Peer knowledge
Use Cases
Learn from what other DMOs are actually doing with AI. Members share real projects, results, and lessons learned. No vendor pitches, no sponsored content. Peer knowledge only.
AI-Powered Visitor Concierge Covering 1,000+ Partners
Visit Tampa Bay deployed an AI chat concierge handling 6,500+ visitor questions, 45% of them outside business hours.
Challenge
Visit Tampa Bay needed to provide visitors with accurate, on-brand answers to questions about attractions, sports events, dining, and transport — including outside business hours, when 45% of visitor enquiries were occurring.
Approach
Partnered with Satisfi Labs to deploy an AI chat agent trained on CRM data and website content. The agent was connected to partner agents at Zoo Tampa and the Tampa Bay Rays through a "chat-in-chat" capability, enabling seamless handoffs to authoritative partner sources.
Outcome
Over 6,500 visitor questions handled since implementation. 45% of conversations occurred outside normal business hours. Content scalability improved across more than 1,000 partner organisations.
Ethical considerations
The AI agent was trained to redirect sensitive queries and align all responses with the DMO’s values and messaging guidelines. Human oversight was maintained through ongoing training by DMO staff.
Lessons learned
Deploying an AI concierge earlier would have captured significant visitor engagement value. Integration with partner data sources is critical for response accuracy.
Source / attribution
Simpleview / Satisfi Labs case study, 2025. (simpleviewinc.com — publicly available)
Virtual Concierge That Reveals What Visitors Actually Want
Visit Austin used conversational AI to surface visitor intent that surveys and analytics could not reveal.
Challenge
Visit Austin wanted to understand visitor intent more accurately than demographic data and website analytics alone could reveal.
Approach
Deployed Satisfi Labs AI Chat on the destination website, powered by Simpleview CRM data. The conversational interface provided granular, real-time answers to visitor questions about listings, events, and transport.
Outcome
The chat data revealed unexpected visitor intent patterns — for example that 41% of hotel enquiry conversations began with non-hotel related questions, and 77% of exploratory questions were asked outside business hours. This data informed the digital marketing team’s content and campaign strategy.
Ethical considerations
All visitor interaction data was used in aggregate for strategic insight. No individual visitor data was used for profiling or targeted advertising.
Lessons learned
Conversational AI produces a quality of visitor intent data that surveys and analytics cannot replicate. The value extends beyond customer service into strategic intelligence.
Source / attribution
Simpleview / Satisfi Labs case study — Visit Austin, 2024. (simpleviewinc.com — publicly available)
AI Search Visibility Strategy: Becoming the Authoritative Answer
Brand USA built an answer-engine optimisation strategy to remain visible as travellers shift trip planning to AI tools.
Challenge
Brand USA recognised that over 60% of travellers were using AI tools for trip planning but rarely visiting destination websites during the research process. Traditional SEO was no longer sufficient to ensure visibility.
Approach
Developed an answer-engine optimisation (AEO) strategy focused on three priorities: structured website schema markup so AI systems could read destination data reliably; creation of content that answered questions AI systems could not find elsewhere online; and moving beyond top-ten lists toward hyperlocal, contextual content with genuine informational depth.
Outcome
Positioned Brand USA as an authoritative content source for AI-driven travel discovery, reducing dependence on traditional search ranking as a primary visibility mechanism.
Ethical considerations
All published content remained factually accurate and human-reviewed. No AI-generated content was published without editorial oversight.
Lessons learned
AI visibility requires a fundamentally different content strategy from SEO. The question to answer is not "what keywords do we rank for?" but "can an AI system accurately describe us?"
Source / attribution
Janette Roush, SVP Innovation and Chief AI Officer, Brand USA, cited in PhocusWire, May 2026. (phocuswire.com — publicly available)
AI-Driven Programmatic Advertising for Real-Time Campaign Optimisation
A large North American DMO used data-trained AI to optimise paid media bids and targeting in real time.
Challenge
A large North American DMO needed to improve the return on its paid media investment by moving away from historical audience assumptions toward real-time performance-based optimisation.
Approach
Implemented data-trained AI for programmatic advertising, enabling real-time bid adjustments, audience targeting, and budget reallocation based on live campaign performance rather than historical data.
Outcome
Measurable improvement in media buying efficiency and audience targeting precision. The AI layer enabled adjustments that human media buyers could not make at the same speed or granularity.
Ethical considerations
Audience targeting parameters were reviewed to ensure no discrimination by protected characteristics. Human campaign managers retained authority over strategic decisions and budget thresholds.
Lessons learned
Programmatic AI is fundamentally different from consumer-facing generative AI. It requires specialist vendor selection and clear performance KPIs defined before deployment.
Source / attribution
Orange 142, Practical Generative AI Use Cases for DMOs, 2025/2026. (orange142.com — publicly available)
Building a Human-Centric AI Governance Framework Across a DMO Team
A major convention and visitors bureau integrated AI across departments while protecting human creative direction.
Challenge
A major North American convention and visitors bureau needed to integrate AI tools across multiple departments without losing the human-centred storytelling that differentiated their destination marketing.
Approach
Adopted a governance framework built on three principles: define the problem before selecting a tool; treat AI as a collaborative partner rather than a standalone solution; invest in developing uniquely human skills including creative judgement, emotional intelligence, and critical review of AI outputs. Staff used AI for idea generation, first drafts, and data analysis, with human review required before any output was used externally.
Outcome
AI was integrated into daily workflows across marketing, content, and operations without replacing human creative direction. Staff confidence in using AI tools increased significantly after structured governance was in place.
Ethical considerations
Human oversight was embedded as a non-negotiable requirement across all AI use cases. Staff were explicitly trained that AI outputs require human judgement to be contextualised and refined.
Lessons learned
The most common mistake is jumping to tools before defining the problem. Governance and training must precede deployment.
Source / attribution
HIVE Interactive, presented at VISIT DENVER, cited in Destinations International blog, 2024/2025. (destinationsinternational.org — publicly available)
Developing a DMO AI Policy: Five Questions Every Organisation Must Answer First
Destinations International’s five-question framework helps DMOs set AI policy before adopting tools.
Challenge
Across the DMO sector, organisations were adopting AI tools without a governing policy, creating inconsistency, compliance exposure, and reputational risk. The 2025 DestinationNEXT Futures Study identified AI adoption, workforce readiness, and organisational capacity as the three core strategic themes requiring immediate attention.
Approach
Destinations International developed a structured AI policy framework based on five foundational questions: What is AI actually doing in your organisation today? What values must your AI use reflect? What are the boundaries — what will you not use AI for? Who is accountable? How will you review and update the policy as AI evolves?
Outcome
DMOs that adopted the framework reported clearer staff guidance, reduced ad hoc AI experimentation, and greater confidence in communicating their AI approach to funders and stakeholders.
Ethical considerations
The framework explicitly required DMOs to define ethical boundaries before selecting tools, rather than retrofitting governance after adoption.
Lessons learned
Real innovation in AI adoption comes from intentional governance and values alignment, not from the tools themselves.
Source / attribution
Destinations International AI Policy Framework, 2025. (destinationsinternational.org — publicly available)
Answer-Engine Optimisation: Structuring Destination Content for AI-Mediated Discovery
With 60%+ of travellers planning via AI, DMOs are structuring content so AI systems cite them accurately.
Challenge
Research from the Digital Tourism Think Tank found that over 60% of travellers now use AI tools for trip planning, yet these travellers rarely visit destination websites during the research process. DMOs risk becoming invisible at the moment travel decisions are made.
Approach
DMOs implementing AEO strategies focused on: auditing website schema markup to ensure AI systems could read destination data accurately; creating structured content that answered questions unique to the destination and not available from other sources; building verified, authoritative data feeds that AI systems could reference with confidence; and measuring AI-driven traffic separately from traditional organic search.
Outcome
SimpleView and Granicus analysis found that ChatGPT made nearly two million requests to DMO websites in an eight-day period, confirming that AI systems are actively consuming DMO content at scale. DMOs with structured data and original content were more likely to be cited.
Ethical considerations
All content published for AEO purposes must be factually accurate. AI visibility strategies that rely on misleading or exaggerated content expose DMOs to reputational and regulatory risk.
Lessons learned
AEO is not a replacement for good content strategy — it is an extension of it. The starting point is accurate, structured, original information that exists nowhere else on the internet.
Source / attribution
Digital Tourism Think Tank, PhocusWire, and SimpleView/Granicus research, 2025/2026. (thinkdigital.travel and phocuswire.com — publicly available)
Reclaiming Staff Time: AI for Repetitive Administrative Tasks
Small and mid-size DMOs reclaimed up to 5% of staff time per quarter by automating routine admin with AI.
Challenge
Small and mid-size DMOs face disproportionate administrative burdens relative to their staff capacity. Research cited by Destinations International indicated that DMOs can reclaim up to 5% of staff time per quarter by deploying AI for repetitive tasks.
Approach
DMOs piloted AI tools for: generating first drafts of board reports and stakeholder updates; automating routine email drafting and template management; summarising long documents and research reports; processing and categorising partner data; and building itinerary and content recommendations.
Outcome
Staff reported meaningful time savings on low-value tasks, freeing capacity for strategic work. Chatbots and itinerary builder tools also improved visitor satisfaction and website engagement at low implementation cost.
Ethical considerations
All AI-generated documents and emails required human review before sending or publishing. Staff were not replaced; time savings were reinvested in strategic and creative work.
Lessons learned
Small DMOs with limited resources benefit most from starting with administrative AI use cases before moving to more complex marketing or analytics applications.
Source / attribution
Destinations International, "4 Ways AI Can Streamline Your DMO’s Workload", 2025. (destinationsinternational.org — publicly available)
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