skiff take
— FLYR
This year is the ‘Year of AI’, artificial intelligence (AI) is everywhere, and the number of products and solutions for the travel industry is increasing. But AI is not monolithic. Despite all the promise that comes with the concept, management and other decision makers are not sure what to deploy, how it will affect current systems and processes, and ultimately how it will affect the bottom line. It’s not always easy to understand what helps.
By examining how AI works, its various practical applications, and how AI scales data ingestion and analysis exponentially, travel companies will see where their AI roadmap points in 2023. It will give you a better understanding of what to do. I will go on that journey.
Focus on practical applications
There is news about ChatGPT, DALL-E and so-called generative AI programs that can create new and unique outputs based on specific prompts given to them. An exciting space that is deeply related to the travel industry, but whose generative uses are still in the early stages of development. Today, it’s important for executives to understand that AI comes in many forms.
“[Generative AI] Kartik Yellepeddi, Vice President of ML and AI Strategy at FLYR Labs, said: “You can’t expect to suddenly spawn new pricing strategies…not yet.”
In the travel industry, the “supervised” use of AI is much more controlled than the news-popular generative applications, Yellepeddi said.
So how does a supervised learning model work? Take airline revenue management as an example, how did an AI model contribute to the ultimate goal of maximizing revenue for a particular action? label the historical results of pricing as ‘good’ or ‘bad’ based on The AI can then evaluate new variables and suggest price changes consistent with those “good” decisions. With thousands of daily inputs and repetitions of this action, it trains itself to do more of the good and less of the bad, becoming smarter over time. .
“AI requires a lot of data, but it has the advantage of being highly scalable,” says Yellepeddi. “Technology depends on how you design it, and in theory it can learn a given task. It can recommend actions to take to maximize. ”
To better understand this, consider optimizing the price of a particular flight. This flight is typically released 300 days before departure. Every day, thousands of variables affect potential flight prices and final results, including new bookings, changes in search volumes, competitor sales, and price changes. AI can analyze this ever-changing context in ways that humans alone cannot. This provides price analysts with detailed information not previously available.
How ancillaries are offered, when to overbook, how to price cargo space, and how to deploy marketing funds are other ways airlines and travel agencies are using AI models to improve decision-making.
“Airline pricing and forecasting is a common use case, and we realized that the same machine learning techniques could be applied to many other important commercial functions,” said Yellepeddi.
These types of pragmatic everyday uses allow companies to dip their toes in the water and deploy AI capabilities while operating in relatively low-risk scenarios.
Seek a scalable solution
As travel companies look to capitalize on AI opportunities long-term across their organizations, they must be prepared to invest time and technology to change the way they operate.
Revenue management systems, for example, have historically been built on a fixed growth scenario and look broadly at year-over-year changes.
“Historical revenue management systems did one thing: price a flight, but now there are 10 to 15 transactions with the same customer during the same trip, from accessories to other offers. We are doing it,” said Yellepeddi.
With the rate of change greatly accelerating in today’s travel environment, AI can be an asset as it is much more dynamic and reactive than humans. Cloud technologies give businesses more flexibility in data storage, analytics, and applications, but legacy systems with fixed servers were not built to scale in this way. Fixed servers have fixed costs and capacity, so businesses cannot allow AI to use all available data at will. Because you have to make upfront decisions about how much information you can reasonably manage. Effectively, this hinders our ability to scale and maximize the execution of advanced AI models.
“The cloud has really changed things,” says Yellepeddi. “Most importantly, we can use all the data in the decision-making process.”
trust in technology
One of the most important considerations for executives when using AI is the need to give up some control and trust the technology.
If there are thousands of data points related to pricing generated daily, an analyst can reasonably examine hundreds of them. It’s AI’s job not only to see all these data points, but also to flag data that needs human attention for meaningful results. Building a deeper level of trust improves an analyst’s ability to use information to optimize recommendations.
The strength of AI is not its ability to always be right, but its ability to react quickly to situations, continuously explore and exploit market opportunities, and learn from mistakes on a larger scale than humans.
From that perspective, an important recent evolution in AI has improved explainability. In other words, it is now possible to “show that work”. AI models can now not only spew out decisions, but also provide information about how those decisions were reached.
“If the ‘good’ decision is ultimately more important than the ‘bad’ decision, how it generates revenue is less important, as long as the decision-making process can be made visible where necessary,” Yellepeddi said. says Mr. “Building that trust is critical to driving adoption of advanced AI technologies.”
The impact of AI on productivity, its ability to exponentially scale data analysis and decision-making, and its ability to learn on the job, show its work, and escalate to humans when necessary, will drive automation, automation, and innovation across the travel industry. Drive efficiency, profitability. Revenue management and marketing for freight, maintenance, etc. By taking advantage of today’s practical business opportunities, executives will also be better prepared to understand and integrate the latest AI applications coming online in the near future.
For more information on FLYR and its commercial intelligence and optimization platform powered by AI and deep learning, please visit www.flyrlabs.com.
This content was co-created by FLYR and SkiftX, the branded content studio for Skift.
