Smarter Journeys: AI is Impacting the Future of Travel

Most organisations know that AI matters, but many are still trying to understand what it can deliver for them. The result is a growing gap between ambition and action. Leaders hear about generative AI, personalisation and automation, but struggle to translate those themes into concrete use cases for their own organisation. At the same time, no one wants to be the company that realises too late that competitors have quietly used AI to create a better customer experience, lower their costs and gain market share.

The travel industry is a useful example of how the potential of AI can come to life in practice. From personalisation to automation, it helps illustrate how these capabilities could address real challenges and create meaningful value.

Closing this gap doesn’t start with buying tools. It starts with improving AI literacy and building a clear understanding of the art of the possible – drawing on solid examples, including those emerging from the travel sector, to show what strong adoption can look like in practice.

What do we mean by AI Literacy in the Travel Industry?

AI literacy is a set of competencies that enable organisations to understand, use and critically evaluate Artificial Intelligence (AI) technologies and their social, ethical and practical implications. 

For travel organisations, that means having enough understanding of the main types of AI capability, being able to recognise realistic use cases in their own customer journeys and operations and being confident in challenging vendors and internal teams on value, risk and feasibility. 

The aim isn’t to turn leaders into data scientists, but to build their expertise in recognising what AI can do in the travel industry and make them confident answering questions like:

• Where could AI reduce friction in the booking journey on our travel website?
  
  

• Which workflows in customer support or operations are repetitive enough to automate safely?
  
  

• Which decisions (pricing, staffing, disruption) could be improved with better prediction?
  
  

• Where would AI create regulatory, privacy, or brand risk if it gets things wrong?
  
  

Start With the Art of the Possible

A practical way to build AI literacy is to anchor it in real use cases rather than abstract concepts. In travel, there are rich opportunities to apply AI across areas such as content safety, speech, natural language, machine learning and vision. 

These categories provide a simple lens for understanding the art of the possible: 

What it would mean for your organisation if you could automatically read and classify reviews at scale; let customers ask for what they want in their own words; predict demand with more accuracy; or understand how people flow through your spaces in real time. 

The next step is to bring technical understanding together with focused organisational analysis. Work with stakeholders to map out customer journeys, internal workflows, pain points, and cost drivers. At each stage, consider where AI could streamline operations, enhance user experience, or support smarter decisions

This is also where it helps to distinguish between copilots and agents. Generative AI has mostly behaved like an adviser because it responds when prompted, but agentic AI goes further by planning multi-step tasks, calling tools and APIs, and executing actions with limited oversight. 

In the travel industry, that matters because many of the most painful moments are not about getting information, they are about getting things done across multiple systems, such as rebooking during disruption, issuing vouchers or processing refunds, changing itineraries and notifying suppliers, or reassigning rooms based on preferences and availability, in near real time. 

Once you’ve mapped those moments, you can rank initiatives by strategic objective, organisational value, deliverability, and risk, rather than by how innovative they sound. 

A Non-negotiable Foundation: Data governance

Before charging ahead, many organisations need a reality check. Effective AI relies on some simple but non-negotiable data foundations: clear data ownership, defined standards, trusted data sets and controlled access to sensitive information. 

If your data is poor quality, inconsistently managed or scattered across platforms with no clear responsibility, AI will be a liability, not an asset. 

Without strong data governance, there is a real risk that inaccurate or biased models will drive bad decisions, that inconsistent data will feed different answers to different teams and that weak controls around privacy and consent will create regulatory and reputational exposure. 

Good governance means defined ownership, clear policies, quality controls and transparent lineage from source to impact. It also means being able to explain how data has been used to train models and how decisions are made. Only once this foundation is in place does it make sense to scale AI with confidence. Without it, any success will be fragile and hard to repeat.

How AI Creates Value In Travel

Content Safety: Learning from what customers are already telling you

User generated content is both an asset and an early warning system. Reviews, comments and photos influence buying decisions and reveal issues long before they appear in formal reports. AI-driven content analysis can scan social media and review platforms at scale, spotting patterns such as repeated complaints about cleanliness, delays or safety concerns. 

Trends in sentiment and language highlight problems early so teams can intervene before they become reputational risks. For the organisation, this means earlier detection of issues, more focused service effort and a reduced impact on your brand. For travellers, it means faster resolution of real problems, visible responsiveness and more reliable experiences.

Speech: Making travel more accessible and immersive

AI speech tools do more than just translate,  they help narrate, guide and support travellers at every stage of the journey, especially those with accessibility needs. Hotels, attractions and destinations can offer audio descriptions of facilities, artefacts and locations for visually impaired travellers or for anyone who prefers audio to text. 

Content can be translated and delivered through apps and wearables in the traveller’s preferred language. For the organisation, this broadens the addressable market, strengthens accessibility credentials and differentiates the experience. For travellers, it creates more inclusive journeys, a greater sense of independence and a richer connection with the places they visit.

Natural Language: Letting customers ask in their own words

Natural language capabilities allow customers to interact with travel brands in the way they naturally think and speak. Instead of rigid filters and drop-down menus, customers can describe what they want: a child-friendly beach resort with late checkout, museums near their hotel that offer skip-the-line tickets, or multi-centre itineraries that balance cost and comfort. 

AI-driven chat and search can interpret these requests, surface relevant options and handle common queries without forcing customers into narrow paths. When issues are more complex, the same tools can capture context before handing over to human agents. Organisations benefit from higher conversion rates, reduced contact centre load and a clearer picture of customer intent. Travellers enjoy less friction, faster answers and a sense that the service actually understands what they are asking for.

Machine Learning: From generic offers to meaningful personalisation

Machine learning is the engine that turns data into predictions and recommendations. On the customer side, it can power personalisation that feels genuinely helpful rather than intrusive. Models can learn from preferences and past behaviour to surface relevant destinations, ancillaries, upgrades and on-trip activities at the right moment, not as an endless stream of generic offers. 

On the operational side, machine learning can improve demand forecasting, staffing plans, pricing decisions and fraud detection. These may be less visible to the end user, but even modest improvements in these areas can translate into significant financial value at scale. Organisations gain increased revenue per customer, better utilisation of assets and reduced losses, while travellers see smoother operations, fewer disruptions and services that feel more reliable.

Vision: Understanding what is happening in real time

Vision-based AI can help travel organisations understand what is happening in real time in their physical environments. Advanced image recognition systems can analyse video feeds to track queues, measure footfall and identify bottlenecks in airports, stations, hotels and attractions. This insight helps teams deploy staff more effectively, re-design layouts and improve safety. 

For organisations, the result is better use of space and people, improved safety and smoother operations. For travellers, it translates into shorter queues, easier navigation and experiences that feel designed rather than accidental.

Turning Possibilities into Competitive Advantage

Seeing the art of the possible is only the first step. To turn potential into progress, organisations need to move from ideas to structured experimentation. That means developing AI literacy across leadership, product and delivery teams so they can confidently explore opportunities and make informed choices. It requires organisational analysis to identify and prioritise use cases that clearly link to outcomes, and running controlled experiments with measurable goals, sound governance and clear accountability. When use cases succeed, they should be embedded into products, services and ways of working, not left as standalone pilots.

At the same time, data governance must move forward with AI adoption, tightening definitions, ownership, controls and monitoring, so that, as systems become more advanced, they remain explainable in practice, secure by design and managed as part of a disciplined control framework. 

For travel organisations, the potential rewards are significant. AI can enable services that are more responsive and inclusive, operations that are leaner and more resilient, and decisions that are both faster and better informed. In practice, those gains will belong to organisations that act now: investing in understanding what is possible, firming up their data foundations and running focused experiments where AI can prove its worth.

Looking to adopt AI? Our data readiness assessment for AI helps you understand where you stand and what is needed to get value from AI. 

To get started, schedule a consultation with our Data Solutions Experts.