A Systematic Review on AI in Tourism: A Methodology Guide

Artificial intelligence applications in tourism and hospitality span personalized travel recommendation systems, AI-powered customer service chatbots, demand forecasting, and dynamic pricing models, and a systematic review on this broader topic benefits from the same disciplined scoping that any multi-application AI topic requires.

Choosing a specific tourism AI application

A review examining AI recommendation systems' effect on booking behavior is asking a genuinely different question than one examining chatbot customer service satisfaction or AI-based demand forecasting accuracy, and each deserves its own tightly scoped review rather than an attempt to synthesize findings across these substantively different application types.

Outcome measures in tourism and hospitality research

This field uses outcome measures including booking conversion rates, customer satisfaction scores, forecasting accuracy metrics, and revenue or occupancy performance indicators, and specifying which of these your review addresses, with attention to whether included studies measure this outcome in genuinely comparable ways, is essential groundwork before search and screening begin.

Study design diversity in this literature

Tourism AI research includes controlled experiments comparing AI-assisted versus traditional approaches, industry case studies of specific implementation outcomes, and survey research on traveler and industry attitudes toward AI tools. Specifying eligible designs clearly in your protocol, and matching appraisal tools to whichever mix you ultimately include, follows the same principle relevant across every AI application topic covered in this series.

Demand forecasting as a distinct methodological structure

Where your review addresses AI-based demand or pricing forecasting specifically, this represents a genuinely different evidence structure than most other tourism AI questions, typically involving accuracy comparisons between AI forecasting models and traditional statistical forecasting methods over defined time periods, requiring extraction and synthesis approaches suited to comparing predictive accuracy rather than a standard intervention-effect structure.

Search strategy across tourism and technical literature

Comprehensive coverage requires searching tourism and hospitality-specific databases and journals alongside broader business databases and, where relevant, technical AI literature, since research on this topic is published across genuinely different disciplinary venues depending on whether a given study's primary contribution is technical or industry-application-focused.

Industry and practitioner literature considerations

A meaningful share of relevant evidence on AI adoption in tourism exists in industry reports and practitioner publications rather than peer-reviewed academic literature, and depending on your review's specific purpose, considering whether grey literature search is warranted, and how you will appraise the quality of any included industry sources differently from peer-reviewed academic sources, is worth deciding explicitly at the protocol stage.

Currency of findings in a fast-moving commercial application area

As in other commercially applied AI domains, the specific tools and capabilities examined in earlier tourism AI research may not reflect current commercial offerings, and being explicit about your search date and honestly discussing how this affects your findings' current applicability reflects the same currency consideration relevant across every fast-moving AI application topic.

A practical starting point

Before beginning your search, narrow your question to one specific tourism AI application, define your outcome measure precisely enough to support consistent extraction across included studies, and decide explicitly whether your review's purpose calls for including industry and practitioner literature alongside peer-reviewed academic sources.

Seasonal and destination-specific variation worth considering

Tourism AI research findings may vary meaningfully across different destination types, seasons, and traveler demographics, and where your included studies span this kind of variation, considering it explicitly as a potential source of heterogeneity, rather than assuming a single pooled finding applies uniformly across all tourism contexts, produces a more genuinely useful synthesis for readers working in specific, varied tourism contexts.

A note on rapidly evolving commercial AI offerings

Given how quickly commercial AI tools available to tourism and hospitality businesses continue to evolve, explicitly dating your review's findings and discussing how quickly this specific commercial landscape changes helps readers calibrate how much continued relevance to expect from your synthesized conclusions over time.

A closing consideration on this topic's practical audience

Tourism and hospitality operators making real technology investment decisions benefit from honest, well-scoped synthesis on this topic, making the discipline of narrow question framing and transparent limitation reporting genuinely useful beyond academic contribution alone. That practical value is precisely why careful, well-scoped methodology is worth the effort here, not just for academic completeness but for real decision support.

A final word on serving working practitioners

Tourism and hospitality professionals reading your review are typically weighing real, practical technology investment decisions under genuine time and resource constraints, and writing your findings with this practical audience clearly in mind, alongside the academic rigor your methodology requires, makes your synthesis considerably more likely to actually inform real decisions.

Considering the traveler experience throughout

Behind every AI adoption metric in this literature sits an actual traveler experience being shaped by these tools, and keeping this human dimension in view throughout your synthesis, not just the operational and financial metrics that dominate much of this literature, adds a valuable, sometimes underrepresented perspective to your review's overall contribution. That human-centered framing is part of what distinguishes a genuinely thoughtful systematic review from a narrowly technical summary of adoption metrics alone, and it is this human dimension that ultimately gives a technical review its lasting relevance and worth, well beyond a narrow accounting of adoption rates and operational efficiency metrics alone, remembering that behind every dataset sits a traveler whose actual experience these tools are meant to improve.

A final word on this topic's broader relevance

Tourism sits at the intersection of technology, culture, and genuine human experience in a way few other industries do quite as visibly, and a systematic review conducted with real care on this specific topic offers value well beyond the tourism sector itself, contributing to a broader understanding of how AI tools function when deployed in service of something as fundamentally human as travel and hospitality. That broader relevance is worth keeping in mind throughout a project that might otherwise feel narrowly industry-specific in scope but genuinely broad in what it ultimately contributes to understanding responsible AI deployment, wherever it happens to show up next across an increasingly connected world.