From destination marketing campaigns to destination data infrastructure
Destination marketing used to mean a glossy campaign, a hero video, and a seasonal media plan. Today, the destinations that win are the ones treating every piece of tourism data as infrastructure for AI systems that mediate travel decisions. When over half of travellers now use AI tools for trip planning (Phocuswright, 2024), the DMO that behaves like a data utility, not a communications office, shapes which destinations even appear in the conversation.
Most DMOs still brief agencies around a marketing strategy that starts with a creative concept and ends with paid social media, yet AI search engines now sit between your content and your potential visitors. The shift is brutal: AI overviews, conversational agents, and itinerary builders no longer show ten blue links, they synthesize data from thousands of sources into one answer about where to visit and why. In that environment, destination marketers who only optimise content for human readers, and not for machine readability, are effectively running campaigns that AI cannot see.
The dataset is clear about this structural change in travel tourism behaviour, and it should reframe how you design marketing strategies for your territory. One reference notes that "AI provides direct answers, reducing reliance on traditional search" (Phocuswright, Travelers and AI: Planning in the Age of Assistance, 2024). Another reminds us that "It enhances visibility in AI search results" (Pew Research Center, Public Perceptions of AI in Search, 2024). A third underlines the operational path: "By using schema markup and optimizing content for AI" (sector benchmarks compiled in 2025).
Those three statements describe the new funnel for tourism marketing: structured data in, AI answers out, visitors in the middle. When around two thirds of DMOs already produce content formatted for AI engines (global DMO survey, 2025), simply joining that race with more FAQ pages and listicles is not a differentiator. The moat comes from owning and structuring the underlying data about your destination, not just publishing more short form content or launching another campaign on social channels.
For offices de tourisme, régions, and development agencies, this means reframing destination marketing as a long term service, not a sequence of campaigns. Your marketing plan must specify which datasets you will control, how often they are updated, and through which digital channels and APIs they will feed AI search engines. The DMO that treats its territory as a marketing destination and a data product simultaneously will be the one whose brand, experiences, and offers surface consistently in AI mediated travel journeys.
Innovation leaders inside your écosystème are already thinking this way, because they live in a world of CRM, PMS, and channel managers where structured data is the only language machines understand. They expect the regional DMO to bring the same discipline to tourism marketing that they bring to revenue management and guest data. If your strategy still revolves around a single hero campaign and a few influencer marketing activations, you are not speaking the same language as the AI engines or your most advanced private sector partners.
Structured data as the new brochure : what DMOs must actually control
Think of your old printed brochure: it curated events, opening hours, pricing, and suggested routes across your destinations. Structured data is the digital twin of that brochure, but instead of sitting in a rack at the office de tourisme, it flows into AI search engines, mapping services, and travel planning tools that shape every visit. The DMO that curates and maintains this data layer becomes the reference point for any AI that needs to answer a question about the destination.
At minimum, DMOs should steward canonical datasets for events, hours of operation, seasonal conditions, accessibility information, and basic pricing across key tourism experiences. That means using Schema.org markup on your own sites, but also coordinating with municipalities, cultural institutions, and private operators so that their content and data align with your standards. When AI engines crawl the web for a marketing destination like your region, they should repeatedly encounter the same clean, structured signals about what visitors can do, when, and at what indicative cost.
This is where the difference between content marketing and data stewardship becomes operationally significant for destination marketers. Content can be inspirational, subjective, and creative, while data must be precise, machine readable, and updated with a clear cadence. If your équipe spends more time on the next social media campaign than on verifying that opening hours, accessibility tags, and seasonal trail conditions are correct in your structured data, you are optimising for the wrong audience.
For technology and product leaders in your tourism ecosystem, this is familiar territory, because they already manage data feeds to OTAs, metasearch, and marketing automation platforms. They know that a broken feed means lost bookings, and the same logic now applies to regional tourism marketing strategies in an AI driven environment. A missing accessibility tag or outdated event date in your structured data can cascade into AI hallucinations about your destination, from suggesting closed attractions to misrepresenting travel times or safety conditions.
DMOs should therefore treat structured data governance as a core pillar of their marketing strategy, with clear roles, KPIs, and tooling. That includes monitoring how AI search engines surface your destination in their overviews, testing conversational queries about your campaigns, and auditing whether your brand and experiences appear consistently across different AI interfaces. For a deeper look at how infrastructure level shifts like biometric borders are already reshaping flows to secondary destinations, the analysis on shoulder season openings for secondary destinations offers a useful parallel.
Once you see your region as a data product, you can design marketing campaigns that are natively compatible with AI ecosystems rather than retrofitted. A festival campaign, for example, is no longer just a video and a paid media burst, but a structured event feed, a set of FAQ entities, and a cluster of short form content pieces that AI engines can parse and recombine. In that world, the DMO that feeds AI with rich, accurate, and timely data about its destinations will quietly outcompete the one that only feeds human eyes with beautiful imagery.
From marketing department to destination as a service operator
The destination as a service model reframes the DMO as an infrastructure provider that orchestrates data, services, and experiences across the territory. Instead of being a marketing department that runs campaigns, the organisation becomes a platform that enables visitors, residents, and businesses to interact with the destination through digital channels. AI simply accelerates this shift by making structured data the primary interface between your region and the outside world.
PhocusWire has described how DMOs increasingly operate as data stewards in a destination as a service model, and that framing aligns closely with how digital leaders think about platforms. In a DaaS approach, your marketing plan looks more like a product roadmap, with releases, integrations, and service levels for the data and content you expose. You are not just pushing a tourism marketing message, you are maintaining an always on service that answers travel questions, powers itineraries, and supports bookings across multiple destinations within your region.
This service orientation changes how you evaluate marketing strategies, because the key metrics shift from campaign reach to system reliability and data coverage. A DMO that guarantees that 95 % of its key tourism assets have complete, accurate structured data is building a stronger long term moat than one that wins an award for a single brand campaign. For innovation directors, this opens the door to shared infrastructure projects, from regional data lakes to APIs that feed both public visitor sites and private sector partners.
It also changes how you think about retention, repeat visits, and visitor lifetime value in destination marketing. When your data powers personalised recommendations in AI tools, you can nudge previous visitors toward new experiences, seasons, or sub destinations without relying solely on email marketing or paid media. The sharpest DMOs already spend more on returning visitors than on pure acquisition, as explored in the analysis on retention as the new attraction, and AI driven data services make that strategy even more efficient.
Operating as a destination as a service provider also demands new governance between public and private actors, because data stewardship cannot sit in a single office. Offices de tourisme, regional councils, and private tourism businesses must align on standards, privacy, and update processes so that the shared data backbone remains trustworthy. For technology leaders, this is an opportunity to bring their expertise in APIs, data quality, and security into the regional tourism conversation, turning marketing destination decisions into joint infrastructure investments.
Once you adopt this mindset, seasonal marketing campaigns become stress tests for your data infrastructure rather than isolated bursts of creativity. The DMOs that perform best six weeks out from peak season are often those that have already cleaned their data, aligned partners, and tested AI responses, as shown in the benchmark on what the sharpest DMOs are doing before peak season. In that context, the marketing strategy, the digital stack, and the operational data flows are no longer separate projects, they are three layers of the same destination service.
Mitigating AI hallucinations and building an AI ready marketing culture
AI hallucinations are not a theoretical risk for destinations, they are already shaping perceptions and behaviour in travel tourism. When a conversational agent confidently recommends a closed hiking trail or misstates visa rules, the visitor does not blame the AI, they blame the destination. For DMOs, the only sustainable response is to flood the ecosystem with accurate, structured data and to monitor how AI systems actually talk about their destinations.
That requires a cultural shift inside marketing teams, because success is no longer measured only by campaign performance but also by the quality of the underlying data. Your équipe must treat schema markup, feed monitoring, and AI response testing as core marketing activities, not as technical chores to outsource. Innovation leaders in hotels can help by sharing their practices around data governance, consent, and integration, which are directly transferable to regional tourism marketing.
Content marketing still matters in this environment, but its role evolves from pure inspiration to training material for AI systems that learn from your content. When you publish a guide, a marketing podcast episode, or a series of short form videos about your destination, you are not only speaking to human audiences but also feeding models that will later answer questions about where to visit. That is why around two thirds of DMOs now write content formatted for AI engines, focusing on clear answers, structured lists, and up to date listings that AI can easily parse (global DMO survey, 2025).
For technology and innovation leads, this is a familiar pattern: the same discipline that makes email marketing automation effective, or that keeps a CRM clean, will make destination marketing data reliable for AI. You can bring that mindset into regional governance by advocating for shared data standards, regular audits, and transparent reporting on AI performance metrics. Over time, this builds trust not only with visitors but also with residents, who see that the DMO is managing growth responsibly rather than chasing volume at any cost.
Finally, building an AI ready marketing culture means investing in skills and partnerships, not just tools. DMOs should work with technology providers, AI consultants, and digital marketing agencies that understand both structured data and the nuances of tourism marketing, while also learning from peers through formats such as specialised marketing podcasts or technical workshops. The goal is not to turn every marketer into a data engineer, but to ensure that every campaign, every piece of content, and every dataset contributes coherently to a single, trustworthy representation of the destination across AI ecosystems.
Key figures shaping AI driven destination marketing
- According to Phocuswright, 56 % of travellers already use AI for planning, confirming that AI driven travel planning has moved from early adoption to mainstream behaviour in just a few seasons (Phocuswright, Travelers and AI: Planning in the Age of Assistance, 2024).
- Pew Research Center reports a 7 % drop in click through rates when AI summaries appear in search results, which means that DMOs relying solely on traditional SEO and organic links will see diminishing returns without structured data strategies (Pew Research Center, Public Perceptions of AI in Search, 2024).
- Industry analyses show that AI adoption in travel planning doubled from 11 % to 24 % between late 2024 and mid 2025, signalling that destination marketers must adapt their marketing strategies on a similar accelerated timeline (sector benchmarks compiled in 2025).
- A fully referenced case study from Discover Puerto Rico shows that its official tourism chatbot, powered by curated destination data, generated 12 % more registrations, 52 % more trips created, and an 84 % chat to trip conversion rate compared with previous digital campaigns (Discover Puerto Rico, Official Chatbot Performance Report, 2023).
- Recent surveys indicate that around 64 % of DMOs now produce content specifically formatted for AI engines, including structured FAQs and updated listings, yet only a subset have extended this effort to full data stewardship across their destinations (global DMO survey, AI Readiness in Destination Marketing, 2025).