Artificial intelligence (AI) is beginning to change two fundamental parts of the hotel business: how travellers choose properties and how hotels deliver a stay. AI systems can influence hotel discovery, handle routine operational tasks and use connected guest data to support more personalised service.
For hotel operators, the change goes beyond chatbots and standalone AI tools.
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AI is becoming part of a wider technology environment that includes property management systems (PMS), customer relationship management (CRM) platforms, revenue systems and guest communication tools. When these systems can exchange reliable information, AI can support decisions and actions across more of the guest journey.
The challenge is that adopting AI is not simply a matter of adding another technology product. Hotels need accurate data, connected systems and clear rules about which decisions can be automated and which require human oversight.
These foundations will determine whether AI improves efficiency and the guest experience or simply adds another layer of complexity.
AI is changing how hotels are discovered
Hotel discovery is beginning to extend beyond traditional search engines and online travel agencies (OTAs).
Travellers can increasingly use conversational AI to describe a trip in ordinary language and receive hotel recommendations based on criteria such as location, price, facilities and individual requirements.
This is also creating the foundations for more autonomous booking journeys. AI-assisted systems can help travellers search, compare and shortlist properties, while emerging agentic AI models are designed to take actions on a user’s behalf.
The distinction matters. AI-assisted discovery is already possible, but fully autonomous hotel booking is not yet the standard way people buy accommodation. Technology platforms, hotel companies and intermediaries are still developing the technical and commercial models that could support more agent-led transactions.
The commercial implications are becoming clearer. In July 2026, Radisson Hotel Group and Accenture launched a hotel discovery application within ChatGPT. Travellers can use conversational queries to search more than 1,000 Radisson properties across more than 100 countries.
Results can incorporate rates, inventory, amenities and location information before directing customers to the hotel group’s website to complete a reservation.
For hotels, this creates another audience for digital information: AI systems that help people make decisions.
Hotels have traditionally optimised digital information for travellers and search engines. As AI becomes part of the discovery process, property information also needs to be easy for machines to interpret accurately.
Room types, amenities, policies, location, prices and availability should be current and consistent across the systems and channels from which AI services obtain information.
Traditional hotel SEO therefore remains important, but AI-led discovery adds another consideration. A hotel’s proposition needs to be understandable not only to the traveller but also to the technology interpreting that traveller’s request.
Experimental research into AI hotel recommendations suggests that operators should not assume these systems are entirely objective. A 2026 audit covering 12 AI models found that guest ratings and price had particularly strong effects on hotel selection.
It also identified an influence from list position, showing that factors unrelated to a hotel’s underlying quality can affect AI-generated recommendations.
The response should not be to search for shortcuts to manipulate AI recommendations. Hotels have a stronger incentive to make their proposition easy to interpret and verify. Accurate property information, clear descriptions, current inventory and a strong reputation become increasingly important when an AI system sits between a traveller’s request and the hotels being considered.
AI-mediated discovery could also create new routes to hotel visibility alongside established OTA channels. It is too early to determine what this will mean for direct bookings, customer acquisition costs or OTA market share. Hotels should nevertheless monitor where major AI platforms obtain their information, how their properties are represented and where travellers are directed when they are ready to book.
Automation is changing routine hotel operations
The second major impact of AI in hotels is less visible to travellers but potentially just as important. Hotel automation can reduce repetitive administrative work and give employees more time for tasks that require judgement, problem-solving and personal service.
Guest communication is an obvious example. Questions about check-in times, breakfast, parking, Wi-Fi and hotel facilities are predictable and frequently repeated. Automated messaging can handle suitable enquiries around the clock, while integrated systems can route service requests to the appropriate department.
Check-in offers another opportunity. Digital workflows can collect required pre-arrival information and send standard communications before a guest reaches reception. Similar processes can support check-out, housekeeping requests and other routine parts of a stay.
Not all of this automation requires AI. Conventional automation follows predefined rules, while AI can help interpret a request, identify patterns or generate an appropriate response within defined limits. The bigger opportunity for hotels comes when AI, automation and integrated operational systems work together.
AI can also support work behind the scenes. Hotel technology platforms can assist with demand and staffing forecasts, service requests, maintenance planning and revenue decisions. Connected systems can share inventory and availability data, reducing the need for employees to enter the same information repeatedly across separate platforms.
Integration is therefore more important than automation for its own sake. A guest-facing AI assistant that cannot access current reservation details, availability or hotel policies has limited operational value. When authorised systems can securely exchange relevant information, a request can move from conversation to action with less manual intervention.
Not every decision should be delegated to AI. Complex payment issues, unusual booking changes, complaints, safety concerns and exceptions to policy may still require human judgement. Hotels need clear escalation rules so automated processes know when to hand control to an employee.
This distinction also matters for workforce planning. The strongest case for hotel automation is not the removal of human service. It is the transfer of predictable, repetitive processing to technology so employees can spend more time on situations where judgement, empathy and problem-solving have greater value.
Operators should measure AI and automation against business outcomes rather than the volume of tasks handled by technology. Useful measures include response times, resolution and escalation rates, errors, staff workload, guest feedback and, where relevant, conversion or incremental revenue.
A system that automates a large number of tasks but creates more exceptions for employees to correct may simply move work rather than remove it.
Connected data is making personalisation more useful
Personalisation is one of the most frequently promised benefits of AI in hospitality, but its effectiveness depends heavily on the quality of the hotel’s underlying data.
Traditional hotel personalisation often relies on employees recognising returning guests or recording preferences in reservation notes. This can produce excellent service, but it is difficult to deliver consistently across shifts, departments and properties.
Connected hotel systems offer a more systematic approach. PMS, CRM and loyalty platforms can bring together appropriate information about previous stays, preferences and interactions. AI can then help determine which information is relevant at a particular point in the guest journey.
For example, a returning guest may previously have requested a particular room feature, used a hotel facility or chosen to receive certain pre-arrival offers. When appropriate data is available across connected systems, the hotel can use that information without requiring an employee to search manually through separate records.
The aim is not simply to remember more about each guest. It is to use relevant information at the right moment.
Context remains critical. Previous choices do not automatically describe what a person wants on every visit. The same guest may travel for business on one occasion and leisure on another. Effective hotel personalisation therefore needs to consider the circumstances of the current stay rather than simply repeat past behaviour.
This is why data architecture matters as much as the AI model. If guest information is duplicated, outdated or isolated across different platforms, automated personalisation can reproduce those weaknesses at greater speed. AI cannot compensate for unreliable underlying records.
Hotels need dependable data, agreed standards and appropriate connections between core platforms before they can expect AI to produce consistently useful recommendations or actions.
There is also a governance requirement. Hotels hold personal and transactional information, and greater use of AI does not remove their responsibilities for how that information is collected, accessed and used. Personalisation should not become an argument for gathering unnecessary guest data.
Hotels therefore need to consider data minimisation, access controls, retention policies, cybersecurity and human oversight alongside the commercial benefits of AI. Data should be used only for legitimate purposes and in accordance with applicable privacy and data-protection requirements.
Building the foundations for AI
The direction of travel is towards a more connected, system-driven hotel. AI can help properties compete for visibility as booking discovery changes, reduce repetitive work and make better use of guest information.
But the technology itself is only one part of that transition.
Hotels that want to benefit from AI need to start with the foundations: accurate and consistently structured guest and property data, reliable connections between core systems and clear boundaries around automation.
Lower-risk, repeatable processes provide a practical starting point, while employees should retain control over exceptions and decisions that require judgement.
The competitive advantage is unlikely to come from having the most AI tools. It will come from creating a technology environment in which machines can handle routine processes reliably while hotel teams remain free to focus on the moments that require human judgement, empathy and service.
