AI could weaken travel operators’ margins without changing bookings

Stable booking shares can hide a changing travel business

A hotel operator could maintain its current share of direct bookings as AI transforms travel purchasing and still become less profitable. Customers might keep booking through the same mix of direct and indirect channels while AI platforms gain influence over how they discover hotels, compare prices, and decide where to book. Distribution reports would look stable, even as customer acquisition costs and dependence on intermediaries increased.

Direct bookings generally give travel operators higher margins and greater access to customer information. Indirect bookings, through online travel agencies and other intermediaries, usually carry lower margins but remain important for reaching customers and maintaining occupancy. The balance between these channels is therefore a familiar measure of distribution performance. Yet it shows where transactions happen while revealing much less about the commercial relationships behind them.

AI could widen that gap by changing several stages of travel purchasing. Generative AI, which produces responses and recommendations from user requests, already helps travelers research destinations, compare accommodation, and plan itineraries. As these systems develop, the companies operating them could gain more influence over customer acquisition and eventually booking transactions.

Travel’s history offers reasons to be cautious about predictions of disruption. The industry has experienced major technological changes while maintaining a broadly stable balance between direct and indirect bookings. Post-Covid growth has also slowed amid economic weakness and volatility, making further technology investment harder to justify.

But historical stability deserves closer examination. Travel operators spent heavily to defend their customer relationships during earlier digital transitions, and AI gives other companies new ways to influence demand. For executives deciding how to respond, the central question is whether existing distribution arrangements can keep producing profitable bookings on acceptable commercial terms.

Travel’s last digital transformation required active defense

The strongest argument for waiting comes from the industry’s experience with e-commerce. Over roughly 15 years, customers moved heavily toward online purchasing, yet the overall balance between bookings made directly with travel operators and those handled by intermediaries remained broadly consistent.

Bain & Company and Euromonitor found that online bookings accounted for 43% of travel bookings in 2011. By 2025, the share had reached an estimated 70%.

The comparison appears in Figure 2, titled “For travel operators, the balance between direct and indirect bookings has held steady as e-commerce evolved.” Bain & Company and Euromonitor provide the underlying data. Their methodological note states that bar totals may differ slightly from the sum of their component segments because of rounding.

These figures show a major change in purchasing behavior without a comparable shift in the industry’s overall distribution structure. An executive could reasonably see this history as evidence that travel’s commercial relationships can absorb another technological transition. But the rise of online travel agencies shows why stable channel shares tell only part of the story.

Digital intermediaries changed how customers reached operators

Online travel agencies expanded the role of intermediaries by bringing accommodation choices, prices, availability, and booking functions together in one place. Customers could compare competing offers more easily, giving these platforms greater influence over which properties attracted attention. Operators consequently had to compete for visibility within services that increasingly shaped purchasing decisions.

Mobile commerce extended that competition by making travel research and purchasing available throughout the day. Social media opened another route for travelers to discover destinations, hotels, resorts, and other experiences. Together, these developments changed how customers found operators and evaluated their offerings before making reservations.

A booking recorded as indirect in 2025 could therefore follow a very different customer journey from one recorded in 2011. Direct bookings also changed as travelers encountered brands through multiple digital services before eventually reaching an operator’s website. The final transaction channel remained measurable, but the activities influencing that transaction became more complex.

This created a commercial challenge for operators. Intermediaries could strengthen their role in discovery and comparison without necessarily increasing their share of completed bookings. Defending direct channels required operators to improve their own customer acquisition, marketing, and purchasing experiences.

CRM investment helped operators defend customer relationships

One important response was heavy investment in customer relationship management (CRM) software. CRM systems organize customer information and help businesses coordinate marketing, understand preferences, and maintain relationships across repeated interactions. For hotel operators, these capabilities made it easier to communicate with previous guests and encourage them to return directly.

A hotel brand, for example, could use a returning customer’s booking history and preferences to provide more relevant offers or loyalty communications. These interactions helped direct channels compete with the convenience and reach of online intermediaries. As digital competition increased, customer information and the ability to use it became more valuable commercial assets.

The connection between this investment and distribution stability matters. CRM was one of several ways operators adapted as e-commerce developed, alongside changes in marketing, distribution, and customer engagement. Maintaining an existing commercial position can require substantial investment even when the headline result barely changes.

That spending also affected profitability. A direct booking avoids some intermediary costs, but attracting and retaining direct customers still requires investment in technology, marketing, loyalty programs, and service. Two operators with identical direct booking shares can therefore have different acquisition costs, retention rates, and levels of dependence on outside platforms.

Over three decades of e-commerce development, operators have repeatedly adapted to new purchasing behavior. Online booking, mobile commerce, and social media all changed the work needed to maintain profitable customer relationships. AI presents another potential shift, with the added possibility that software could influence both a customer’s initial choices and the eventual transaction.

AI is changing travel discovery before booking distribution

AI’s immediate influence comes from making complicated travel research easier. A traveler can ask a system to recommend destinations, compare accommodation, or prepare an itinerary based on specific requirements. The system can organize information that would otherwise require several searches and visits to different websites.

This convenience gives AI platforms an opportunity to influence which destinations, hotels, resorts, cruises, and attractions customers consider. A useful recommendation can direct attention toward particular operators before the customer reaches a booking service. The earliest commercial effects may therefore appear in website referrals and research habits.

AI referrals are growing rapidly from a small base

Similarweb data shows that generative AI platforms such as ChatGPT are becoming a growing source of visits to travel websites. Over a one-year period, AI-referred traffic increased substantially across the travel categories examined.

The findings appear in Figure 1, titled “For travel websites, the share of traffic referred by generative AI has surged recently, albeit from a low base.” Similarweb tracked three to five operators in each travel website category. Its methodological note also states that bar totals may differ from the sum of individual segments because of rounding.

The low starting point matters when interpreting the increase. A fourfold rise in referral share represents rapid growth, but AI referrals still account for a relatively small part of travel website traffic. The finding shows that a new route to travel websites is taking shape.

For the hotel operator introduced earlier, this changes the customer acquisition problem. A prospective guest might previously have discovered the property through conventional search or an online travel agency. Increasingly, that guest could first encounter the hotel through an AI-generated recommendation, making the information available to AI systems relevant to future demand.

Frequent users are developing new research habits

Referral data measures visits arriving at websites, while consumer research helps explain how people use AI during travel planning. Bain’s Consumer Lab examined consumers who had already used AI to help research and book travel. Among those users, frequent reliance on AI was substantial.

These results show substantial reliance on AI among existing users in both the United States and the European markets surveyed. They measure frequency of use among people already familiar with AI-assisted travel, so they capture different behavior from Similarweb’s website referrals.

Frequent use matters because repeated interactions can establish new research habits. Travelers accustomed to receiving useful recommendations may increasingly begin planning within an AI interface. When the system provides a shortlist of suitable accommodation, customers may spend less time evaluating alternatives themselves.

This could shift influence toward platforms involved at the beginning of the purchasing journey. Operators would still compete on price, quality, and suitability, but AI platforms could play a larger role in deciding which options customers consider. That influence would create commercial value for the companies operating these platforms, especially if their services later extend into transactions.

Booking functionality is developing, but adoption remains early

The next stage is already beginning. Google has added travel booking functionality to its search AI mode, extending AI-assisted discovery toward purchasing. Google has a commercial interest in this development because keeping consumers within its search and AI services can strengthen its position in travel discovery and transactions.

The functionality also shows how an established technology company can use existing consumer relationships to enter additional stages of travel purchasing. A traveler who begins with an AI-assisted search could eventually compare options and complete more of the transaction within the same environment. This would give the platform influence over a larger part of the customer journey.

Several obstacles still limit this development. AI systems can provide incorrect information about destinations, properties, availability, and other travel details. Fragmented interfaces can also make moving between planning, comparison, and purchasing difficult.

Meanwhile, a single dominant AI application for travel has yet to emerge. End-to-end autonomous booking, in which software independently completes a transaction on a customer’s behalf, remains at an early stage. These limitations help explain why the clearest current evidence concerns AI-assisted research and discovery.

Yet influence over discovery can have commercial consequences before autonomous booking becomes common. A platform that repeatedly directs customers toward particular travel providers may become an important source of demand. If it later takes on a larger role in transactions, that position could strengthen its negotiating power with operators and existing intermediaries.

AI could weaken distribution economics without changing channel shares

The commercial risk begins where AI’s influence over customers meets the economics of distribution. An operator could preserve its current direct booking share while becoming more dependent on companies that influence indirect demand. Alternatively, AI could encourage more customers to complete transactions through intermediaries, increasing the proportion of lower-margin bookings.

These developments would affect profitability in different ways. The first changes the commercial conditions surrounding an apparently stable distribution mix. The second changes the mix itself, potentially reducing margins and limiting access to customer information.

AI platforms could gain leverage within existing channels

Consider the hotel operator maintaining its existing direct booking share. Its website continues generating reservations, while online travel agencies and other intermediaries handle roughly the same proportion as before. Conventional distribution reporting would suggest its competitive position had remained stable.

Now suppose AI platforms become important sources of hotel recommendations before customers complete bookings through those intermediaries. The platforms could influence which properties receive attention, how competing offers appear, and which booking routes customers follow. Their involvement would add another commercially significant relationship to the distribution process.

The operator might then face higher customer acquisition expenses or stronger demands from distribution partners, even though the final channel mix remained unchanged. A platform responsible for directing substantial demand could gain leverage over businesses seeking access to those customers. The hotel’s distribution costs and negotiating position would increasingly depend on arrangements beyond its direct control.

These effects remain prospective. Commercial relationships between AI platforms, operators, and established intermediaries are still developing, as are the effects on industry margins. Nevertheless, the mechanism matters because a booking’s profitability depends partly on the cost and conditions of acquiring the customer.

Google’s position illustrates the value of discovery

Google provides an established example of how influence over customer discovery can become commercially important. Its position in search means travel operators already consider Google when planning marketing and customer acquisition. Companies seeking search traffic have a commercial relationship with a platform that influences which businesses consumers find.

AI services could develop similar influence if travelers increasingly use them to research and purchase trips. Combining recommendations, comparisons, and transaction assistance would allow a platform to participate in several stages of demand generation. As more customers rely on that service, access to its users could become more valuable.

Several distribution arrangements are possible. AI companies might refer customers directly to operators, send them through established online travel agencies, or become more involved in handling reservations themselves. Each arrangement would divide customer access, transaction responsibilities, and commercial influence differently.

Operators therefore need to understand who generates demand within each channel. A single indirect booking percentage cannot distinguish between customers acquired through existing intermediaries and those whose choices were shaped by a new AI platform. That difference could become important when negotiating commercial terms.

AI agents could increase pricing pressure

A related risk emerges as AI systems become capable of carrying out more purchasing tasks. An AI agent is software that performs a sequence of actions on a user’s behalf, such as searching across providers, comparing suitable options, and narrowing a shortlist. Agentic AI describes systems that carry out connected tasks with increasing independence, while agentic commerce applies those capabilities to purchasing.

In travel, an agent could help a customer compare accommodation across several providers and booking channels. A budget-conscious traveler might ask it to identify the lowest-priced property that meets specific requirements. The agent could evaluate room types, amenities, booking conditions, and prices before recommending suitable options.

As these comparisons become easier, customers may see differences between competing offers more clearly. That could increase price pressure on operators whose products appear similar based on the information an agent uses. Hotels with comparable locations, room types, facilities, and service levels may face stronger competition on price.

The problem becomes harder when valuable differences are difficult to represent in product information. Service quality, atmosphere, personal attention, and distinctive experiences can justify higher prices, but an AI system may struggle to evaluate them accurately. Commoditization occurs when customers begin treating differentiated offerings as interchangeable, making price a more important basis for selection.

The eventual effect will depend partly on how AI capabilities develop. Agents may become better at understanding qualitative preferences, and customers with strong brand preferences may continue choosing familiar operators. Even so, easier comparison gives operators a reason to make both their pricing and distinctive value clear to AI systems.

Greater intermediary dependence could weaken customer relationships

Pricing pressure could be accompanied by reduced access to customer information if more bookings move through third parties. A traveler might research a trip through AI and complete the reservation through an AI platform or online travel agency. The hotel would receive the booking, but an intermediary could control more of the information generated during discovery and purchasing.

This matters because operators use first-party data, information collected directly through their own customer interactions, to understand and retain guests. Booking histories, stated preferences, loyalty activity, and previous stays can help a hotel recognize returning customers and communicate more effectively. Access to this information supports personalized marketing and service.

When intermediaries control more customer interactions, operators may find it harder to maintain the same understanding of their guests. For example, a hotel brand can use previous booking information to make relevant offers to returning customers. If a growing share of reservations originates through third parties, reaching those customers directly may become more difficult.

The financial consequences extend beyond the commission attached to an individual reservation. Weaker direct relationships can increase reliance on paid customer acquisition and intermediary demand. Over time, that dependence may affect retention costs and the operator’s ability to encourage repeat bookings.

Existing online travel agencies could also become stronger

AI creates opportunities for established travel intermediaries as well as new technology platforms. Online travel agencies already have consumer relationships, booking infrastructure, and access to large numbers of travel offerings. Adding AI-powered planning and purchasing tools could make their services more convenient and encourage customers to complete more of their purchasing journey within these platforms.

A traveler could use an online travel agency’s AI assistant to choose a destination, compare accommodation, prepare an itinerary, and complete reservations in one environment. That convenience could strengthen the agency’s relationship with the customer and increase the value of its services to operators.

Online travel agencies have a direct commercial interest in this outcome. Greater customer engagement and booking activity can strengthen their distribution businesses and negotiating positions with travel suppliers. Their incentives may therefore differ from those of hotel operators seeking to build direct customer relationships.

New AI platforms and established agencies could compete for control of the same customer interactions. They might also work together through referral or transaction arrangements. Either development would make it important for operators to understand which partners control demand and what those relationships cost.

Indirect distribution will remain valuable because it helps operators reach customers and maintain occupancy. The commercial challenge is to preserve profitable access to that demand as the companies and technologies supporting distribution change.

Two scenarios require different management responses

These risks produce two scenarios with different implications for management.

In the first scenario, executives need to examine the economics within existing channels. Acquisition expenses, intermediary terms, pricing performance, and the companies responsible for generating demand become important indicators. Direct booking share alone could remain stable while these measures deteriorate.

In the second scenario, management must also respond to booking volumes shifting toward indirect channels. This would make channel mix a more immediate financial concern, alongside customer data access and retention. The response could require stronger direct-channel incentives or changes to intermediary arrangements.

Neither scenario is inevitable, and AI’s eventual effect on travel margins remains uncertain. Their value lies in identifying different ways distribution economics could change. Operators can use this distinction to decide which risks they face and which investments would address them.

Operators need defenses that match their commercial position

The appropriate response begins with how each operator currently attracts customers and earns profitable repeat business. A hotel group with substantial direct traffic and an established loyalty program faces different challenges from a smaller brand that relies heavily on intermediary bookings. Their options also differ according to brand strength, inventory, customer relationships, and negotiating power.

Executives planning customer engagement over the next five to ten years should therefore assess how exposed their businesses are to AI-driven changes in demand. They need to understand the financial effect of a possible decline in direct bookings, the opportunities created by new discovery channels, and the costs of accessing those customers.

Those findings should guide investment in AI discoverability, pricing information, direct booking experiences, personalization, and distribution partnerships. Each capability addresses a particular commercial problem, so priorities should follow the operator’s existing strengths and weaknesses.

Make travel offerings visible in AI-assisted discovery

The most immediate task is to understand how an operator appears when travelers use AI to research trips. Traditional search engine optimization (SEO) aims to improve visibility in conventional search results. AI-powered search creates another form of discovery because users may receive generated recommendations instead of reviewing a list of website links.

Many of these responses come from large language models, AI systems that interpret requests and generate text. Different platforms can draw on different information sources and retrieval methods to construct their answers. Operators therefore need to understand how their properties, services, and prices appear in the information these systems can access.

That work includes content on operators’ own websites and information supplied through relevant third-party platforms. A hotel, for example, should assess whether AI systems can accurately identify its location, accommodation types, facilities, services, and suitability for particular travel occasions. The same principle applies to resorts, theme parks, and cruise operators.

Accuracy matters especially when customers have specific needs. A family researching a holiday may ask an AI system to recommend accommodation with suitable facilities and experiences. A resort seeking family travel demand benefits when those characteristics are accessible and accurately represented in the information the system uses.

Other operators may target customers looking for a particular type of experience. Their content should explain what makes the offering suitable for those travelers. This helps AI systems connect the product with relevant requests and gives customers a clearer reason to consider it.

The goal is commercially useful visibility. An operator gains more from appearing in recommendations that match its strengths than from attracting large amounts of poorly qualified traffic. Customer segments, travel occasions, and existing competitive advantages should therefore guide content priorities.

This requires marketing teams to develop new skills. Teams accustomed to conventional search rankings will need to investigate how AI platforms select information, generate recommendations, and differ from one another. As AI becomes more important in customer acquisition, marketing spending may gradually shift toward these platforms.

Operators should monitor that change alongside the quality of demand it produces. Relevant traffic, booking conversion, and acquisition economics will help determine whether AI-generated visibility contributes to profitable growth.

Make pricing easy for AI agents to interpret

Once an offering appears in AI-assisted discovery, its pricing must also be understandable. An agent comparing accommodation may evaluate total cost, included services, booking conditions, and suitability for a particular trip. Unclear or difficult-to-access information could make an otherwise attractive property harder to assess.

Operators can address this by presenting prices and relevant conditions consistently across the channels they manage. Clear information helps agents compare offers accurately and gives customers a better understanding of what they are purchasing. It also allows operators to explain differences between apparently similar options.

Greater transparency could make differences between direct and intermediary offers easier to identify. A customer may see that one channel offers a lower price while another includes additional services or benefits. Operators will need to consider how these offers appear when an AI system compares them side by side.

The consequences will vary by customer segment. Price-sensitive travelers may respond strongly to cheaper alternatives, while others may value trusted brands or particular experiences. Pricing decisions should therefore account for both competitive cost and the characteristics that support premium positioning.

Give customers stronger reasons to book directly

Better AI visibility can attract prospective customers, but direct channels still need to convert that interest into bookings. The purchasing experience should be convenient, with clear benefits that encourage customers to complete reservations through the operator. Personalized offers, loyalty programs, and effective service can all support that decision.

Convenience becomes more important as customers grow accustomed to AI systems that simplify research and comparison. A traveler who has quickly identified suitable accommodation through AI may have little patience for a complicated booking process. Unnecessary searching, repeated information entry, or unclear choices could make an intermediary more attractive.

Customer-facing AI agents offer one possible response. A hotel could introduce an assistant that helps visitors identify suitable rooms, answers questions about services, and guides them through reservations. A resort or cruise operator could provide similar assistance when customers compare packages and experiences.

These tools could make direct interactions easier while allowing the operator to maintain its customer relationship. Their value, however, depends on the problem they solve. A business with substantial website traffic but weak booking conversion may benefit from improving the purchasing experience, while one struggling to attract suitable visitors may need to prioritize discovery.

Existing customer relationships can also guide investment. An operator with a strong loyalty program might gain more from improving personalization than from building a broad new AI interface. The decision should reflect how customers currently interact with the business and where profitable demand is being lost.

Use personalization to build stronger brand preference

Direct booking competitiveness also depends on the experience customers expect from the brand. Hotels, resorts, theme parks, and cruise operators create value through service, personal attention, and memorable experiences. These qualities can influence whether travelers seek out the same operator when planning another trip.

AI could strengthen that advantage by helping businesses use customer information more effectively. Operators already collect preferences through loyalty programs, previous visits, and direct interactions. Making that information easier for employees to access can improve how they prepare for and respond to customer needs.

For example, a hotel could use a returning guest’s known preferences to tailor communications before a stay and help staff prepare suitable services. The technology would help employees deliver personal attention. This approach preserves the human element of hospitality while making customer knowledge more useful.

Some operators may hesitate to introduce technology into customer-facing services because personal interaction is part of their appeal. That concern makes the design of AI-supported processes important. Technology should help employees provide relevant service while allowing guests to receive the experience they value.

Better experiences can also affect future distribution economics. A traveler with a strong preference for a particular hotel or resort can express that preference during AI-assisted planning. Such customers may remain interested in the brand even when an agent presents cheaper alternatives.

This creates a possible counterweight to commoditization. AI could make standardized travel products easier to compare on price, while helping operators create more distinctive experiences through customer knowledge. The commercial opportunity lies in using personalization to build preferences that remain meaningful across changing purchasing interfaces.

Large operators may be able to negotiate AI distribution terms

Beyond their own customer channels, operators must decide how to work with AI platforms and established intermediaries. Those decisions depend heavily on bargaining power. Large businesses with strong brands, substantial inventory, and direct customer demand may have more options than smaller competitors.

One possible strategy is to restrict access to selected inventory through certain AI search or distribution partners. An operator with strong direct demand could reserve some offerings for its own channels or for intermediaries that meet specific commercial conditions. Such restrictions could protect negotiating leverage, although they might also reduce demand from customers who prefer other platforms.

Large operators could also explore joint ventures with AI companies. A partnership might provide greater influence over how travel products are presented, how transactions are handled, or how commercial value is shared. Its appeal would depend on the importance of the operator’s inventory to the platform and the terms each party could negotiate.

These arrangements remain potential options rather than established solutions. Their commercial value would depend on market conditions, customer behavior, and the strength of the operator’s position. Management should assess where access restrictions or partnerships could improve distribution economics and where they would create unnecessary barriers to demand.

Smaller operators need focused distribution strategies

Smaller brands generally have less influence over commercial terms set by major technology companies and intermediaries. Limited inventory, lower booking volumes, or weaker brand recognition can make special access arrangements and joint ventures harder to negotiate. Their response will often depend more on improving performance within existing distribution channels.

Relevant AI discoverability is one priority. A smaller operator with a distinctive product can benefit when AI systems accurately communicate the qualities that appeal to its target customers. Clear pricing, accessible product information, and convenient direct booking experiences can also help it compete for suitable demand.

Differentiation becomes especially valuable when an operator serves customers with specific preferences. A distinctive resort, for example, may have a stronger position when travelers seek the experience it provides. The challenge is to make those characteristics understandable to both customers and AI systems.

Investment should then be evaluated through commercial results. Operators can examine the quality of AI-generated traffic, booking conversion, customer retention, and the costs associated with each distribution channel. These measures help distinguish useful exposure from activity that attracts attention without producing profitable bookings.

The right balance will differ across business models and customer segments. A smaller operator heavily dependent on intermediaries may prioritize improving direct customer relationships, while another may gain more from reaching new audiences through AI-assisted discovery. In both cases, decisions should follow the economics of the business.

Build-versus-partner decisions should follow commercial priorities

Differences in scale and bargaining power lead to a broader technology decision: which AI capabilities should operators own, and which should they obtain through partners? Customer-facing assistants, content management, personalization, and AI discovery monitoring require different combinations of data, technology, and specialist skills.

Ownership is most valuable where a capability contributes directly to a lasting competitive advantage. Customer information and the systems used to understand it may be particularly important for operators whose profitability depends on loyalty and repeat visits. Other capabilities may be more efficiently developed through external technology providers.

The decision also depends on existing systems and organizational skills. A company with established customer data infrastructure may be able to extend it into AI-supported personalization. Another may gain more from partnering with a technology provider that can deliver the required functionality without a large internal development program.

Executives should connect these choices to the operator’s desired position in the travel value chain. AI may create opportunities to participate more deeply in trip planning, customer engagement, or related services. Some businesses may find their existing role commercially attractive, while others may see value in expanding into adjacent activities.

Expansion requires a clear view of the economics involved. Participating in more stages of a customer’s journey can create additional opportunities for revenue and customer knowledge, but it also requires investment and new operational capabilities. Management needs to determine whether the expected commercial benefits justify those commitments.

The same reasoning applies to partnerships. Working with an AI platform may provide access to customers or technology that would be expensive to develop internally, while also creating dependence on another company’s commercial terms. The decision should reflect the value of that access and the operator’s ability to maintain profitable customer relationships.

For a hotel operator deciding whether to build an AI booking assistant, invest further in loyalty and personalization, or negotiate new distribution arrangements, these choices compete for the same resources. The strongest priority is the one that addresses its most important constraint on profitable demand. That assessment should determine where the operator invests directly, where it partners, and which customer relationships it keeps under its own management.

Final thoughts

AI could reshape travel distribution without producing an immediate shift in direct and indirect booking shares. As AI platforms gain influence over how travelers discover, compare, and purchase trips, operators may face rising acquisition costs, greater pricing pressure, and weaker access to customer information. These changes could affect profitability long before they become visible in traditional distribution metrics.

For executives, the priority is to understand how AI could change the economics of their existing customer relationships. That means looking beyond booking volumes to examine who influences purchasing decisions, what it costs to acquire customers, and how much control the business retains over its distribution channels. Stable booking shares should not be mistaken for stable commercial conditions.

Investment decisions should reflect each operator’s competitive position. Stronger AI discoverability, clearer pricing, more convenient direct booking experiences, and better use of customer data can help protect margins and strengthen customer relationships. Larger operators may also have opportunities to negotiate new distribution arrangements, while smaller businesses may benefit more from targeted improvements to visibility, conversion, and retention.

The industry’s experience with e-commerce shows that distribution stability often requires sustained investment. AI presents a similar challenge, but with new opportunities for technology platforms to influence demand before a booking takes place. The operators best positioned for this transition will be those that measure success not simply by where customers book, but by how profitably they acquire, serve, and retain them.