AI-Enabled Next-Generation Dairy Systems: From Sensors to Smart Processing

The rapid integration of artificial intelligence (AI) into dairy systems has been widely promoted under the Dairy 4.0 paradigm, yet its role as a true system-level "game changer" remains insufficiently substantiated. This study presents a structured critical review of AI-enabled technologies across the dairy value chain, encompassing precision livestock farming, in-line milk quality sensing, smart processing, and advanced ingredient development. A transparent literature search and selection strategy was applied to identify and evaluate relevant studies, with emphasis on reported performance metrics, validation conditions, and real-world applicability. The analysis reveals that while AI demonstrates strong technical capability, particularly in milk quality prediction, sensor-based monitoring, and process optimization, most evidence is derived from controlled laboratory settings, with limited validation under heterogeneous farm and industrial conditions. Key limitations include challenges in model generalizability, data integration, calibration stability, and interoperability with existing infrastructure. Moreover, economic feasibility, scalability across production systems, and governance issues related to data ownership and accountability remain insufficiently addressed. Across the reviewed domains, the impact of AI is found to be conditional rather than inherently transformative. Technologies deliver measurable benefits primarily when embedded within sensor-rich, interoperable systems that directly inform operational decision-making. In contrast, standalone AI applications often function as analytical tools without inducing system-level change. The review further highlights discrepancies between technological potential and demonstrated outcomes, particularly in sustainability performance and industrial-scale implementation. Overall, AI should be conceptualized not as an autonomous disruptive force but as a system-level enabler, whose effectiveness depends on complementary advances in sensing, infrastructure, data governance, and workforce capability. Future research should prioritize field validation, economic assessment, and integration frameworks to bridge the gap between experimental performance and practical deployment, thereby enabling more realistic evaluation of AI's transformative potential in dairy systems.