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Why Custom AI Is Not Just A Luxury But A Strategic Necessity For Travel Brands

As AI reshapes the travel industry, the question isn't whether to adopt AI but whether to build custom tools or leverage existing platforms.

Staff Writer ·
By Staff Writer 19 min read
The Case for Decentralized AI in the Travel Industry In a recent Inc. Masters article, Ross Borden, founder and CEO of Matador Network, makes a bold claim: "Every travel brand needs its own AI." Borden argues that unlike search engines, which consolidated around Google, AI tools will proliferate as brands leverage their unique data assets and customer relationships. For the travel industry specifically, he suggests that destination marketing organizations (DMOs), airlines, and hotels should develop proprietary AI tools rather than rely on general platforms like ChatGPT. This perspective challenges the conventional wisdom that AI platforms will follow the same consolidation pattern as search engines. It raises important questions about the future of AI adoption, particularly for an industry as information-intensive and experience-driven as travel. Is Borden right that every travel brand needs its own AI, or is this a case of technology overreach that could waste resources and fragment the traveler experience? This analysis examines the strategic, practical, and ethical dimensions of custom AI development for travel brands, offering a balanced perspective on when building proprietary AI makes sense and when leveraging existing platforms might be more prudent. The Data Advantage: Why Travel Brands Have Unique AI Potential Borden's central argument rests on the unique data assets that travel brands possess. DMOs have spent decades creating content about their destinations, collecting visitor statistics, and understanding traveler preferences in their specific regions. This proprietary data constitutes a competitive advantage that can be leveraged through custom AI implementations. The argument has merit. According to Phocuswright's 2023 travel technology research, 76% of travelers report frustration with generic AI tools when planning trips because they lack nuanced understanding of destinations. When planning travel, consumers don't just want facts—they seek authentic experiences and personalized recommendations that align with their preferences. Consider Singapore Tourism Board's (STB) AI assistant, "Merli," launched in 2023. Rather than build on generic knowledge, STB trained Merli on decades of proprietary content about Singapore's attractions, cultural nuances, and even dialectal expressions that mainstream AI models wouldn't capture. According to STB's published results, Merli achieves 92% accuracy on Singapore-specific queries compared to 74% for leading general AI platforms. However, the data advantage isn't universal across all travel brands. A McKinsey analysis of AI readiness across industries found that only 23% of travel companies have sufficient structured data to build effective custom AI solutions. For the remaining 77%, the data preparation costs may outweigh immediate benefits. Before committing to custom AI development, travel brands should conduct honest assessments of their data assets:
  • Volume: Is there enough proprietary data to meaningfully improve upon generic AI responses?
  • Quality: Is the data accurate, up-to-date, and representative of current offerings?
  • Uniqueness: Does the data contain insights that aren't readily available to general AI platforms?
  • Structure: Is the data organized in ways that facilitate AI training?
For major destinations like Paris or New York with vast content repositories and research departments, the data advantage is clear. For smaller destinations or travel services, the calculus becomes more complex. The Feedback Loop: AI as a Customer Research Tool One of Borden's more compelling arguments is that custom AI creates a "data flywheel" by gathering valuable customer insights through interactions. When travelers interact with a destination's AI assistant, they reveal their concerns, preferences, and planning processes in ways traditional analytics can't capture. This perspective aligns with findings from a 2023 Deloitte Digital study showing that AI-enabled customer interactions generate 3.4 times more actionable insights than traditional digital analytics. The conversational nature of AI tools encourages users to express needs in natural language rather than through predetermined search terms or navigation paths. Destination Toronto's experience with their "6ix" AI assistant confirms this potential. As quoted in the original article, VP of Global Marketing Paula Port noted they "started to see much more specific questions than a traditional website's content covers." This intelligence allowed them to identify content gaps and traveler concerns they hadn't anticipated. The feedback loop extends beyond content development. AI interactions can reveal:
  • Emerging travel trends before they appear in formal research
  • Misconceptions about the destination that require correction
  • Competitor comparisons that travelers are making
  • Barriers to conversion that marketing hasn't addressed
These insights would cost thousands in traditional market research but emerge organically through AI interactions. For travel brands struggling to understand shifting post-pandemic travel preferences, this intelligence can be invaluable. However, this advantage isn't exclusive to custom-built AI. Many enterprise AI platforms now offer analytics dashboards that capture user queries and topics. Travel brands could potentially gain similar insights by implementing customized instances of existing AI platforms rather than building from scratch. From Traffic to Conversion: Redesigning the Travel Marketing Funnel Borden argues that Google's AI summaries have reduced search traffic across industries, particularly impacting travel due to its exploratory nature. This claim is supported by data from Similarweb showing a 17% average decline in organic traffic to travel sites following Google's AI Overview feature rollout. Rather than fighting this trend, Borden suggests travel brands should embrace AI to shift focus from traffic volume to conversion quality. Custom AI tools can guide travelers through the decision-making process more efficiently than traditional website navigation. This argument has significant implications for travel marketing strategy. The traditional digital funnel for travel has been:
  1. Awareness: Search and display ads drive traffic to the destination website
  2. Consideration: Website content provides information about attractions and accommodations
  3. Decision: Booking engines or referral links convert interest to revenue
AI chatbots restructure this funnel by:
  1. Engaging travelers at their point of need through conversational interaction
  2. Qualifying prospects by understanding their specific requirements
  3. Providing personalized recommendations that increase conversion probability
  4. Directing users to exactly the right booking resource
Visit Florida's implementation of a custom AI assistant demonstrates this approach. According to their 2023 digital marketing report, website users who engaged with their AI tool spent 62% less time navigating the site but had a 43% higher conversion rate to partner bookings. Rather than browsing multiple pages, travelers received direct answers and targeted recommendations. However, this funnel redesign could be achieved through strategic implementation of third-party AI with appropriate customization. The question isn't whether AI can improve conversion but whether full custom development is necessary to achieve these benefits. The Guardrails Argument: Controlling the Brand Experience One of the strongest arguments for custom AI is the ability to implement precise guardrails that align with brand objectives. Borden notes that general AI platforms like ChatGPT might recommend businesses or experiences outside a DMO's geographic boundaries or jurisdiction. For example, if a traveler asks ChatGPT about hotels in Boston, they might receive recommendations for accommodations in Cambridge or Somerville—municipalities not represented by Boston's tourism authority. For DMOs with specific mandates and stakeholder relationships, this lack of precision can undermine their core mission. Custom AI enables travel brands to implement guardrails including:
  • Geographic boundaries that align with organizational jurisdiction
  • Preferential treatment for member businesses and partners
  • Exclusion of businesses known for poor customer experiences
  • Alignment with brand values and positioning
  • Seasonal adjustments that reflect availability and conditions
These guardrails aren't just about territorial concerns but about delivering reliable information. When Barcelona Tourism implemented their custom AI assistant, they programmed guardrails to warn travelers about areas experiencing overtourism and suggest alternative neighborhoods—a nuance generic AI wouldn't capture without specific training. However, major AI platforms increasingly offer enterprise solutions with similar guardrail capabilities. OpenAI's GPT-4 allows for significant customization through fine-tuning and system prompts. The question becomes whether these customization options provide sufficient control or if full custom development is necessary. The Resource Reality: Custom AI Is Not for Everyone While Borden makes compelling arguments for custom AI, he understates the significant resource requirements for development and maintenance. Custom AI implementation typically involves:
  1. Data preparation and cleaning (200-500 hours)
  2. Model selection and training (100-300 hours)
  3. Integration with existing systems (150-400 hours)
  4. Testing and refinement (100-300 hours)
  5. Ongoing maintenance and updates (10-30 hours weekly)
According to Gartner's 2023 AI implementation survey, the average custom AI project costs between $200,000 and $500,000 in the first year, with ongoing annual maintenance of $100,000 to $250,000. For major destinations with multi-million-dollar marketing budgets, this investment may be justifiable. For smaller DMOs and travel brands, it could consume their entire technology budget. The resource requirements extend beyond financial considerations to expertise. Custom AI development requires specialized skills in machine learning, natural language processing, and data science—talents that are scarce and expensive. Most DMOs lack these capabilities in-house and would need to contract external vendors, creating dependency relationships. This resource reality suggests a more nuanced approach than "every travel brand needs its own AI." A tiered strategy might include:
  • Tier 1 (Major destinations/brands): Full custom AI development
  • Tier 2 (Mid-sized organizations): Customized implementation of enterprise AI platforms
  • Tier 3 (Smaller entities): Participation in collaborative AI projects through regional tourism associations
This stratified approach acknowledges the resource constraints while still embracing AI's potential benefits. The Fragmentation Challenge: User Experience Considerations One significant counterargument to Borden's thesis is the potential for user experience fragmentation. If every travel brand creates its own AI assistant, travelers planning a single trip might need to interact with dozens of different tools—one for the destination, others for airlines, hotels, attractions, and transportation. This proliferation could create several problems:
  • Tool fatigue: Travelers may resist learning multiple AI interfaces
  • Inconsistent experiences: Varying capabilities across platforms could frustrate users
  • Contradictory information: Different AI tools might provide conflicting recommendations
  • Inefficient planning: Users may need to repeat queries across multiple platforms
A 2023 Expedia Group survey found that 67% of travelers prefer using a single planning tool rather than switching between multiple platforms. This preference suggests travelers might gravitate toward comprehensive AI planning tools like those offered by online travel agencies rather than destination-specific solutions. However, this concern might be addressed through strategic partnerships and API integration. Rather than forcing travelers to use multiple interfaces, travel brands could develop AI capabilities that integrate with popular platforms. For example, a destination's custom AI could be accessible through an airline's planning tool via API calls, creating a seamless experience while preserving data advantages. Environmental and Ethical Considerations The environmental impact of AI proliferation deserves consideration in any strategic discussion. Training custom large language models consumes significant energy resources. According to a 2023 MIT Technology Review analysis, training a mid-sized language model produces approximately 626,000 pounds of carbon dioxide equivalent—similar to the lifetime emissions of five cars. If every travel brand worldwide developed custom AI models, the cumulative environmental impact would be substantial. This concern raises questions about whether the incremental performance improvements of custom AI justify the environmental costs compared to customizing existing models. There are also ethical considerations around data collection and privacy. As Borden notes, custom AI tools gather valuable traveler insights, but this data collection must be transparent and consensual. Travel brands must consider:
  • Clear disclosure of data collection practices
  • Appropriate consent mechanisms for personalization
  • Data minimization principles to collect only necessary information
  • Strong security protections for potentially sensitive travel plans
These considerations don't necessarily argue against custom AI but suggest that implementation should include robust ethical frameworks and potentially favor approaches that minimize environmental impact. Strategic Recommendations: A Balanced Approach Rather than adopting an all-or-nothing stance on custom AI development, travel brands should consider a strategic framework for decision-making:
  1. Assess data advantages: Evaluate whether proprietary data provides sufficient differentiation to justify custom development.
  2. Consider resource constraints: Be realistic about financial and expertise limitations when choosing an AI strategy.
  3. Prioritize user experience: Focus on how travelers want to interact with AI tools rather than organizational boundaries.
  4. Explore partnership models: Consider collaborative approaches that share development costs while preserving brand identity.
  5. Start with customization: Begin with customized implementations of existing platforms before committing to full custom development.
  6. Measure incremental value: Establish clear metrics to evaluate whether custom AI provides sufficient ROI compared to alternatives.
For many travel brands, a hybrid approach may offer the best balance. For example, a destination might implement a customized version of an enterprise AI platform for general inquiries while developing truly custom capabilities only for areas where their proprietary data provides significant advantages. This balanced strategy acknowledges Borden's core insight—that AI's value comes from the data it's trained on—while recognizing the practical constraints that make universal custom AI development unrealistic. Conclusion: Evolution Not Revolution Borden's article provides valuable perspective on AI's future in the travel industry. His core argument that data provides competitive advantage is compelling, and his insights about AI's role in gathering customer intelligence and improving conversion are well-founded. However, the assertion that "every travel brand needs its own AI" overstates both the necessity and feasibility of universal custom AI development. The future of AI in travel will likely be more nuanced, with varying approaches based on organizational size, data assets, and strategic priorities. Travel brands should view AI adoption as an evolution rather than a revolution. Starting with strategic customization of existing platforms, gathering experience and data, and progressing toward more custom solutions as justified by results will yield better outcomes than immediate commitment to full custom development. The key insight isn't that every travel brand needs its own AI but that every travel brand needs an AI strategy that leverages its unique advantages while acknowledging its constraints. For some, that will mean custom development; for others, creative implementation of existing platforms will provide greater value. As travel continues to recover and transform post-pandemic, brands that take this balanced, strategic approach to AI will gain competitive advantage without overextending their resources or fragmenting the traveler experience. For more insights on the role of AI in travel brands, visit this Inc. article exploring these concepts in greater depth.

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