What the paper studied
Hotels and restaurants face two pressures at once: adopting AI and becoming more sustainable. This review asks where those trends meet. The author searched seven leading hospitality and sustainability journals for work published between January 2010 and September 2026, assessed 101 records against predefined eligibility criteria, and included 54 studies that link a specific AI technology to a sustainability outcome. Each study was coded with an environmental, social, economic and governance (ESG) framework.
Key findings
- A young, fast-growing field. All 54 included studies were published between 2020 and 2026, and 63% appeared in 2025–2026.
- Social and environmental outcomes lead. Social outcomes (37%) and environmental outcomes (35%) form two comparably sized main streams, followed by governance and responsible AI (15%), economic outcomes (9%) and integrated perspectives (4%).
- Robots and conversational AI dominate. Service robots are the most frequently studied technology (37%), followed by conversational and generative AI agents (17%).
- Most evidence is self-reported. 70% of the studies rely on perceptions or intentions, and only four use actual behavior or operational data.
- Notable blind spots. AI for energy and water management and for ESG reporting was not directly addressed in the reviewed studies.
- An integrated framework. The paper proposes a framework linking AI capabilities, psychological and organizational mechanisms, and sustainability outcomes, and sets out directions for future research.
Why it matters for hospitality
AI is increasingly pitched to hotels as a sustainability lever, but the top-journal research so far says more about how guests and employees feel about robots and chatbots than about measured reductions in energy, water or waste. The applications where operators most expect AI to cut environmental impact and simplify reporting, such as energy and water management and ESG disclosure, are the ones this body of research has not yet directly studied.
Practical takeaways
- Treat claims that AI improves sustainability as hypotheses to test on your own operational data, not as established results.
- When piloting AI for energy, water or ESG reporting, set baseline metrics up front so results can be measured rather than perceived.
- Consider social and governance outcomes (workforce effects, responsible AI) alongside environmental ones; together they make up most of the published evidence.
- Hotels and groups willing to share operational data with researchers can help close the gap between perception studies and measured outcomes.
Limitations
By design, the review covers seven leading hospitality and sustainability journals, so work published in other journals, conference proceedings or industry reports falls outside its scope, and its blind spots describe that corpus rather than the wider literature. Because most included studies measure perceptions or intentions, the review cannot estimate how large AI's real-world sustainability effects are.