Navigating the Future of Destination Discovery: How AI is Reshaping Visitor Engagement
Search is changing, and with it, the way travellers discover destinations. More than 35% of Millennials and Gen Z now use AI as their primary trip planning tool, placing these platforms on par with destination websites.
Featuring Catharina Riess, Director Mediahouse, at Vienna Tourist Board, and Nils Persson, Chief Marketing Officer, at Visit Sweden, in conversation with Nicholas Hall, CEO & Founder, Digital Tourism Think Tank.
Search is changing, and with it, the way travellers discover destinations. More than 35% of Millennials and Gen Z now use AI as their primary trip planning tool, placing these platforms on par with destination websites. Meanwhile, most destinations report significant declines in organic traffic, with many attributing this shift directly to AI. The implications reach far beyond traffic metrics, touching the very heart of how destinations connect with potential visitors and build lasting brand awareness.
During a recent LinkedIn Live session, we held as part of our Future. Destination. Trends series, we asked two leading voices in destination marketing to explore what this transformation means for the industry. The conversation with Catharina Riess, from the Vienna Tourist Board, and Nils Persson, fromVisit Sweden moved beyond the initial concern about declining search traffic towards a more considered understanding of both the challenges and opportunities that AI presents for destinations.
Accepting that search behaviour has changed represents the starting point. Destinations need to understand as much as possible about how discovery now works. The focus centres on two core responsibilities: deciding which stories to tell and understanding which channels will carry those stories most effectively. Both aspects now require rethinking in light of how AI mediates the discovery process.
The Content Quality Challenge
The instinct when facing declining traffic might be to produce more content, casting a wider net in hopes of capturing attention. Evidence from leading destinations suggests this represents the wrong response. The focus needs to shift towards producing quality content that tells a story. This means moving beyond the basic information that has traditionally filled destination websites towards content that provides genuine value through narrative, context and expertise. This is because generic descriptions of attractions or simple listings of opening hours and addresses hold little value when that same information exists in dozens of other places across the web.
Understanding what makes content valuable to both travellers and LLMs requires new skills. Content needs to demonstrate depth of insights, provide unique perspectives and contextual information that only destinations, with their local knowledge of a place, can deliver. Creating content that works in this environment demands both strong storytelling capabilities and an understanding of how information is structured and tagged, prioritising authentic and high-profile media partnerships. This is because high-profile media partnerships extend reach whilst lending credibility, creating content that stands out both in terms of production quality and the authority of the voices delivering the message.
Content Expertise and the DMO Advantage
DMOs hold a distinct advantage in the AI era when it comes to credibility and expertise. Unlike commercial platforms, DMOs represent an authoritative voice when promoting the destination. This matters enormously when LLMs attempt to assess which sources to trust and surface in response to queries. When developing a content strategy DMOs need to understand what their target audiences seek and create content which speaks directly to them.
The brand proposition becomes the lens through which all content is filtered, ensuring consistency of the messaging, whilst demonstrating genuine expertise about what makes a destination distinctive. This is because AI systems don't simply scrape information randomly. They attempt to identify and privilege authoritative sources. Destinations that can clearly demonstrate expertise through comprehensive, well-structured content that reflects deep local knowledge position themselves to be recognised as credible sources by these systems.
Sustained Human Judgement in AI Planning
The enduring importance of human judgement, curation and skills remains even as AI continues to assume a larger role in travel planning. Generative AI tools excel at surfacing recommendations, particularly for hidden gems and lesser-known experiences. The technology makes it easier to provide personalised suggestions that go beyond the obvious attractions. However, this capability doesn't diminish the need for a human-centred approach, particularly when it comes to the strategic work of helping small destinations and businesses build the datasets and structured content required for visibility within AI systems.
For example, travellers who use AI-powered travel planning tools appreciate the ability to get personalised itinerary suggestions. However, creating those recommendations depends on tourism professionals first doing the foundational work of identifying experiences, gathering accurate information and structuring content appropriately.
This work demands expertise that no AI system currently possesses. It requires understanding of local context, identifying what makes experiences distinctive and translating that knowledge into formats that both visitors and AI systems can access. Someone must decide what information matters, how to structure it effectively and how to maintain consistency and accuracy across multiple channels. These remain deeply human tasks that benefit from AI assistance but cannot be replaced.
Brand Salience in the Discovery Shift
The continuing relevance of brand awareness deserves attention even as AI systems mediate more discovery. There's a temptation to assume that if AI answers travel planning queries directly, traditional brand-building efforts become less important. However, destinations must know what their brand is because there are layers to how a visitor will decide why to visit. AI can suggest destinations and create itineraries, but it cannot make the emotional connection or convey the distinctive character that ultimately drives choice.
The rise of AI doesn't invalidate investments in brand-building or storytelling. These efforts become more important as they provide the foundation upon which AI systems draw when responding to queries. A strong brand becomes more discoverable precisely because it creates the kind of distinctive, memorable content that AI systems recognise as valuable.
Source: Created with ChatGPT
Connecting Strategy to Measurement
Measuring AI impact remains challenging, particularly as these systems provide limited visibility into how they select and surface information. Many destinations struggle to demonstrate the impact of their AI strategies. However, destinations can track branded search volumes, referral traffic from AI platforms or qualitative assessments of whether their content appears in AI-generated recommendations.
In fact, the most effective approach begins with clear strategic objectives rather than rushing to adopt new technologies. Leading DMOs identify specific goals that matter to their context: whether encouraging visitors to stay overnight rather than passing through or finding the balance between visitor satisfaction and resident wellbeing that recognises tourism's impact on local communities.
These strategic objectives can turn into specific KPIs that teams can track and optimise against. The approach avoids the trap of measuring everything whilst providing clear direction for tactical decisions. It also ensures that AI readiness efforts connect to broader organisational goals.
Infrastructure for Discoverability
Whilst much of the discussion in the LinkedIn Live focused on content strategy and brand positioning, the technical work required to ensure discoverability within AI systems deserves equal attention. This involves creating structured data, developing APIs that make information easily accessible and organising content in ways that LLMs can understand.
Technical readiness serves as the foundation for everything else. Destinations can create exceptional content, but if that content isn't structured appropriately or made available through channels that AI systems can access, discoverability can suffer. This requires investment in technical capabilities that many destinations have historically undervalued, seeing them as back-end concerns rather than core marketing priorities.
The work involves identifying all the products and experiences a destination offers, then curating and structuring data formats. It requires developing APIs that allow AI systems to query this information programmatically rather than relying on web scraping. It demands attention to metadata, tagging and other technical details that prove essential for discoverability.
Source: Created with ChatGPT
Building Collaborative Resilience
Partnerships emerge repeatedly as essential in navigating the AI transition. DMOs cannot solve these challenges alone and the pace of technological change makes collaboration more valuable than ever. Working with media partners, technology providers and other destinations to share insights and knowledge can help with developing a set of approaches that benefit the broader industry. This collaborative spirit reflects a recognition that DMOs share a common challenge when it comes to maintaining relevance in an AI-driven discovery environment.
These partnerships also prove particularly valuable for smaller destinations that may lack the resources to develop sophisticated technical infrastructure independently. Industry-wide standards and shared platforms are becoming increasingly important. Therefore, collaborative approaches that establish common standards whilst preserving the distinctive character of individual destinations may offer the most sustainable path forward.
Embracing Experimentation and Flexibility
The uncertainty inherent in this moment of AI transformations is real. Nobody knows exactly how AI-driven discovery will evolve, which platforms will dominate or what additional changes lie ahead. This makes extremely adaptability valuable. Building a team which is comfortable with experimentation, able to test approaches quickly and pivot when results disappoint becomes essential. This requires tolerance for failure and recognition that perfect strategies cannot be planned in advance when the landscape keeps shifting.
Flexibility in resource allocation matters as well. Being willing to reallocate budget and attention as the effectiveness of different channels changes might mean reducing investment in tactics that worked well historically but show declining returns currently, whilst increasing support for emerging channels even when the return on investment remains unclear. The AI transition demands a different operating model, one that prizes responsiveness and treats strategy as an ongoing conversation.
Strategic Priorities for the AI Era
Several strategic imperatives emerge for DMOs navigating this transition:
Content quality trumps content volume: When the same basic information exists in multiple places accessible to AI models, maintaining it on a destination website creates no strategic value. DMOs must differentiate through stories, local expertise, curated perspectives and narratives that are very context heavy. Every piece of content needs to demonstrate genuine depth rather than contributing to an ever-growing pile of generic material.
Discoverability requires active investment in technical infrastructure: Waiting passively to be discovered by AI systems puts DMOs at the mercy of intermediaries who may extract value or distort representation. Creating APIs, structuring data appropriately and building direct connections between destination content and AI models is important. This technical work doesn't replace creative content but enables it to be found and used effectively.
Brand awareness remains important: AI can suggest destinations and surface information, but it cannot create the emotional connections or convey the distinctive character that ultimately drives visitor choice. Sustained investment in brand-building creates the foundation that makes destinations memorable and discoverable.
Human expertise continues to play an essential role: AI cannot replicate the work required to understand local context, identify what makes experiences distinctive, structure information appropriately and maintain the quality and accuracy that makes content valuable. AI amplifies human expertise rather than replacing it. The strategic work of helping small destinations and businesses build the datasets needed for AI visibility demands skills and judgement that remain distinctly human.
Partnerships and collaboration accelerate progress: The challenges facing DMOs are largely sector-wide rather than organisation-specific. Sharing learnings, developing common standards where appropriate and building collaborative infrastructure strengthens the entire industry's capacity to navigate this transition successfully. No destination can solve these challenges in isolation.
For destination marketers seeking to understand these shifts more deeply and connect with peers wrestling with similar challenges, Future. Destination. Brand. offers an opportunity to engage with these questions in person. Taking place from 3-5 December in Barcelona, the event dedicates two full days to exploring the importance of a brand, storytelling and the implications of AI for destination marketing. This includes both leadership-focused discussions through our MarTech Workstream on shaping an effective AI strategy and real DMO cases revealing how destinations are using emerging tools effectively.
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Featuring Catharina Riess, Director Mediahouse, at Vienna Tourist Board, and Nils Persson, Chief Marketing Officer, at Visit Sweden, in conversation with Nicholas Hall, CEO & Founder, Digital Tourism Think Tank.
Search is changing, and with it, the way travellers discover destinations. More than 35% of Millennials and Gen Z now use AI as their primary trip planning tool, placing these platforms on par with destination websites. Meanwhile, most destinations report significant declines in organic traffic, with many attributing this shift directly to AI. The implications reach far beyond traffic metrics, touching the very heart of how destinations connect with potential visitors and build lasting brand awareness.
During a recent LinkedIn Live session, we held as part of our Future. Destination. Trends series, we asked two leading voices in destination marketing to explore what this transformation means for the industry. The conversation with Catharina Riess, from the Vienna Tourist Board, and Nils Persson, fromVisit Sweden moved beyond the initial concern about declining search traffic towards a more considered understanding of both the challenges and opportunities that AI presents for destinations.
Accepting that search behaviour has changed represents the starting point. Destinations need to understand as much as possible about how discovery now works. The focus centres on two core responsibilities: deciding which stories to tell and understanding which channels will carry those stories most effectively. Both aspects now require rethinking in light of how AI mediates the discovery process.
The Content Quality Challenge
The instinct when facing declining traffic might be to produce more content, casting a wider net in hopes of capturing attention. Evidence from leading destinations suggests this represents the wrong response. The focus needs to shift towards producing quality content that tells a story. This means moving beyond the basic information that has traditionally filled destination websites towards content that provides genuine value through narrative, context and expertise. This is because generic descriptions of attractions or simple listings of opening hours and addresses hold little value when that same information exists in dozens of other places across the web.
Understanding what makes content valuable to both travellers and LLMs requires new skills. Content needs to demonstrate depth of insights, provide unique perspectives and contextual information that only destinations, with their local knowledge of a place, can deliver. Creating content that works in this environment demands both strong storytelling capabilities and an understanding of how information is structured and tagged, prioritising authentic and high-profile media partnerships. This is because high-profile media partnerships extend reach whilst lending credibility, creating content that stands out both in terms of production quality and the authority of the voices delivering the message.
Content Expertise and the DMO Advantage
DMOs hold a distinct advantage in the AI era when it comes to credibility and expertise. Unlike commercial platforms, DMOs represent an authoritative voice when promoting the destination. This matters enormously when LLMs attempt to assess which sources to trust and surface in response to queries. When developing a content strategy DMOs need to understand what their target audiences seek and create content which speaks directly to them.
The brand proposition becomes the lens through which all content is filtered, ensuring consistency of the messaging, whilst demonstrating genuine expertise about what makes a destination distinctive. This is because AI systems don't simply scrape information randomly. They attempt to identify and privilege authoritative sources. Destinations that can clearly demonstrate expertise through comprehensive, well-structured content that reflects deep local knowledge position themselves to be recognised as credible sources by these systems.
Sustained Human Judgement in AI Planning
The enduring importance of human judgement, curation and skills remains even as AI continues to assume a larger role in travel planning. Generative AI tools excel at surfacing recommendations, particularly for hidden gems and lesser-known experiences. The technology makes it easier to provide personalised suggestions that go beyond the obvious attractions. However, this capability doesn't diminish the need for a human-centred approach, particularly when it comes to the strategic work of helping small destinations and businesses build the datasets and structured content required for visibility within AI systems.
For example, travellers who use AI-powered travel planning tools appreciate the ability to get personalised itinerary suggestions. However, creating those recommendations depends on tourism professionals first doing the foundational work of identifying experiences, gathering accurate information and structuring content appropriately.
This work demands expertise that no AI system currently possesses. It requires understanding of local context, identifying what makes experiences distinctive and translating that knowledge into formats that both visitors and AI systems can access. Someone must decide what information matters, how to structure it effectively and how to maintain consistency and accuracy across multiple channels. These remain deeply human tasks that benefit from AI assistance but cannot be replaced.
Brand Salience in the Discovery Shift
The continuing relevance of brand awareness deserves attention even as AI systems mediate more discovery. There's a temptation to assume that if AI answers travel planning queries directly, traditional brand-building efforts become less important. However, destinations must know what their brand is because there are layers to how a visitor will decide why to visit. AI can suggest destinations and create itineraries, but it cannot make the emotional connection or convey the distinctive character that ultimately drives choice.
The rise of AI doesn't invalidate investments in brand-building or storytelling. These efforts become more important as they provide the foundation upon which AI systems draw when responding to queries. A strong brand becomes more discoverable precisely because it creates the kind of distinctive, memorable content that AI systems recognise as valuable.
Source: Created with ChatGPT
Connecting Strategy to Measurement
Measuring AI impact remains challenging, particularly as these systems provide limited visibility into how they select and surface information. Many destinations struggle to demonstrate the impact of their AI strategies. However, destinations can track branded search volumes, referral traffic from AI platforms or qualitative assessments of whether their content appears in AI-generated recommendations.
In fact, the most effective approach begins with clear strategic objectives rather than rushing to adopt new technologies. Leading DMOs identify specific goals that matter to their context: whether encouraging visitors to stay overnight rather than passing through or finding the balance between visitor satisfaction and resident wellbeing that recognises tourism's impact on local communities.
These strategic objectives can turn into specific KPIs that teams can track and optimise against. The approach avoids the trap of measuring everything whilst providing clear direction for tactical decisions. It also ensures that AI readiness efforts connect to broader organisational goals.
Infrastructure for Discoverability
Whilst much of the discussion in the LinkedIn Live focused on content strategy and brand positioning, the technical work required to ensure discoverability within AI systems deserves equal attention. This involves creating structured data, developing APIs that make information easily accessible and organising content in ways that LLMs can understand.
Technical readiness serves as the foundation for everything else. Destinations can create exceptional content, but if that content isn't structured appropriately or made available through channels that AI systems can access, discoverability can suffer. This requires investment in technical capabilities that many destinations have historically undervalued, seeing them as back-end concerns rather than core marketing priorities.
The work involves identifying all the products and experiences a destination offers, then curating and structuring data formats. It requires developing APIs that allow AI systems to query this information programmatically rather than relying on web scraping. It demands attention to metadata, tagging and other technical details that prove essential for discoverability.
Source: Created with ChatGPT
Building Collaborative Resilience
Partnerships emerge repeatedly as essential in navigating the AI transition. DMOs cannot solve these challenges alone and the pace of technological change makes collaboration more valuable than ever. Working with media partners, technology providers and other destinations to share insights and knowledge can help with developing a set of approaches that benefit the broader industry. This collaborative spirit reflects a recognition that DMOs share a common challenge when it comes to maintaining relevance in an AI-driven discovery environment.
These partnerships also prove particularly valuable for smaller destinations that may lack the resources to develop sophisticated technical infrastructure independently. Industry-wide standards and shared platforms are becoming increasingly important. Therefore, collaborative approaches that establish common standards whilst preserving the distinctive character of individual destinations may offer the most sustainable path forward.
Embracing Experimentation and Flexibility
The uncertainty inherent in this moment of AI transformations is real. Nobody knows exactly how AI-driven discovery will evolve, which platforms will dominate or what additional changes lie ahead. This makes extremely adaptability valuable. Building a team which is comfortable with experimentation, able to test approaches quickly and pivot when results disappoint becomes essential. This requires tolerance for failure and recognition that perfect strategies cannot be planned in advance when the landscape keeps shifting.
Flexibility in resource allocation matters as well. Being willing to reallocate budget and attention as the effectiveness of different channels changes might mean reducing investment in tactics that worked well historically but show declining returns currently, whilst increasing support for emerging channels even when the return on investment remains unclear. The AI transition demands a different operating model, one that prizes responsiveness and treats strategy as an ongoing conversation.
Strategic Priorities for the AI Era
Several strategic imperatives emerge for DMOs navigating this transition:
Content quality trumps content volume: When the same basic information exists in multiple places accessible to AI models, maintaining it on a destination website creates no strategic value. DMOs must differentiate through stories, local expertise, curated perspectives and narratives that are very context heavy. Every piece of content needs to demonstrate genuine depth rather than contributing to an ever-growing pile of generic material.
Discoverability requires active investment in technical infrastructure: Waiting passively to be discovered by AI systems puts DMOs at the mercy of intermediaries who may extract value or distort representation. Creating APIs, structuring data appropriately and building direct connections between destination content and AI models is important. This technical work doesn't replace creative content but enables it to be found and used effectively.
Brand awareness remains important: AI can suggest destinations and surface information, but it cannot create the emotional connections or convey the distinctive character that ultimately drives visitor choice. Sustained investment in brand-building creates the foundation that makes destinations memorable and discoverable.
Human expertise continues to play an essential role: AI cannot replicate the work required to understand local context, identify what makes experiences distinctive, structure information appropriately and maintain the quality and accuracy that makes content valuable. AI amplifies human expertise rather than replacing it. The strategic work of helping small destinations and businesses build the datasets needed for AI visibility demands skills and judgement that remain distinctly human.
Partnerships and collaboration accelerate progress: The challenges facing DMOs are largely sector-wide rather than organisation-specific. Sharing learnings, developing common standards where appropriate and building collaborative infrastructure strengthens the entire industry's capacity to navigate this transition successfully. No destination can solve these challenges in isolation.
For destination marketers seeking to understand these shifts more deeply and connect with peers wrestling with similar challenges, Future. Destination. Brand. offers an opportunity to engage with these questions in person. Taking place from 3-5 December in Barcelona, the event dedicates two full days to exploring the importance of a brand, storytelling and the implications of AI for destination marketing. This includes both leadership-focused discussions through our MarTech Workstream on shaping an effective AI strategy and real DMO cases revealing how destinations are using emerging tools effectively.
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