The U.S. commercial airline system is an economic engine which generated an estimated $168.2 billion in operating revenue in 2016. Ticket fares represented 74.5 percent of operating revenue or $125.2 billion. In 2016, the overall category of transportation represented approximately 2.7 percent of the national GDP.
Airline passenger traffic is projected to double over the next two decades. Today, leading airlines are exploring how AI can help them keep pace with customer demand and improve operational efficacy, speed and customer satisfaction.
To learn how the top four U.S. airlines are using AI, we researched this sector in depth to help answer questions business leaders are asking today, including:
- How are industry leaders like American Airlines and Delta Airlines using AI today?
- What have been the tangible results of these airline AI applications?
- What are the trends across airline AI applications – and how will they impact the industry’s future?
This article aims to present a comprehensive look at how the four leading commercial passenger airlines are using AI. Companies were ranked based on 2016 operation revenue sourced from company financial reports.
Before we begin exploring each company, we’ll present the common patterns that emerged throughout our research in this sector.
Artificial Intelligence in the Airline Sector – Insights Up Front
The most popular AI applications from the top four industry leaders currently using AI appear to be:
- AI Assistants: Responding to customer inquiries and responding to voice commands for domestic airline flight info and ticket availability through interactions using natural language (see American Airlines and United below)
- Smart Logistics: Machine learning algorithms are being applied to data to help automate airline operations. (see Southwest below).
- Facial Recognition: Facial recognition technology is being used to perform customer identity verification and to match passengers to their luggage through kiosks (see Delta below, and you may want to read our full “facial recognition use-cases” article here)
In the full article below, we’ll explore the AI applications of each airline individually. We will begin with American Airlines, the #1 ranking U.S. commercial airline based on 2016 revenue figures.
In 2017, the current leading airline focused its annual app development competition, HackWars, on “artificial intelligence, drones and augmented and virtual reality” technologies. HackWars IV, was a 24-hour hack-a-thon that reportedly brought out over 700 “designers, developers and IT” professionals. Participants worked in teams aiming to come up with an idea for an innovative app that would be beneficial for both “customers and employees.”
The 1st place team, “Team Avatar,” reportedly designed an app that would allow users to determine the size of their luggage in advance of arriving to airport or at a kiosk before proceeding to the gate. The winning team also claimed that their app would allow users to “prepay for any potential expenses” associated with their luggage.
Most likely in an effort to protect the idea, American Airlines did not show a demonstration of how “Team Avatar’s” application would function in the official video. Based on the three categories of interest in the competition, it is possible that the winning app was developed using AI but this is not confirmed. Emerj Founder Dan Faggella states:
“If anything, ‘HackWars’ is a demonstration of AA’s eagerness to innovate (and to let the press know about it), but it’s symbolic of the current nascent stage of AI: Businesses all know they should be applying AI, but are having a hard time finding where and how. If nothing else, AA seems to at least be making the effort, and we hope to see more traction with the firm over the years ahead.”
In May 2017, Delta announced a reported $600,000 investment in four automated self-service bag checking kiosks, including one that will incorporate facial recognition technology. The airline selected Minneapolis-St. Paul International Airport to debut the four self-service kiosks, and claims that facial recognition technology will be used to verify customer identity by matching customer faces to passport photos.
While Delta Airlines doesn’t seem to have their own YouTube video of the new self-service bag check kiosk, WCCO – CBS Minnesota explained the technology well in a video from earlier this year:
Evidence of the airlines interest in integrating more self-service and automation into its operations is evident in its previous initiatives such as “ticketing kiosks and check-in via the Fly Delta Mobile app”.
“We are dependent on technology initiatives to provide customer service and operational effectiveness in order to compete in the current business environment. For example, we have made and continue to make significant investments in delta.com, mobile device applications, check-in kiosks, customer service applications, airport information displays and related initiatives, including security for these initiatives. The performance, reliability and security of the technology are critical to our ability to serve customers.” -2017 Annual Report
Delta claims that previous innovations mentioned above have helped to streamline customer traffic in airports and have also “drastically improved customer satisfaction scores.” However, the airline does not specifically provide any data pertaining to customer feedback in the press release.
(Readers interested in customer service AI applications may want to read about the innovative AI kiosk ideas that we covered in our fast food AI uses cases article.)
The airline shows limited evidence of AI implementation, but there is some evidence of Southwest using machine learning to improve operations. Jeff Hamlet, former Director of Air Operations Assurance at Southwest Airlines has stated that he and his team used machine learning techniques such as time series analysis and pattern recognition to enhance their data mining capabilities.
Time series analysis refers to a method for evaluating a series of data points that are ordered according to time. This type of analysis is often used to identify trends or patterns.
Hamlet claims that these approaches enabled his team to identify potential flight glitches found in pilots’ data reports. These findings were then relayed to air traffic control at the site of arrival. Hamlet concludes that in this reported instance, contributed to the avoidance of an incident.
In September 2017, United Airlines announced a collaboration with Amazon Alexa called “United skill.” The app reportedly allows Alexa users to find answers to the most common questions about United flights by communicating through natural language.
Once users add “United skill” to their existing Alexa app, they are able to ask Alexa common questions about flight statuses, flight times and amenities. Though United skill, examples of commands that Alexa can process include:
- “Alexa, ask United: what is the status of flight 959?”
- “Alexa, ask United: does flight 869 have Wi-Fi?”
- “Alexa, ask United to check me in.”
However, the app has some limitations. For example, commands must be phrased in a very specific way and information on certain features such as airline check-in are restricted to domestic flights.
Based on reviews published on Amazon’s website, so far, United skill has had a mixed reception. Some complaints include incorrect flight times and routine misunderstanding of crucial elements of vocal commands such as “flight number.”
As the pioneering airline to integrate Alexa functionality, it is expected that there will be a learning curve. It will be interesting to see what improvements Amazon will make over time and if the collaboration will ultimately prove mutually beneficial for both companies.
(Readers with a strong interest in Amazon’s conversational interface technology may want to read our full article titled: “Chatbot Comparison – Facebook, Microsoft, Amazon, and Google“.)
Concluding Thoughts on AI in Commercial Airline Sector
AI is being explored in the commercial airline segment of the aviation industry and is being integrated across multiple areas including customer service, airport and flight operations. Airport development will be a particular area of importance according to an annual report published by the International Air Transport Association.
The association anticipates that the cost of airport development, specifically improving and modernizing existing infrastructure and operations, will exceed $1 trillion over the next fifteen years. Therefore, innovation will be a critical building block of these efforts. Specifically, AI and self-service airport kiosks and apps should mesh well into this industry outlook.
In aviation, the transmission and translation of data is fundamental to market competition and safe flying. Jeff Hamlet former Director of Air Operations Assurance at Southwest Airlines and Ashok N. Srivastava, the project manager for the Aviation Safety Program at NASA posit that efficient data management is achieved through the continued creation of new algorithms.
These algorithms or apps would be tailored to the new problems that are being reported by pilots, the FAA, and others involved in aeronautics space. Policies and procedures that affect the transmission of data are of fundamental importance to the future of machine learning in aeronautics.
Thus, we can anticipate that machine learning algorithms will continue to play an important role in how leading airlines translate their data interpretation into valuable outcomes for their companies.
It is also important to consider the economic impact as it pertains to job growth. As a bustling multi-billion dollar industry, it is anticipated that over the next 20 years we will see widespread and lasting growth in the commercial aviation job market. Global economic expansion has contributed to airlines “expanding their fleets and flight schedules” to satisfy growing consumer demand. In 2016, the aviation industry sustained an estimated 67.7 million supply chain jobs and produced $3.0 trillion in global value-added output.
While the leading commercial passenger airlines are relatively early-adopters of AI, industry projections depict a business environment primed for innovation and automation. We will continue to monitor how AI emerges throughout the industry as we anticipate wider implementation in the coming years.
Header image credit: Infosys