Abstract
Artificial intelligence (AI) is no longer peripheral in tourism and hospitality (TH). It already shapes how demand is forecast, prices are set, service is delivered, and capital is allocated. What remains unsettled is how these changes propagate through firm performance, labor markets, market structure, and long-run productivity (Bilgihan et al., 2024; Dogru et al., 2025). The integration of AI into manufacturing, finance, and healthcare has already demonstrated its potential to restructure costs, alter market design, and recalibrate labor demand (Acemoglu and Restrepo, 2019; Mithas et al., 2020). Yet, the TH industry possesses distinctive characteristics that make it especially instructive site for research: the centrality of human experience, the heterogeneity of service encounters, and the reliance on cultural and emotional labor. When algorithms begin to mediate these experiences, through smart destinations, automated service agents, or dynamic pricing systems, the economic effects are profound. In this editorial, we discuss the importance of investigating the economic implications of AI for the TH industry. We also briefly highlight the studies published in this special issue and how these studies investigate these issues and their contribution to the extant literature. We conclude with providing an agenda for future research.
The special issue: The economics of artificial intelligence in tourism and hospitality
The contributions in this special issue collectively aim to address two foundational questions. First, how does AI alter efficiency, productivity, and profitability within TH firms and markets? Second, what broader socioeconomic shifts, such as wage restructuring, employment dynamics, and inequality, emerge when AI substitutes or augments human labor? By grounding these questions in theory and empirical evidence, the issue provides a framework for policymakers, practitioners, and other industry stakeholders to evaluate AI’s role in shaping the economic future of the sector. For academics, the issue highlights the urgency of treating AI not as a technological add-on but as a systemic economic phenomenon. Theoretical frameworks drawn from labor economics, industrial organization, and behavioral economics provide fertile ground for future inquiry. In this context, the study by Lee et al. (2025) investigating the “Economic impact of artificial intelligence (AI): Conceptualization of business value of AI and future agenda for tourism and hospitality research” introduces a strategic conceptual framework categorizing AI’s roles into automation and information provision for better decision-making, and transformation of business models. Empirical researchers can benefit from applying mixed methods, ranging from econometric modeling to experimental designs and simulation studies, to capture the multi-layered effects of AI adoption.
New market power
AI is changing the back-office operations of TH organizations. Online travel agencies (OTAs) like Booking.com and Expedia are investing heavily in recommendation engines that learn consumer preferences at a granular level. These systems could tilt market power further toward the largest platforms, squeezing small hotels and local operators. Dynamic pricing tools are now embedded in many booking systems. Personalized pricing implies that two consumers may face different prices for identical inventory, conditional on predicted willingness to pay, which is a practice with both efficiency and fairness implications. For consumers, this can feel like personalized service or exploitation. For economists, it raises urgent questions about fairness and competition. AI will create and shift economic value. Demand generation, conversion, and “AI gatekeepers” will be important issues in TH and AI. In this context, the study by Napierała and Tomczyk (2025), which is titled “AI-enabled marketing in online travel platforms: Who and where adopts platform-led loyalty tools? examines the unequal adoption of AI-supported loyalty tools offered by Online Travel Agents (OTAs). The findings of this study showed that AI adoption is highly concentrated among the most popular hotels in metropolitan areas with higher-priced services, suggesting that the increasing reliance on OTA-controlled AI tools deepens the competitive disadvantage for Small and Medium-sized Enterprises and hotels in peripheral locations.
Although this study makes essential contributions to the extant literature, multiple research questions remain to be answered. AI itinerary builders and assistants compress search costs and raise the probability of purchase by translating vague preferences into concrete, bookable options. Agentic AI (autonomous assistants that plan and transact) is the next step. Analysts model double-digit revenue uplifts when agents orchestrate cross-sell/upsell across transport, lodging, and in-destination services, while the distribution structure shifts as agents can source inventory directly (Cosmas et al., 2025). Incumbent platforms are actively preparing for this shift. AI raises effective demand by lowering frictions and improving matching; it also reallocates surplus among hotels, OTAs, metasearch, and emerging “agent platforms.” We expect a new optimization problem: AI visibility, analogous to SEO but with different signals (schema quality, price integrity, trust, and post-stay feedback fidelity).
Human experience, algorithmic mediation
Perhaps the deepest change lies in how tourists and guests experience travel itself. Now we have chatbots handling customer service to robots checking in guests, AI is increasingly the first and sometimes only point of contact. This shift has profound economic consequences. Trust must now be extended to machines. The study by Mohammed and Denizci Guillet (2025) titled “Stakeholder perspectives on integrating explainable artificial intelligence into hotel revenue management systems” examined the “black box” problem (i.e., the difficulty in understanding opaque algorithmic decisions) in revenue management systems. The study found that Explainable Artificial Intelligence (XAI) is viewed as essential for building trust and achieving economic benefits. The study further offered a roadmap, detailing that XAI must explain decisions on key dimensions (what, how, why, what if), thereby providing the specific informational requirements needed to successfully integrate XAI into revenue management systems. Along the same lines, in their study titled “AI versus human-generated hotel pricing: Unpacking consumer perceptions of trustworthiness and fairness” Lee and Sharma (2025) investigate the transparency issue in algorithmic pricing. Using controlled experiments, the study findings revealed that consumers consistently preferred human-generated pricing over AI, suggesting that for AI implementation to succeed in revenue management, the process must be perceived as just, not just efficient. The bigger question: will guests still value human connection enough to pay for it? Or will efficiency trump empathy?
Productivity and labor
AI technologies offer promises, such as increased productivity and decreased labor cost. AI can lift profitability, but it can also depress wages if it displaces human staff. Early deployments show sizeable productivity gains concentrated among lower-tenure workers in service settings, with weaker effects for experts. Under what conditions do these gains pass through to prices or wages in TH services? In this context, a conceptual study by Ivanov et al. (2025) titled “The economics of service robots in hospitality companies” presents a conceptual model for evaluating robot investments, emphasizing four key dimensions, including task automatability, operational impact, labor dynamics, and performance metrics. This framework provides a practical, detailed conceptual framework to aid managerial decision-making and risk mitigation.
Future research should estimate pass-through elasticities and report both firm-level outcomes (ADR, RevPAR, conversion) and welfare-relevant outcomes (prices paid, realized wait times, wage ladders). Recent field evidence, from a large-scale assistant shows approximately 15% higher resolutions per hour, with gains concentrated among lower-tenure staff and modest quality trade-offs at the top of the skill distribution (Bakir et al., 2025; Brynjolfsson et al., 2025). Learning persists even when the tool is unavailable, and customers exhibit fewer escalations. TH studies should therefore report distributional impacts (by tenure/skill), adherence and exposure measures, and pass-through to prices, wages, and guest experience. We also need to investigate whether firms adopting AI see measurable gains in productivity and labor share, provided they also invest in upgrading workforce skills. Or without that investment, would the gains skew toward the employer, leaving workers behind?
Sustainability and resilience
AI is being deployed to address one of the industry’s most pressing challenges: sustainability. We need to research whether AI-driven systems can help hotels and destinations reduce energy use, manage water consumption, and optimize waste management. In this context, the study titled Artificial intelligence (AI) interacted ESG-based sustainable tourism: Economic insights by Isik et al. (2025) addresses a major gap by introducing the ESG/ITA (Environmental, Social, Governance to International Tourist Arrivals) metric to quantify environmental burden per tourist. The findings reveal that AI integration enhances the positive influence of sustainable tourism on GDP, positioning AI as a critical policy tool for destinations. Furthermore, investigating the durability of economic shocks on tourism receipts and GDP in major destinations, the study by Kocak et al. (2025) titled “Do shocks to tourism receipts have a transitory or persistent nature? A comparison of traditional and artificial intelligence-based analytical procedures” illustrated the efficacy of using advanced AI-based analytical procedures. The findings based on the AI-based models showed that the shocks are transitory for tourism receipts in countries like Italy, Japan, and Mexico, which provides policymakers guidance to implement short-term relief or long-term structural reforms. However, further research is still needed to investigate the extent to which AI adoption affects the nexus between tourism and sustainable development. Similarly, more research is needed to examine the efficacy of AI-based empirical methodologies in the context of economics and beyond (Dogru et al., 2024).
Concluding remarks
The studies published in this special issue collectively examined the economic, strategic, and operational dimensions of AI and robotics adoption in the tourism and hospitality industry. The findings from these studies collectively suggest that while AI integration offers pathways to enhanced efficiency, sustainability, and economic resilience, such adoption and integration also introduce challenges for the tourism and hospitality industry. These studies highlight both the transformative potential and the systemic risks of AI and robotics adoption. They provide multiple avenues of academic research to empirically investigate the implications of AI and robotics adoption in the tourism and hospitality. For industry leaders, policymakers, and other stakeholders, the insights provide a roadmap for balancing innovation with economic and ethical considerations, ensuring that technological progress supports sustainable growth, operational transparency, and long-term resilience.
The contributions in this issue are the beginning of a sustained research agenda. As AI technologies advance, they will continuously challenge our assumptions about productivity, labor, equity, and sustainability in tourism and hospitality industry. We hope this special issue provides both clarity and inspiration for the next wave of research. AI’s economic consequences in tourism and hospitality will not be uniform, and this is precisely why the TH field needs clear theory, credible empirics, and attention to institutions. The contributions in this issue demonstrate that TH can be a proving ground for the economics of AI that is both rigorous and practical. We invite scholars, practitioners, and policymakers to use the agenda above to frame the next wave of work.
We extend our deepest gratitude to the authors, reviewers, and editorial team who made this issue possible. We are confident that the works gathered here will serve as essential references for academics, PhD students, and practitioners seeking to navigate and shape the economic implications of AI in TH.