adopt agile practices so they can respond quickly to change. For many airlines, this is not an easy cultural shift – agile transformation needs full buy-in from the entire company. Nonetheless, being agile is the only way that airlines can rapidly modernize their core platforms. From here, the runway is clear for airlines to adopt technology like generative AI to reach their operational, passenger experience, and sustainability goals ahead of schedule.
“Digitalization that supports differentiation doesn’t only involve customer interactions, technological solutions, or coordination. It increases the speed with which new products can be brought to market and improves service. It’s about total operational and innovative agility, where everyone and everything within the organization is involved integrally and coherently.” - Tijmen de Groen Director Network Planning and Revenue Management, Transavia Airlines.
Read the case study: Transforming Company Culture Allows Transavia Airlines to ‘Take-off’
About Xebia
Xebia is a pioneering global software engineering and IT consultancy. We’re helping airlines like Air France KLM, Delta, and Emirates create digital change that elevates them to deliver operational efficiency, better passenger experiences, and improved sustainability.
Learn more about our work with the world’s leading airlines.