What Tourism CEOs Want to Know About AI

By Greg Oates, Director of AI Advocacy, Matador Network

CEOs at destination marketing/management organizations (DMOs) are seeking AI guidance specifically tailored to their chief executive roles.

A CEO in the U.S. told me recently that current AI education is often too surface level, vague, unstructured, or trying to cover too much ground to be useful for a chief executive. He said, “We’ve been talking for a while now about how we need AI education targeting leadership, but it doesn’t really exist.”

While destination leaders face many more questions about AI than answers, one thing is 100% clear: CEOs must own and lead their organizations’ AI integration across all departments. AI isn’t something to outsource or delegate to others, and it’s not a “marketing thing” for the CMO to figure out. 

Conor Grennan, chief AI architect at NYU Stern School of Business, consults with clients on AI strategy from JPMorgan to NASA. I asked him how important it is for CEOs to lead AI education, adoption and integration across their organizations.

“CEOs have to understand how to use AI and why; they really have to have felt the delight about what AI can do,” he said. “They also have to see clearly how AI is a leadership and change management challenge, not a tech challenge. Then they need to start out being vulnerable, and be able to say, ‘Hey, I was skeptical about AI. I didn’t understand it, either. But now I can explain the way I learned AI and how to communicate why it’s important so other people can understand it.”

Why do CEOs need to understand how to use AI? And more importantly why?

As Jeff Bezos stated at The New York Times DealBook Summit last year, “Modern AI is a horizontal enabling layer” that will soon be embedded in every device and digital application in existence.

“These kind of horizontal layers like electricity and compute and now artificial intelligence, they go everywhere,” he said. “I guarantee you there is not a single application that you can think of that is not going to be made better by AI.”

Here’s the clip:

I met with about a dozen CEOs in North America to find out what they want to know about AI specifically from a CEO perspective.

Some leaders are seeking big-picture guidance with a broad overview of high-level strategy. 

“Tell me the five big questions about AI that I need to know, and what people are saying about them,” one CEO told me. “You don’t have to answer those questions but just tell me what I need to focus on. And tell me what people don’t want to talk about. What makes them squirm? Like, if AI is changing how people are searching for destinations, then how much should I stop paying Google?”

Another chief executive said, “We see three areas of opportunity for AI: Administrative tasks and general productivity; marketing chat, SEO and data readiness; and helping customers find information about our city more effectively.… The two most important things I want to know are how to develop AI policy and how is AI changing SEO.”

A year ago, most CEOs had a wait-and-see attitude about AI, following how the technology evolved and what their cohorts in other organizations were doing. Today, there is a general consensus among CEOs that they need to invest in AI, but the question is how and where.

One CEO told me he knows he needs to integrate AI tools and processes into his DMO, but he has three overarching questions that he wants answered first, at least at a high level:

• “How do we create value with AI for the organization, our community and our residents? Everything we do has to benefit our residents.”
• “How do we explain that value to our team, our board, our partners and the community, and what are the KPIs? Those are the basics: How do we explain AI and how do we measure it?”
• “What do we need to develop and adopt in terms of ethics and policy?”

Other CEOs are seeking clarity on the basic fundamentals of AI: “I need to understand what are the problems that we’re trying to solve with AI. What is the purpose of a chatbot, for example? What am I buying, and what can we as an organization accomplish with it?”

And then others just want immediate actionable solutions: “I’m not interested in hypotheticals. Show me five ways I can implement AI on Monday. Give me turnkey AI solutions that work today.” 

Personalized data is one of generative AI’s biggest value propositions. Conversational AI (chat) is the new marketplace, and it’s eating the entire funnel including in-destination engagement. In some cities, AI chat conversations between DMOs and visitors is exceeding 25% in-destination, giving DMOs a way to influence visitor behavior in real time. Also, in some industry segments like tours and activities, click-through conversions in-chat are approaching 20% in some regions.

Everyone is looking for first/zero-party data around that.

“We have a low level of proprietary data about who is in our destination and what they’re doing,” asserted one CEO. “We have a few disparate data sources, but we need to connect those, and we want to be able to use it predictively. For example, we’re looking at regression analysis to help develop future scenario modeling, and we’re looking at AI to help us with that.” 

Another CEO said, “We need data that provides material value for our partners. That’s our mandate. How are our visitors spending money and where? How are they behaving and traveling through our area? How can we help people find our partners in a more personalized way?”

And one CEO added, “A key priority for us is figuring out the data equation so outbound messaging can be tailored to a 1-on-1 relationship.”

Striving for that direct individual customer engagement has been the holy grail for marketers for decades. The ability to deliver mass personalization at scale is now coming into focus with enhanced AI data curation.

National organizations like Destination Canada and Germany Travel have invested millions of dollars in data lakes/warehouses and knowledge graphs to restructure their tourism product data from scratch, capitalize on the data more effectively to rank higher in AI search, and drive incremental revenue to partners. That’s what the future looks like. For more information, check out the new Canadian Tourism Data Collective and Germany’s Open Data Project.

Today, local tourism organizations are starting to see how data structure and content architecture are strategic imperatives for them, too.

One municipal CEO explained, “I think it might almost be too early to have this conversation in some areas until AI matures more. One thing I am eager to know now is how do we develop a data lake to structure our own data and messaging to make it easier for AI search tools to find.”

AI search and SEO are a huge conversation today. During the past year, the #1 question I heard while speaking about AI with dozens of North American destination organizations, their boards and key industry/community stakeholders, was: How do we rank higher in AI search? What happens when AI buries organic content below the fold? What’s going to happen to our websites? Are they going to disappear? Is a DMO a DMO without a DMO website? How do we need to produce content differently? What the heck is a data lake?

One CEO I spoke with is planning on developing a new website for her organization. She said, “I don’t even know what I don’t know about AI search, but we need to figure some of that out before we start spending any money.”

Along with data, defining KPIs for AI integration is a hot topic of debate, and presently there isn’t a complete and codified list of metrics that DMO leaders agree on. That said, perhaps the travel and tourism industry collectively needs to understand what AI is, and what it can accomplish at scale, before attaching too many metrics to it.

A CMO told me at DI MarCom Summit this month, “People didn’t ask about ROI with the internet when it first came around. We just knew we had to have it,” because it was a new channel for reaching visitors. Same as AI now. Expanding on that, he suggested that DMOs need to break AI success tracking into awareness metrics and performance metrics, and get that conversation right before anything else.

Unlike previous technology evolutions, there doesn’t seem to be as much of a generational divide between early adopters of AI and everyone else. The industry can’t look to Gen Z for guidance about how to use AI to the same degree we once asked Millennials for insights about Instagram.

Again, driving AI adoption across an organization begins with the CEO. Once that’s established, the real challenge is convincing various department leaders and staff to lean into AI. It’s a challenge because there is widespread concern, skepticism and/or suspicion among many staff members across many industries that they’re being asked to learn new technologies that will eventually replace them.

Leadership should say to employees that no one can force them to use AI, but the job market will. The data is clear. According to the Linkedin/Microsoft 2024 Work Trend Index:

• 79% of leaders agree their company needs to adopt AI to stay competitive
• 71% of leaders are more likely to hire a less experienced candidate with Al skills than a more experienced one without
• 66% of leaders won’t hire someone without AI skills
• 60% of leaders worry their organization lacks a plan and vision to implement AI

And that’s a year old.

But, understandably, emphasizing those numbers comes across as threatening. A better strategy for driving AI adoption is finding staff members who are already seeing success with AI, supporting them with access to education and new AI models, and working out from there. The more employees see their colleagues using AI, the more some of them feel inclined to lean in.

Therefore, every DMO needs a comprehensive strategic AI road map that includes purpose, policy, education, integration and a continuous feedback loop across all levels of staff. CEOs need to create a space for that.

“We’re giving our staff time to play around with AI, but they have to present what they’re focusing on and what they’re learning to their individual supervisor,” a CEO told me.

Another added, “We’re focusing on staff adoption. But I need you to explain to me what works, how to implement, and what should I watch out for.”

I’m presently working with the various departments at Matador Network where we’re exploring a bunch of various AI models to optimize workflows, boost creativity, enhance strategic planning, diversify product, etc. There’s a well-communicated mandate from the top that Matador will become an AI-first company, and sooner than later.

To help accelerate that, we created an AI task force to bring more perspectives from across the company together to integrate AI into operations. That has been valuable with some immediate takeaways, such as staff is now reaching out and asking for assistance with AI.

But it’s only a start.

“There’s this idea that if you can get a core group of people using AI then their attitudes and behaviors will spread organically throughout the rest of the company,” said Conor Grennan at NYU. “It doesn’t work that way. You have to find the people who have fears about AI and listen to them to understand why. You have to say, ‘Yes, AI does suck at that, but here’s what it’s good at.’ You also need to weed out the negative people who are against AI outright. They’re dangerous.”

Let’s go back to that first CEO who wants to know the five big questions about AI. Based on the input from the above CEOs, here’s a first stab at that:

1. How can I create AI policy that serves our organization but doesn’t suppress experimentation among staff? (Interestingly, policy did not come up that much with the CEOs I spoke with.)
2. How do we need to develop our websites, content and data to rank higher in AI search? How is AI SEO different, and the same, as old SEO?
3. What are existing AI chat platforms accomplishing, how are visitors using them, and how are they influencing travel decision making and purchase behavior?
4. If AI is not just for marketing, what does the ecosystem of AI tools for all departments look like across the organization. And what do they do in terms of increasing productivity, creativity and quality?
5. How do I get staff to buy in? We know AI is an imperative for our competitive advantage and relevance. And we know AI is an imperative for employees for their professional growth and marketability. But how do we get people to really believe?

If you’re a DMO CEO and you would like to share your thoughts on this post and add to the conversation, let us know. Also, we at Matador have developed a strategic DMO AI Road Map that provides a surgical, phased approach to AI education, adoption and integration. If you want to learn more about that and the GuideGeek AI chat platform, reach out at greg@matadornetwork.com.

For details about how GuideGeek was developed to lead local, regional and national DMOs globally into the future, check out this Crunchbase story written by Matador CTO Stefan Klopp: Reinforcement Learning From Human Feedback Took Travel AI Tool To Near-Perfect Accuracy.