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<p>This study investigates the application of Artificial Intelligence (AI) in the wine sector for marketing-related processes, with a specific focus on its implications for marketing analytics. Adopting the PRISMA protocol, a Systematic Literature Review (SLR) was conducted, leading to the identification and analysis of 31 scientific contributions. The findings reveal that AI applications are predominantly concentrated in technical and production-related domains, such as quality control, traceability, and process optimization, mainly relying on machine learning and data-driven approaches. In contrast, a more limited body of research addresses AI in marketing contexts, where consumer data is used to support marketing analytics functions, including segmentation, personalization, and demand prediction. The results highlight a structural gap in literature: despite the widespread adoption of AI technologies across the wine system, their integration into marketing analytics remains limited. A conceptual framework is proposed that distinguishes between direct and indirect AI-driven data pathways in marketing analytics. Implications for Small and Medium-sized Enterprises (SMEs) and ethical considerations related to AI use are also acknowledged. These findings suggest important directions for future research aimed at bridging the gap between technological capabilities and marketing analytics applications.</p>

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Wine marketing strategies with AI for resilience of small and medium-sized enterprises

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年6月15日

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  • Marzia Ingrassia,
  • Simona Bacarella,
  • Pietro Chinnici,
  • Federico Modica,
  • Giusi Giamporcaro,
  • Stefania Chironi

摘要

This study investigates the application of Artificial Intelligence (AI) in the wine sector for marketing-related processes, with a specific focus on its implications for marketing analytics. Adopting the PRISMA protocol, a Systematic Literature Review (SLR) was conducted, leading to the identification and analysis of 31 scientific contributions. The findings reveal that AI applications are predominantly concentrated in technical and production-related domains, such as quality control, traceability, and process optimization, mainly relying on machine learning and data-driven approaches. In contrast, a more limited body of research addresses AI in marketing contexts, where consumer data is used to support marketing analytics functions, including segmentation, personalization, and demand prediction. The results highlight a structural gap in literature: despite the widespread adoption of AI technologies across the wine system, their integration into marketing analytics remains limited. A conceptual framework is proposed that distinguishes between direct and indirect AI-driven data pathways in marketing analytics. Implications for Small and Medium-sized Enterprises (SMEs) and ethical considerations related to AI use are also acknowledged. These findings suggest important directions for future research aimed at bridging the gap between technological capabilities and marketing analytics applications.