"The challenge with machine learning is the huge amount of data needed to enable the machine to learn the patterns of the individual or subsegment," Phocuswright concludes. "Even when a vast amount of data is available, the infrequency of leisure travellers and the differing personas of the business traveller may inhibit the effective use of machine learning for personalisation."
That's a dilemma some airlines face when attempting to utilise AI and machine learning. Viva Aerobus CEO Juan Carlos Zuazua recently told attendees at the CAPA - Centre for Aviation Americas Aviation Summit that the airline was capturing every bit of passenger information, but unfortunately it is only using approximately 5% of that information.
During the past couple of years the airline has spent a lot of time on AI as part of its ancillary revenue strategy "to be able to know more about our customers". That study of AI is likely to continue for Viva Aerobus where approximately 45% of its revenue is derived from non-ticket products.
Further analysis from Statista shows that machine learning projects took home the most funding in 2019, receiving more investment than all other artificial intelligence systems combined. Between both machine learning apps and platforms, over USD42 billion went to the development of those automating systems. All other projects that use artificial intelligence, including advancing smart robots, virtual assistants, and natural language processing, got about USD38 billion by comparison.
While funders are getting behind more machine learning ideas, some of the biggest players in the space are putting resources behind not just advancing these systems technologically but also ethically. Google and IBM are among the industry behemoths leading the way. As artificial intelligence grows, grossing more funding dollars, the responsible development of these technologies is imperative for their proper use in the future.
