United States: AI In Travel, Hospitality & Leisure Part 1: How Travel Businesses Can Prepare To Leverage AI

It is now accepted wisdom that artificial intelligence (AI) can deliver dramatic gains for an organization's bottom line. Gartner's 2022 CEO Survey ranked AI as the "most impactful new technology" for the third year in a row. Analysis from the McKinsey Global Institute reports that the incremental value opportunity afforded by AI is greatest in the travel industry compared to all other major global business sectors.

Almost every business across travel – including hotels, airlines, casinos, cruises, car rentals and more – is leveraging AI or machine learning (ML) to a degree for dynamic pricing, chatbots, finance process automation or other use cases. However, very few travel companies have reaped the returns seen in tech-heavy industries or suggested by research and surveys.

How can examples of AI transformation in travel be so ubiquitous while tangible value is so rare?

Individual initiatives executed in silos won't deliver the foundation to scale and launch the next 10, 20 or 100 plus initiatives that will actually drive "transformational" value. That requires enterprise thinking, a well-articulated strategy, reliable data to support it, and above all, the skill and will of the people who will operate within this powerful new paradigm.

Here are four actions travel companies can do now to prepare their organizations for transformational use of these tools and technologies:

• See the forest and the trees. Most AI products require the same fundamental data assets: customer profiles, stay or trip data, and so on. While launching individual efforts in silos may allow for speed, it can also be shortsighted. When organizations step back and develop enterprise strategies that tie AI to business goals with defined value targets, they can prioritize the foundational assets and functional models in a way that supports quick wins and scale, speed and maximized value for the long haul.
• Fund data like a mission-critical system. Building out a customer profile or similar data asset is usually funded as part of a short-term project tied to a specific use case. The business case is narrow, and the expected horizon for ROI is short. Instead, businesses should treat these data assets more like traditional core applications including a property management system (PMS), passenger service system (PSS) or central reservation system (CRS)—like capital assets with long horizons for both funding and ROI expectations. Integrated, governed and shared data assets unlock the transformative value of AI, but development is resource-intensive and achieving ROI takes time. If the business case is tied to a single use case, it will rarely justify the expense.
• Treat data management as a business discipline. The predictions, recommendations and actions prescribed by AI only make sense if its data inputs are representative of business reality. General managers, desk agents and corporate employees, alike, must be accountable for correctly entering data into the applications that feed AI models. Furthermore, poor application design causes bad source data just as frequently as user error. When designing applications, engineers should give data capture as much focus (and funding) as user experience.
• Think like the experience company you are rather than the tech company you are not. AI is not a technology project. The value of AI is the insights embedded into business processes that enable businesses to deliver the best experiences more profitably to passengers and guests. Keep the internal or external customer at the center of design. What problem will the model solve for employees or customers? Do outputs make sense, and will end users actually trust or take advantage of them? The most sophisticated AI product drives exactly zero value if nobody uses it. Effective AI begins with the end user in mind and is targeted on solving real problems.

The promise of AI isn't incremental value at the edges. It is a competitive differentiator that can be seen in rising stock prices and in discussions by CEOs on investor calls. It is equally exciting and transformative. Adopt this mindset from Day One, and not only will initial use cases be successful, but enthusiasm for the transformative power of AI and everyone's role in driving that change will spread across the enterprise.

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