It’s been a difficult summer for the airline industry. Following on from the enforced groundings and lost revenue caused by Covid-19, international air travel in Europe has suffered something of a media crisis over the last few months.
Tabloids and broadsheets widely reported of “travel mayhem” throughout the summer in the face of flight delays and cancellations. And, instead of the myriad of factors that created a perfect storm of disruption – including changing consumer behaviour, staff shortages and the impact of Brexit – airlines typically fell firmly into the crosshairs of the media.
But was this criticism fair and does the media coverage paint an accurate picture of the situation? The reality is that 1-2% of flights get cancelled in any normal summer season. This year, that figure rose to around 3-4%. It seems that two percentage points is the threshold at which business as usual flips to industry crisis.
The challenge is that the logistical complexity involved in running an airline efficiently has never been higher. That’s why it’s important to take a step back and consider the role technologies such as AI can play in solving – or at least mitigating – similar crises in the future.
During any unprecedented period of disruption, airlines must make some extremely difficult decisions. These typically revolve around issues such as which flights to cancel, which cabin crews to divert to different flights, how to compensate or reimburse customers, and where to invest to minimise future disruption.
No one human – or team of humans – can make these decisions. The sheer volume of information that must be taken into account is incredibly vast. This includes predicted volumes of travellers, the number of no-shows for any given flight, fuel costs and availability, baggage handler capacity, runway capacity, road and rail travel disruption, the weather, pilot and crew availability – even in-flight food supplies. The list is nearly endless, and the models for running aviation that worked five years ago simply don’t work in a post-Covid and post-Brexit era.
Simple statistical models that can only focus on one or two areas of manipulation at a time aren’t fit for purpose. Airlines need a more sophisticated model that can bring everything together and analyse the impact of specific scenarios across the whole system. This is where AI comes into play. AI can help the decision-making process for airlines around areas such as demand management and flight resourcing. AI tools can resource flights far more effectively than any human team, primarily because they can analyse data at scale and turn this data into actionable, real-time insights.
This allows airlines to make smarter decisions based on tangible data analytics – thereby ensuring that they have planes in the right place, with the right crew, at the right time. It also makes it easier to balance the commercial impact of flight cancellations – considering factors such as alternative flight options, the cost of paying for overnight stays, airport slots etc – against the disruption to customers
But the real value comes from being able to go one step further and get ahead of disruptions through AI-powered predictive modelling. This enables airlines to track everything that’s happening across an incredibly complex system and predict the impact of events in order to react as close to real time – or even ahead of real time – as possible. Instead of constantly being on the back foot, they’ll be empowered to proactively adapt.
Providing a top-notch user experience for customers is also important in times of crisis. This is what will help build and maintain loyalty even when things aren’t going as planned. Travellers are at the core of any airline’s business, so travellers must also be the focus of online strategies.
Again, AI has a vital role to play. For example, airlines can use AI systems to proactively communicate with customers about any potential delays or cancellations, while at the same time analysing flight data to offer alterative flights. This would ensure that customers aren’t left in the dark, while at the same time freeing up contact centre agents to focus on more complicated customer interactions.
AI can also be used to gain deeper insights into customer behaviours, needs and preferences. For example, the Lufthansa Group used advanced data models and AI-powered analytics to meet the changing needs of its B2B customers. By diving deeper into behavioural patterns and customer segmentation, Lufthansa was able to deliver a new loyalty program that can be tailored to specific customers and includes automated self-service capabilities.
Ultimately, there are now so many external factors and moving parts in aviation that airlines need to rethink their approach. They must recognise the role that AI can play in helping them remain flexible in today’s volatile environment and ensure ongoing profitability. Amidst such complexity, AI is the key to unlocking more effective decision-making in aviation and reaching new heights over the years to come.
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