The Use of Artificial Intelligence (AI) in the Wine Sector – Use Cases, Benefits, Implementation Challenges and Drivers [védés előtt]

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Artificial Intelligence (AI) is an innovation that has gained widespread attention in recent years, not only in popular culture but also across academic, practical, and policy domains. However, the technology has been under development for approximately eight decades at the time of this dissertation, with multiple use cases recognized earlier in different areas. The commoditization of AI, lowered barriers to entry and increased awareness about the technology’s potential benefits is expected to result in its adoption in industries that were previously considered as traditional sectors with low levels of digital maturity. The wine industry is one of those segments, and with a significant weight in global agricultural production, employment and cultural heritage, identifying how AI can transform the wine value chain can provide valuable insights. The objective of this study is therefore to understand the impact of AI on winemaking, by answering four main research questions. These are aimed at understanding the use cases, benefits, implementation challenges and adoption drivers of AI in the wine value chain. The results are achieved through applying a variation of different methods of academic inquiry, starting with the review and bibliometric analysis of relevant academic literature, continuing with the utilization of the Delphi method, where two rounds of surveys are conducted with a 19 highly relevant experts in this field. To ensure that more granular insights are captured, the results are then corroborated by in-depth interviews with 10 respondents and the systematic analysis of the outcomes of those with qualitative and quantitative techniques. The findings of this research show that AI adoption in the wine sector is primarily shaped by economic and environmental pressures, climate-related risks, labour shortages, market competition, and growing consumer expectations for digital and personalised experiences. These external drivers contrast with internal constraints, including cultural resistance, risk aversion, limited digital skills, and concerns about preserving authenticity. The study identifies the most feasible use cases in precision vineyard management (such as disease detection, irrigation optimisation, and climate forecasting) as well as process optimisation in fermentation and quality control, and emerging applications in consumer personalisation. AI adoption offers clear benefits related to efficiency, cost reduction, sustainability, and product consistency. However, uptake remains hindered by high investment costs, inconsistent data quality, infrastructural limitations, and underdeveloped governance and policy support. Together, these findings highlight an adoption landscape where operational potential is strong, but socio-technical and institutional barriers continue to shape implementation outcomes. This study provides important insights into the driving forces of AI implementation in winemaking, as well as the potential benefits and challenges associated with such a significant transformation. This research is intended to contribute to the growing number of contributions on AI’s implications on business model innovation. To the best knowledge of the author, no previous study was conducted that analysed the impact of AI on winemaking by applying a multifaceted mix-methods approach, with a comprehensive review of academic literature and the systematic collection of highly relevant expert opinions, which also highlights the novelty of this research.