The tourism and hospitality industry faces constant pressure to do more with less. From managing seasonal swings in demand to handling repetitive administrative tasks, operators and destination marketers often struggle to find time for strategic planning and guest engagement. While artificial intelligence offers obvious efficiency benefits, many organizations feel overwhelmed by the sheer volume of tools and technical jargon, leaving them stuck in the planning phase.
Peter Pilarski, Founder of Tourism AI Network, helps solve this problem by making artificial intelligence practical and accessible for travel professionals. His company provides consulting, training, and a structured AI Adoption Framework designed specifically for destination marketers, hotels, and tour operators. In this interview, we ask Pilarski how tourism organizations can move past the hype, implement practical workflows, and build lasting capability across their teams.
Q: Many tourism operators feel overwhelmed by the fast pace of artificial intelligence development. How do you help organizations cut through the noise and identify which tools actually matter for their daily work?
Peter Pilarski: While we believe that AI is about much more than tools and that tools should not be the first consideration, tourism operators should invest in a team account from one of the leading North American frontier companies such as Claude, Chat GPT, Gemini or Co-Work. While there are many other tools available, getting team account benefits from these providers and learning how to leverage one of these tools well is a critical starting point and likely all that many tourism operators will ever need.
Q: You offer a 90-minute AI Intuition Sprint to help teams kickstart their journey. What are the most common efficiency gaps you see during these sessions, and how quickly can organizations start seeing real results?
Peter Pilarski: Our intuition sprints are designed to teach people how AI systems work in general and how to leverage them in a way that produces consistent, reliable results. The goal of a sprint session is to help participants identify tasks that are ideal for AI and to work through the steps of automating a workflow. These sessions are hands-on and we help participants understand and identify AI patterns and build AI intuition, to make them more effective at automating their workflows and improve their efficiency and effectiveness.
Q: Tourism is fundamentally about human connection and hospitality. How do you ensure that implementing automated workflows enhances the guest experience rather than making it feel cold or robotic?
Peter Pilarski: There are many workflows in tourism that are invisible and administrative, which are necessary, but are not client facing. By automating these workflows first, tourism professionals are able to free up time for client facing tasks and to provide great hospitality and memorable tourism experiences when guests arrive. Also important is clearly defining tasks that can be fully AI automated, partially AI automated and human-always and to also be clear about when a human must be in the loop. Additionally, if AI systems are set up properly and provide the right context, instructions and safeguards, then can quite effectively become customized at the individual client level, making recommendations more personal and relevant to client preferences and needs. Mapping workflows out in this way is the work that tourism operators need to do to make AI work for both their clients and them and exactly the type of work we do when helping tourism businesses become more AI enabled.
Q: Your AI Adoption Framework is built specifically for the travel sector rather than being a generic business model. Why is it so important to have a strategy tailored to the unique challenges of tourism and destination marketing?
Peter Pilarski: Tourism doesn't run like other industries, and generic AI advice ignores that. A destination marketing organization juggles seasonality, dozens of independent operators, and a product that's fundamentally about human experience, and you can't automate your way around that. Retail AI adoption assumes one company, one dataset, one voice. Tourism is the opposite: fragmented ownership, inconsistent data, and a brand that has to stay authentic across hundreds of front-line interactions a generic model was never built for.
We built ours around three things generic AI programs miss: how small operators can actually use this without a data team, how to protect guest experience while automating the boring parts, and how a destination's voice stays consistent when fifty different businesses are all telling pieces of the same story. That's not a generic problem. It's a tourism problem, and it needs a tourism answer.
There's also a competitive dynamic that doesn't exist anywhere else. In most industries, your competitor's win is your loss. In tourism, it's the opposite: a hotel doing well brings more people to the region, which fills restaurants, which supports tour operators, which makes the destination worth visiting in the first place. Everyone technically competes for the same traveler, but the whole ecosystem rises or falls together. That changes what AI adoption should even look like. It's not about one operator optimizing in isolation, but rather how visible and coherent the whole destination is when someone asks an AI where to go. We help our clients use that ecosystem to their advantage instead of fighting it.
Q: Data governance and ethical considerations are becoming major concerns for businesses adopting new technology. What steps should a destination marketing organization take to protect their data while experimenting with these tools?
Peter Pilarski: Data governance in tourism is tricky because the risk isn't really about one company's database, it's about how much data gets shared across a destination's ecosystem, from booking platforms to visitor surveys to third-party AI tools. Before a DMO experiments with anything, they need to know what data they actually hold, where it lives, and who else has access to it. That sounds basic, but most organizations can't answer that clearly.
The second step is tiering the data itself. Not everything needs the same level of caution. Aggregate visitor trends are low-risk to experiment with. Anything tied to individual travelers, whether that's contact information, payment details, or behavioral tracking, needs a much higher bar before it touches any AI tool, especially third-party ones.
The third piece is picking tools with clear data-handling terms. A lot of AI platforms are vague about what happens to the data you feed them, whether it's used for training, who can see it, how long it's retained. DMOs need to ask those questions upfront, not after they've already uploaded a year of visitor data.
We tell clients to treat this like any other vendor risk assessment. Start small, test with low-risk data, and build the governance habits before you scale up. The DMOs who get burned are usually the ones who move fast on the tools and slow on the guardrails.
Q: When you look at the operators who successfully scale their usage of these tools, what core habits or training methods set them apart from those who struggle to adapt?
Peter Pilarski: The operators who scale AI successfully treat it as a people and culture issue first, a systems and process issue second, and a technology issue third. Most companies get that order backwards. They buy the tool, hope it changes behavior, and wonder why adoption stalls six months later.
The single biggest differentiator we see is leadership. This is a leadership moment more than a technology moment. When a leader is genuinely engaged, using the tools themselves, experimenting, building their own intuition for what AI can and can't do, that changes everything downstream. Leaders have the broadest view of the organization, so they're the ones who can actually see where automation creates real transformation versus where it's just a shiny distraction. If the leader treats AI as someone else's project, the whole company follows that lead.
The second piece is culture. The operators who scale fastest have built an environment where people can experiment, where a failed attempt doesn't get punished, and where wins get shared openly instead of hoarded. Curiosity has to be encouraged, not just tolerated. That's what turns one person's AI win into a company-wide habit.
But culture and curiosity alone aren't enough. The operators who actually stick the landing also have a written AI roadmap, with clear goals, timelines, and a direct line back to business priorities. Without that, experimentation turns into a scattered pile of side projects nobody can measure. The roadmap is what keeps the curiosity productive instead of chaotic, and it's how leadership knows whether an initiative is actually working or needs to be killed
Our conversation with Peter Pilarski highlights that success with artificial intelligence does not require a deep technical background. Instead, it demands a clear strategy focused on solving specific operational problems. By starting small, training staff properly, and prioritizing ethical data use, tourism organizations can save valuable time and redirect their energy toward what matters most: the guest experience.
The gap between operators who use these tools and those who rely on manual processes will widen significantly over the next few years. Adapting to this shift is essential for long-term survival in the travel industry. Tourism AI Network offers a proven, industry-specific roadmap for businesses ready to stop guessing and start building real capability.
To learn more visit https://tourismainetwork.com/