Artificial intelligence may provide greater return on investment
PwC opines that to have a successful sustainable tourism sector, there must be protected and enhanced opportunity for the future with actionable intelligence through big data and analytics.
Dan Krittman, PwC Advisory Principal, warns that “despite the historical promises of Artificial Intelligence and Big Data that have often been accompanied by expensive and confusing hype, things are different this time around and now is not the time for the tourism industry to get complacent about finding ways to utilise the tremendous volumes of data in their possession.”
“The travel and hospitality segments pioneered some of the earliest use cases of big data and sophisticated analytics with segmentation and guest loyalty programs. Since that time progressive organisations have invested time and resources in tools and techniques to address some of the more mainstream uses of Big Data such as demand prediction and pricing optimization, decision support, scheduling, and new product development just to name a few.”
Krittman suggests that “while mainstream applications of Big Data analysis are just as important today as they were yesterday, less obvious applications of Big Data analysis and techniques may prove even more impactful in protecting the sustainability of an organization and its stakeholders. Generating revenue is critical, but protecting that revenue is just as important. Combatting nefarious behaviors, detecting the theft of loyalty/reward points, ensuring the supply chain, and preventing employee/customer/agent fraud and corruption are critical to the longevity of any entity.”
He did point out that there would be challenges. “Attracting experienced data science professionals already in short supply will be no easy task. Additionally, some of the big data usage challenges that the tourism industry faces come from its long-term usage of legacy information systems for a variety of processes. One of the consequences of this is that key data is often fragmented across multiple functions and units.”
However Krittman argues that “while there may be some challenges, tools, techniques, and service providers are available to facilitate the transition to, or expansion of, Big Data applications and AI. By way of example, Advanced Natural Language Generation (“NLG”), technology that transforms structured data into insightful language, is emerging as a practical application of AI, which can be used across the enterprise to drive operational efficiency, scale expertise, and accelerate decision-making. Akin to a top-tier analyst who interprets and communicates what is most interesting and important in the data, Advanced NLG is already being used to analyze and articulate data-driven information at incredible scale.”