Unlocking the potential: hybrid blockchain and AI-enabled traceability model development and implementation in the dairy industry – proof-of-concept

• Presents a novel hybrid blockchain and AI-enabled end-to-end SC traceability model. • Validates a multilayer Web3-based architecture integrating smart contracts, ML algorithms & IoT-enabled data capture. • Offers a proof-of-concept and feasibility analysis, highlighting scalability, transaction speed & system responsiveness. Conventional traceability systems without real-time information transmission are susceptible to tampering. In contrast, blockchain and artificial intelligence (AI)-enabled traceability models offer transparency and accountability, given their decentralized nature and immutability. This research conceptualizes and develops a hybrid blockchain and AI-enabled traceability (prototype) model and implements it in the dairy industry. The study includes a collaborative research methodology, including a literature review to analyze the existing traceability solutions, identify data entry points, select model requirements, and deploy smart contracts, decentralized applications (Dapps) and Web3 technologies to develop and validate the proposed model via Testnet . The findings present the user interface developed as a prototype traceability model and its characteristics, such as transparency, decentralized nature, and immutability, followed by practical validation. The post-implementation data analysis highlighted the security, privacy, smart contract validation rules, and comparative insights, as well as the alignment of the theoretical model with practical applications using Web3 technologies. This research contributes to the literature on hybrid blockchain and AI-enabled traceability, highlighting the potential for exploring opportunities in the food industry.