AI Hospitality Alliance

Research Archive

2024

Archived AI Hospitality Alliance research published in 2024.

Current year202520232022
Academic ResearchDecember 10, 2024Current Issues in Tourism

AI and the Future of Talent Management

Georges El Hajal, Ian Yeoman

This paper argues that future hospitality performance will depend less on having more staff and more on having teams that can work effectively with AI. Hotels that begin building AI literacy now through hiring, training, and manager development are likely to be in a stronger position than those that wait. The research also points to practical uses in workforce planning, especially smarter scheduling and better use of labor data. The business implication is that AI should be used to improve decision-making, strengthen productivity, and support employee development, rather than being seen only as a tool for reducing labor cost.

OperationsGuest Experience
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Academic ResearchNovember 26, 2024Businesses (MDPI)

A Change Management View on Technology Adoption in Hotel Organizations

F. A. R. Somera, K. Petrova

Hotels consistently underperform on technology ROI not because they buy the wrong tools but because they underinvest in the organizational change required to use them well. This paper finds that staff 'digital readiness' — how prepared, involved, and confident employees feel about new technology — is a stronger predictor of AI deployment success than the technology's own capabilities. The practical prescription: budget for change management at roughly 20–30% of the technology spend, involve frontline staff in selection and piloting before launch, and communicate the 'why' explicitly and repeatedly. Teams that feel informed and equipped adopt faster and perform better than teams that are simply trained.

Guest ExperienceReviews & SentimentEthics
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Academic ResearchJune 5, 2024Journal of Modern Hospitality

Role of Artificial Intelligence in Revenue Management and Pricing Strategies in Hotels

A. Gatera

AI-based pricing tools improve hotel RevPAR by enabling continuous, real-time price adjustments that human revenue managers simply can't execute manually at the same speed or volume. The documented improvements across studies range from 3% to 12%, with the gains driven primarily by faster response to demand signals and better segmentation — capturing full-rate business travelers while staying competitive for leisure segments. The paper is equally clear about the three barriers: messy or fragmented data, technology integration complexity in legacy hotel stacks, and the skills gap on revenue management teams that still need to evolve from rate-setters to data analysts. The financial case for overcoming these barriers is strong; the competitive cost of not overcoming them is growing.

Revenue ManagementGuest ExperienceTourismReviews & SentimentEthics
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Academic ResearchJune 3, 2024British Food Journal

Machine Learning for short‑term property rental pricing based on seasonality and proximity to food establishments

D. de Jaureguizar Cervera, J. de Esteban Curiel, D. C. Pérez‑Bustamante Yábar

Analyzing 220 Airbnb listings in Madrid, this study uses machine learning to show that short-term rental properties don't compete in one single market — they cluster into distinct sub-markets, each with its own optimal pricing rhythm. Some properties should hold high base rates year-round; others need aggressive seasonal adjustment. Location matters in a specific way that's operationally useful: proximity to everyday amenities like supermarkets and restaurants is a stronger price driver than proximity to tourist attractions, reflecting the shift toward travelers wanting to 'live like a local.' For hotel revenue managers competing against short-term rentals, this research reinforces the value of hyper-local competitive set analysis over broad market comparisons.

Revenue ManagementOperationsGuest ExperienceTourism
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Academic ResearchMay 28, 2024Tourism and Management Studies

Hotel demand forecasting models and methods using artificial intelligence: a systematic literature review

H. Henriques

A systematic review of AI-based hotel demand forecasting finds that machine learning models consistently outperform traditional statistical methods — particularly during volatile periods, around irregular events, and when enriched with external data like competitor rates and local event calendars. The performance gap is largest for hotels in dynamic markets; in stable, predictable markets the advantage is smaller but still present. The most important practical finding is that data quality matters more than model choice: a well-implemented simple model trained on clean data beats a sophisticated model trained on messy data every time. Before investing in forecasting technology, audit and fix your data infrastructure.

Revenue ManagementGuest ExperienceReviews & Sentiment
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Academic ResearchJanuary 1, 2024International Journal of Hospitality Management

Decoding the future: Proposing an interpretable machine learning model for hotel occupancy forecasting using principal component analysis

Daniele Contessi, Luciano Viverit, Luis Nobre Pereira, Cindy Yoonjoung Heo

Most AI forecasting models are 'black boxes' that revenue managers don't fully trust — and tools you don't trust don't get used consistently. This paper proposes a machine learning approach that groups historical booking patterns into recognizable clusters (early corporate, last-minute leisure, event-driven spike, etc.) before applying a forecasting model to each, making the prediction explainable: not just 'occupancy will be 87%' but 'because booking pace matches the early corporate pattern, which historically reaches 87%.' The result is both more accurate than standard methods and more operationally useful, because revenue managers can see the reasoning and make better decisions around it.

Revenue ManagementOperationsEthics
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