Deliverable D3.2 “ Interim Progress Report of Use Cases for Sustainability and Initial Contribution to the Integrated Framework” presents the status and early outcomes of four pilot Use Cases developed under Work Package 3 of the FoodDataQuest project. These Use Cases demonstrate how data driven innovation including AI, DNA based tools, and behavioral analytics can enhance sustainability, traceability, and stakeholder engagement across the food and healthcare sectors. 

In each Use Case, an iterative and integrative approach is followed, combining co-design and agile development with continuous stakeholder engagement. AI, ML, and digital tools are deployed and tested in real-world settings, while progress is monitored through SDG-aligned KPIs. Findings are integrated into a shared framework, enabling mutual learning, technical refinement, and scalable, coherent implementation across all Use Cases. 

Use Case 1 addressed traceability and safety in the olive oil supply chain. Agroknow developed AI models for pesticide risk and fraud detection, complemented by BioCoS’s DNA-based varietal classification. Stakeholder engagement ensured practical relevance, and the next phase will integrate these tools into workflows and enhance consumer trust. 

Use Case 2 focused on improving hospital food services at UKCM, focusing on waste reduction and patient nutrition. Business processes and pilot scenarios for personalized nutrition, waste monitoring, and traceability are ready, with next steps including dashboard deployment, staff training, and evidence-based recommendations. 

Use Case 3 has laid the groundwork for a retail dashboard and consumer app integrating environmental and social indicators. Mock-ups and preparatory trials aim to validate circularity and traceability in livestock production. The next phase will connect live datasets and provide scalable tools for informed, sustainable choices. 

Use Case 4 has developed an AI-driven communication strategy, shifting from stylistic to behavioural personalization. TAU and WR integrated predictive models and LCA-linked consumption data. Next steps focus on refining the AI pipeline, running simulations, and delivering tailored messages to promote eco-conscious behaviour. 

Check out the detailed report of each use case in D3.2 in our public deliverable repository.