Even though the promise of Artificial Intelligence (AI) to transform the operations of entrepreneurial firms remains monumental, the coherence of literature on the boundary conditions (i.e. which AI aspects, when and how to deploy them) under which AI could be exploited to enhance agritourism operations is yet to emerge. Drawing on a Systematic Literature Review (PRISMA), the study explored the various components and architecture of AI implementation in agritourism, the conditions under which they are deployed and different AI-embedded strategies that agritourism entrepreneurs use to seize hold of opportunities in the sector. Findings suggest that the widely employed AI components are AI trip forecasting systems, AI-enabled trip generation, trip personalisation and post-trip review techniques. The broad AI architecture used in agritourism spans deep learning forecasting techniques, recommender systems, natural language processing and face recognition, wearable and activity trackers, geotagging, visual navigation, autonomous vehicles, personalisation systems, machine learning and intelligent automation and controlled environments. The conditions under which these are employed involve trip planning (e.g. identifying agritourism locations), trip generation (e.g. choice of mode of transport, payment techniques, activity selection), trip experience (e.g. menu choices, activity participation and experience documentation) and post-trip reviews (e.g. reflections, sentiment analysis, journaling) stages. The paper showcases how agritourism entrepreneurial firms can adopt AI for deepening customer orientation, enhancing customer satisfaction and foregrounding managerial productivity, thereby enriching the theorisation and practice of agriculture, hospitality and tourism sectors.