What the paper studied
This conference paper reviews how AI enhances e-business in hotels, with a focus on marketing, reservation systems and revenue management. The author conducted a structured literature review of 21 scientific sources, mainly published between 2022 and 2026, found through Google Scholar, Crossref and publisher platforms. Purely technical studies without managerial implications were excluded, and the literature was grouped into five themes: personalization, conversational AI, revenue management, operational automation and implementation challenges. The analysis draws on the Technology Acceptance Model, Diffusion of Innovations theory and the Resource-Based View.
Key findings
- The value is in the chain, not the tool. AI-supported marketing generates demand through personalized communication and targeting, reservation systems convert that demand with automated, always-available support, and revenue management makes sure bookings are realized at more favorable prices. The strongest contribution comes from the synergy across this guest journey.
- Personalization lifts conversion. Recommendation systems and adaptive interfaces that draw on search history, past bookings and behavior can reduce decision fatigue and raise booking conversion, and CRM integration makes upselling and cross-selling more effective.
- Revenue management shows the clearest measurable effects. Forecasting from seasonality, booking pace, competitor prices, local events and demand elasticity supports real-time pricing and improves RevPAR and ADR.
- Conversational AI widens service availability. Chatbots and virtual assistants provide 24/7 help with reservations, check-in, check-out and additional services, reducing operational workload.
- Barriers are organizational as much as technical. High initial costs, data infrastructure and integration with existing systems, privacy and data protection, and employee resistance and skill gaps all slow adoption.
- Gaps in the evidence. The literature has little on regional differences in adoption, employee adaptation, ethics and long-term financial results.
Why it matters for hospitality
For commercial teams, the paper is a reminder that buying a chatbot, a recommendation engine and a pricing tool separately captures only part of the value. The larger gains come when data and decisions flow between marketing, booking and pricing, which makes integration and data quality strategic priorities rather than IT details. The review also notes that AI-supported digital tools can improve visibility, targeting and competitiveness for small and medium-sized hospitality businesses, not just large hotel groups.
Practical takeaways
- Plan AI across the full e-business chain (marketing, reservations and pricing) and make sure the systems share data.
- Use CRM-connected personalization to drive conversion and ancillary revenue.
- Track RevPAR and ADR as the main measures of AI-driven pricing.
- Budget for integration, data protection and staff training alongside software costs.
- Keep the human side of service in view; the author expects future success to depend on balancing efficiency, innovation and the human dimension of hospitality.
Limitations
The paper relies entirely on secondary sources, with no primary data, and reviews a relatively small set of 21 publications, so it summarizes existing findings rather than testing them. As the author notes, long-term financial evidence for AI in hospitality remains limited.