While it is difficult for us to envision just what an AI-enabled world might entail, many industries, including aviation, are experimenting with initial capabilities.
It is hard to imagine millennials going anywhere these days without a smartphone and always-on connectivity. Life, business and travel is very different today to the pre-web era of 25 years ago. There is a parallel situation right now with artificial intelligence, which is at the early stages of development. While it is difficult for us to envision just what an AI-enabled world might entail, many industries, including aviation, are experimenting with initial capabilities.
On the radar
A minority of airlines already have AI on their radar, according to the 2016 Airline IT Trends Survey co-sponsored by SITA and Flight Airline Business. Last year, 6% were working on AI pilot programmes, but by 2026 that numbers are forecasted to reach 44%.
Although AI attracts a lot of excitement, it is worth looking beyond the hype and the widespread misunderstanding. “Into the mid-term future, the competitive advantage of bringing AI into your business to some degree or another will lend itself to an advantage over other non-AI-enabled airlines,” says Jonathan Newman, commercial director of Caravelo, which develops AI-powered chatbot technology.
It is not just those airlines pursuing a first-mover advantage that should start thinking about the value of evolving intelligent machine strategies. “I would say that AI and machine learning will have as dramatic an impact on our industry as the internet did,” says Bonny Simi, president of JetBlue Technology Ventures, the Silicon Valley-based tech incubator set up by JetBlue Airways 18 months ago. “Every airline should have somebody go spend the next six months [to] understand the impact of this, because I think it could be dramatic.”
“Every airline should have somebody go spend the next six months [to] understand the impact of this, because I think it could be dramatic.”
The current crop of airline and travel AI initiatives are driven by the requirement to automate interactions and decision-making. There are cost savings and efficiencies to be had, but the prize includes the ability to respond to passengers at an individual customer level, opening up additional revenue opportunities. These initiatives can be broadly categorised into those that seek to embed AI into business and aviation systems – so the capability is part of the technology ecosystem – and those focused on addressing the rapid acceleration of conversations with customers on social media.
Mostly, airlines are either working with specialist vendors or collaborating with academics. JTV has taken a different tack by investing in companies that have the potential to transform the industry. It has five areas of focus, which range from the seamless customer journey to evolving regional transport ecosystems. AI, which is just one of the technologies JTV is looking at, could have applications in all these areas, says Simi.
One of JTV’s first strategic investments was in FLYR, a travel and data science start-up. FLYR leverages machine learning and predictive analytics to create price forecasts and offer customers insurance to lock the fare – if the fare goes up, FLYR pays the difference.
“That capability could totally transform airline revenue management in the future if you could have a much better way of determining demand and pricing in advance,” says Simi.
JTV is investigating the creation of omnichannel communications incorporating chat, messaging, telephone and other channels. As a starting point, the start-up has invested in 30 Seconds To Fly, which is developing an AI-powered virtual travel assistant for small and medium-sized businesses that communicates in natural language. Simi sees the investment as a great way to learn first-hand about the technology. “We invest, we incubate so we bring the technologies in house and learn about them. The reason we invested in 30 Seconds to Fly is to get smart about AI chat as it relates to the travel industry,” she says.
Social communications are increasingly putting a massive strain on customer services, notes Colin Lewis, chief marketing officer at OpenJaw, which, in partnership with Ludex, has developed the t-Social chatbot powered by IBM Watson AI. “Airlines that recognise customers are on social [in preference to] their websites… have the capability to enter the conversation with them,” says Lewis.
KLM embedded the DigitalGenius AI into its customer relationship management system because its customers were expecting immediate answers. “If we want to make sure everybody gets an answer in 1 or 2min we maybe need 1,000 or 2,000 agents; of course, that is not a sustainable business solution,” explains Karlijn Vogel-Meijer, director of social, KLM e-commerce.
The airline attracts about 100,000 mentions per week via social media. Naturally, not every one of these is a question, but during one week alone this June it sent out 36,000 answers, of which 11,500 – or about 31% – were sent with the assistance of DigitalGenius.
KLM has trained the AI on 60,000 “Q&As”, so that it can suggest responses to the agents, who may refine these as necessary, thereby making the system even smarter. “So it is not a Q&A that can only answer 60,000 questions; it can answer a lot more questions, based on the training it has had,” says Vogel-Meijer.
She explains that a conversation between a customer and KLM typically comprises five questions and answers. AI can be useful in answering the initial request. “We see that we can take about two to three minutes off the conversation, meaning the first answer is very fast, because… of AI, and that also speeds up the remainder of the conversation.”
Chatbots are of particular interest to companies with a large customer-facing profile because they provide an opportunity to move from what is effectively a dumb menu of options, in increments, towards a sophisticated communicating machine.
However, Lewis believes AI chatbots and social tools will really take off once you can offer experiences and products personalised to individual customers. Instead of a call with a one-off answer and result, airlines will be able to access personalisation data, removing guesswork about how they engage with customers. “It is the difference in terms of transforming business models, operations and culture,” he says.
Caravelo has launched a chatbot for Volaris and has them for Finnair, Tigerair Australia, Condor Airlines and Ukraine International Airlines going live in short order. Newman predicts the technology’s relationship with the customer will become even more personal over time.
“Ultimately, we want to move the chatbot towards becoming a true assistant for the customer using the airline, not just around providing a booking service, but also assisting them while they are on their journey,” says Newman.
“So if a customer is in Barcelona, as an example, there’s no reason why an airline cannot be an assistant to enable them to get around or to provide them with offers and notifications about things that might be relevant to them. What that leads to is potential retailing – as an example, having your airline bot assistant recommending a restaurant to you, which might lead to taking further revenue for the airline.”
It’s a journey
AI tools are also being developed to optimise economy passengers’ in-flight experience. Black Swan, an applied prediction marketing specialist, is working to improve seating assignments. Chief executive and co-founder Steve King explains that it uses data about the type of flight and passengers’ behaviour – for example, if they want to sleep, or if they frequently pause the in-flight entertainment to get up and down, or if they like to be sociable – to seat them with similar people. This is currently being tested on some beta aircraft. “People don’t know why, but the journey is just a bit nicer than it is normally,” he says.
SITA has a number of trials ongoing to push the boundaries of AI deeper into the aviation ecosystem to optimise operations. It is using AI with face recognition tools and biometrics in a pilot scheme with JetBlue at Boston’s Logan International airport to board passengers without the need to show a boarding pass or a passport. Passengers are photographed in real time and, after connecting with the US Customs and Border Protection agency to verify the image and identity, the system integrates with the airline’s departure control system, all in a matter of seconds.
SITA Lab is also developing specialist autonomous vehicles, last year launching Leo, the baggage-handling robot. In May 2017, SITA unveiled KATE, an intelligent check-in kiosk that will autonomously move to busy or congested areas in the airport as needed.
Another ongoing research initiative is to investigate the synergy of AI and neural networks, which emulate the way the brain functions, to predict delays to airline operations 24-72h in advance. The model is trained with a wide range of airline data – everything from notices to airmen, to weather reports, to air traffic control information – then takes in live data to predict events. SITA Lab chief technology officer Jim Peters expects to have some answers this autumn about the data required to get a good prediction that will also then be actionable.
For Peters, some of the bigger challenges are not around using AI to deliver insight as much as “leveraging insight to a positive outcome”. He cautions that it can involve a fairly extensive change management programme. Airlines will need a multidisciplinary team to make it work – data scientists for the maths and algorithms, people who understand the data and business people who are part of the process. “You have to get together a very diverse team of people who don’t normally all sit down and work together to make it work,” he says.
Elsewhere, some business and aviation solutions providers have already augmented their systems with AI capabilities. The advantage is that airlines can reap the benefit without having to make complicated changes to their existing systems, says Mike Barrera, chief operating and technology officer for Radixx International.
The company has deployed AI components in its passenger service system, which use self-learning probability tools to identify changes in a reservation that have been made by multiple sources. This has boosted performance by over 300% and virtually eliminated errors. “It reduces the human interaction with third parties when reservations do not come through properly and increases customer satisfaction, because you don’t have passengers that have a service they purchased and they did not receive it,” explains Barrera.
Radixx has now turned its sights to utilising AI to enable airlines to provide individual passengers with the right products and services at each touchpoint on their journey from call centre to gate. Barrera predicts that airlines that have an ecosystem to support AI throughout their operations will have a major advantage. “They are going to know their customers and their costs better. Ultimately the cost per customer interaction will be significantly reduced because… a significant percent of the customer interaction should be able to be handled by the AI technology.”
Digital commerce specialist SAP Hybris takes the view that AI and machine learning collectively should be included in the core offering. Its travel customers – airlines, railways, tour operators and others – are already using the Hybris platform to leverage the SAP Leonardo digital innovation engine, offering access to the internet of things, voice and text recognition, and AI.
Matthias Goehler, senior vice-president and head of industries at SAP Hybris, says this inclusive approach enables the creation of real-time models of customer segments at the moment travel companies interact with them. They can also apply predictive analytics to a customer’s profile to forecast behaviour and learn from the interaction. “In the end, not only the offer, but the whole engagement with the customer gets more and more personal”.
Hybris is also testing text and image recognition in social media monitoring, combined with AI, to help improve services and operations. A recent proof-of-concept with a rail customer involved monitoring social posts about train lavatories in poor condition, automatically identifying the affected train and raising a service ticket. Then service agents could decide whether to send someone to the next station stop to fix the problem and potentially respond to the individual who flagged the issue on social media.
In the end, for Geohler, the critical issue for any organisation looking to leverage AI capabilities is having the right mindset. Successful disruptors in the travel space and elsewhere have one attribute in common: they put themselves in the customer’s shoes. “I see a lot of big companies tend to think how they optimise themselves and what they can do better. I believe the only way to survive is the complete opposite. Perhaps if you think [about] how a customer would like to be engaged with, you might come up with a different way of engaging with them that could even be a new business model that could disrupt the industry.”