Common challenges in the travel industry and how AI can solve them 

Travel businesses need systems that do the work with faster quotes, smarter pricing, and better traveller experiences.

We’ve mapped 12 key challenges in the travel industry, with real examples of where AI adds value and how we'd approach the build.

1. Fluctuating demand and inaccurate forecasting

Travel businesses face constant swings in demand driven by seasonality, global events, viral trends, or sudden disruptions. Many operators still rely on spreadsheets or static averages to plan marketing, staffing, and inventory leaving them vulnerable to missed opportunities or wasted resources.

AI solution: Predictive demand forecasting

‍AI models can analyse historical booking patterns, real-time web traffic, airline capacity and sales, social sentiment, and global calendars to predict demand for specific destinations, products or dates. These models update continuously, offering more accurate forecasts than manual methods.

Example

A tour operator uses AI to detect a booking spike eight weeks out for New Zealand ski packages. The system recommends boosting paid ads, extending operating hours, and adjusting pricing all before the surge hits.

How we’d implement it

We extract booking data from your systems and combine it with relevant external sources (e.g. flight searches, Google Trends, climate data). Using Azure ML or Power BI Forecasting, we train a machine learning model and present forecasts in a dashboard that’s accessible to sales, ops and marketing, all built using low-code for speed and flexibility.

2. Manual itinerary planning and slow quote generation

Custom quotes for group travel, multi-leg tours or FIT packages often take hours with agents pulling together availability, pricing and options manually. This slows response times, introduces risk of error, and limits scalability.

AI solution: AI-assisted itinerary and quote generation

AI can instantly generate curated trip plans based on customer preferences, group size, budget, and timing. It can recommend relevant flights, transfers, hotels and activities and estimate live pricing based on supplier data.

Example

A wholesaler receives a corporate group enquiry. The system generates three tailored itineraries including flights, 4-star and 5-star hotel options, and activity combinations ready for the agent to review and send in a matter of minutes.

How we’d implement it

We’d build a dynamic quote builder interface using a low-code platform. The interface would pull inventory from your internal database and third-party APIs (e.g. Skyscanner, Expedia Affiliate Network, Rome2Rio), apply business logic and pricing rules, and integrate with an AI recommendation model to suggest options. Agents can personalise and finalise quotes quickly with fewer errors and more consistency.

3. Inconsistent customer service and lack of 24/7 coverage

Travel is a 24-hour experience. When travellers need support, it’s often outside business hours. But not all operators can justify a round-the-clock service desk, especially mid-sized brands or wholesalers.

AI solution: AI-powered virtual agents for support and automation

AI chatbots and digital assistants can manage a wide range of queries, from itinerary lookups and change requests to upselling add-ons and handling disruptions. These assistants integrate with your systems and hand off to humans when needed.

Example

A travel agent integrates a chatbot into their website and WhatsApp. The bot resends itineraries, confirms bookings, suggests airport transfers, and escalates urgent issues to a human agent, significantly reducing email load.

How we’d implement it

We use Microsoft Bot Framework and Azure AI to build natural-language chatbots that connect to your CRM, inventory system or booking engine. These bots can be deployed on your website, in-app, or via channels like WhatsApp or Messenger. Built-in escalation ensures high-priority cases are routed to a live agent when needed.

4. High cancellation and rebooking volume

Unpredictable changes from weather to illness to airline disruptions, result in a high volume of cancellations and rebookings. Handling these manually drains resources, frustrates customers, and increases the risk of policy errors or missed revenue.

AI solution: Rebooking automation and smart refund logic

AI can assess the nature of a cancellation, determine eligibility for refunds or rebookings based on policies, and suggest alternatives in real time including new dates, locations, or product classes.

Example

An airline’s tour partner sees a flight cancellation to a remote destination. AI instantly identifies affected travellers, scans available options and sends rebooking choices via SMS and email, all before the customer has to ask.

How we’d implement it

We’d integrate cancellation and inventory data into a workflow engine. The system uses business rules and machine learning to assess eligibility, match alternatives, and trigger actions (e.g. refund processing, itinerary updates). The agent is looped in only when needed, reducing average handling time significantly.

5. Lack of personalisation in booking or marketing

Customers expect suggestions that match their intent – not generic search results. Most booking systems still surface the same packages to everyone, missing the chance to tailor experiences and increase conversion or upsell.

AI solution: Real-time personalisation and recommendation engines

AI can analyse user profiles, past bookings, browsing behaviour, and inferred preferences to recommend relevant destinations, packages, or upgrades – both in search and during the booking flow.

Example

A returning traveller who previously booked a coastal retreat is now browsing cultural destinations. AI identifies the shift in intent and recommends art-focused city breaks, local experiences and boutique hotels instead of beach packages.

How we’d implement it

We integrate your CRM, website analytics and booking engine with Azure Personalizer or a similar AI model. The front end built in a low-code platform, updates in real time based on user behaviour, adjusting the experience without needing hard-coded rules. Marketing teams can also use this data for personalised email or remarketing campaigns.

6. Disconnected systems and no unified view of the customer

In most travel businesses, customer and operational data is fragmented. Flights, hotels, transfers, support tickets and marketing interactions are often managed in separate platforms. This causes duplicate entries, errors, inefficiencies and a lack of visibility into the full traveller journey.

AI solution: Unified customer view with intelligent data integration and anomaly detection

AI can reconcile and unify customer records across systems by matching identities, cleaning inconsistencies and resolving duplicates. It doesn’t just sync data, it applies intelligence to highlight gaps, validate entries, and build a real-time, centralised view of the traveller’s full history.

Example

A travel group managing multiple brands, flights, accommodation, tours uses AI to consolidate bookings, support logs, contact history and marketing interactions into a single traveller timeline. Agents can see everything at a glance, and automated systems can trigger relevant offers or alerts based on the complete profile.

How we’d implement it

We map your systems, CRM, booking engine, support platform, marketing tools and connect them using low-code integration. AI models match and enrich records, flag anomalies (e.g. mismatched names, double bookings), and maintain a clean, unified customer database. This single view is accessible through a secure dashboard for sales, ops and service teams and supports automation and personalisation across the business.

7. Sustainability pressure from travellers

Travellers are increasingly conscious of the environmental impact of their choices. Many now seek out providers who can offer lower-emission options, eco-accredited properties, or offset programs. But most travel companies can’t easily calculate, surface or personalise these sustainability insights even if they care deeply about the issue.

AI solution: Emissions modelling and eco-ranking

AI can calculate the estimated carbon footprint of each booking by analysing transport mode, travel distance, class of service, and accommodation profile. It can then present alternatives with lower impact, or highlight products that align with sustainability criteria, enabling customers to make informed choices.

Example

A travel app allows users to filter search results by estimated carbon impact. It recommends hotels with environmental certifications, shows greener flight options, and offers a pre-selected carbon offset option at checkout helping users reduce their footprint without compromising their plans.

How we’d implement it

We integrate third-party data from sources like Google’s Travel Impact Model, CHOOOSE, or ICAO carbon calculators. These are layered into the booking flow using low-code tools like OutSystems. Each result, whether flight, hotel, or activity is tagged with emissions metadata and sustainability ratings. We also offer toggles for automatic carbon offsetting and ESG-friendly travel packages, giving customers more control without complicating the booking process.

8. Limited use of user-generated content to inspire or customise trips

Travellers trust other travellers more than marketing copy. But most travel businesses underutilise the vast amount of user-generated content available such as reviews, photos, social media, testimonials to drive engagement or help customers build more personalised itineraries.

AI solution: Curated trip inspiration powered by user content

AI can scan and categorise user-generated content from TripAdvisor reviews to Instagram captions and use that data to build dynamic trip suggestions or “build your own adventure” options. This turns real customer stories into live itinerary inspiration.

Example

A tour platform pulls in Instagram and Google Reviews data for locations and experiences, and uses AI to surface the most relevant, positive and locally trending ideas based on user interest (e.g. food, hiking, family travel). Travellers can then save and assemble these into their own custom trip.

How we’d implement it

We build a content discovery interface on top of your booking engine, layered with a natural language processing model trained to classify and rank public content by theme, tone and relevance. We can pull in live content from public APIs or work with your own review data. The experience is delivered via a low-code web or mobile app, integrated with your inventory for real-time booking.

9. No way to help travellers avoid peak times at busy places

Travellers often want to explore popular landmarks without the stress of long queues, overcrowded tours or overbooked experiences. But most travel platforms can’t offer guidance on the best time to go, or alternative options, because they lack access to live crowd data or predictive insights.

AI solution: Crowd forecasting and off-peak optimisation

AI models can forecast crowd levels at key attractions, transport hubs and tourist areas using historical footfall data, weather, booking patterns and even public events. These insights help travellers avoid peak periods or choose alternatives, improving their experience and reducing pressure on staff and infrastructure.

Example

A city tour app suggests the best time to visit popular sites like the Eiffel Tower or Bondi Beach, based on historical crowd data and live inputs. It automatically recommends early or late visits, alternative days, or quieter nearby attractions.

How we’d implement it

We aggregate third-party datasets (from sources like Google Popular Times, social media check-ins, weather APIs and historic ticketing volumes) and train an AI model to forecast future crowd density. These insights are then embedded into booking workflows, itinerary builders or even push notifications via a custom app interface – all built using a low-code platform like OutSystems for rapid deployment.

10. Missed opportunities to offer the next logical step in a traveller’s journey

Many travel booking systems stop at a single transaction such as a tour, a flight, or a hotel. But customers often need more: airport transfers, flights that match tour start times, pre- or post-tour accommodation. When these aren’t offered automatically, opportunities are lost and customers are forced to look elsewhere.

AI solution: Intelligent next-step recommendations and itinerary completion

AI analyses the context of each booking and predicts what a traveller might need next  offering matching services like airport transfers, connecting flights, travel insurance or hotel stays. It ensures each customer has a complete, seamless journey and increases revenue per booking.

Example

A traveller books a five-day Northern Territory tour. AI suggests flights that land in Darwin 24 hours prior, two nights’ accommodation near the pickup point, and a transfer from the airport, all matched to availability and preferences. The agent can confirm all extras in a few clicks.

How we’d implement it

We use a recommendation engine trained on past booking flows and product combinations, layered into your CRM or booking system via low-code integration. The system can be agent-facing (B2B) or customer-facing (B2C), surfacing live inventory via API and suggesting next steps within the booking flow. The engine is rules-based with machine learning refinement, ensuring both business priorities and customer preferences are considered.

11. Static itineraries that miss critical events, risks, or opportunities

Traditional itineraries are fixed the moment they’re issued but the world keeps changing. Travellers often miss out on experiences (like festivals or major events) because they weren’t surfaced during planning. Worse, they may be unaware of public holidays, political unrest or incoming weather disruptions that affect their trip.

AI solution: Dynamic itinerary augmentation and advisory integration

AI enables itineraries to evolve in real time. By monitoring a traveller’s planned location and timing, AI can surface helpful insights (like local events or holiday closures), enrich the experience with suggested activities, or flag emerging risks like severe weather, strikes or unrest. It turns a static plan into a responsive, intelligent travel assistant.

Example

A traveller books a Tokyo tour in April. AI detects their dates align with a major sumo tournament and cherry blossom festival and adds these to their itinerary. It also flags a public holiday that may affect local transport and suggests alternatives. If a typhoon forms, the itinerary updates with a warning and contact details for rescheduling.

How we’d implement it

We connect your itinerary system to AI services that pull data from public events calendars, global travel advisories, social media trend analysis, and weather APIs. This information is filtered through a relevance engine (based on location, timing and preferences) and served via an agent dashboard or customer-facing app – all powered by low-code tools like OutSystems.

12. Limited visibility of traveller location in an emergency

When incidents occur from natural disasters to political unrest, travel companies need to act fast. But most don’t have accurate, real-time visibility of where travellers are. Without that, they can’t issue personalised alerts, offer assistance or guide travellers to safety. In critical moments, that puts people  and reputations at risk.

AI solution: Real-time traveller location intelligence and alert automation

AI helps travel operators maintain a dynamic view of where travellers are, based on GPS, itinerary data and real-time check-ins. If an incident occurs in a specific location, AI can instantly identify affected travellers, push personalised alerts, and guide them with verified safety instructions – all without needing to manually track every trip.

Example

A political protest escalates near a hotel in Santiago. AI immediately identifies which customers are nearby, sends emergency push notifications to those guests, and recommends a safe route to an alternate location. Agents are alerted automatically and escalation protocols are triggered.

How we’d implement it

We use geofencing and location tracking (opt-in, privacy-respecting) linked to live itinerary data. AI monitors travel advisories, news feeds, embassy alerts and local social media to detect incidents and assess relevance. Alerts are distributed through a mobile app, WhatsApp or SMS built using low-code tools that integrate directly into your travel CRM or itinerary manager.

Ready to take your travel business further with AI?

Talk to our team about practical ways to streamline operations, personalise experiences and build smarter systems – faster.