Case overview
| Case | Context | GenAI functionality & data used | Provider type | Data collected | Iterative improvement process and outcomes |
|---|---|---|---|---|---|
| A – livestock (horses), Germany | Family-run training and breeding stable in Germany with ∼5 employees managing sport horses and youngstock | Wearable GenAI monitoring sensors generating movement, heart-rate, temperature and behavioral analytics (see supplementary materials 2–3) | Start-up specializing in livestock analytics for equine sector | User and provider interviews (March 2025), on-site observations and follow-up user interview (October 2025) (see supplementary materials 8) | Provider feedback recalibrated alert thresholds, improving contextual accuracy. GenAI became embedded in daily monitoring, enabling earlier interventions and improved animal welfare |
| B –livestock (cows, horses and chickens), Switzerland | Family-run mixed livestock farm in Switzerland (∼4 employees) raising cows, horses and chickens | GenAI camera-based monitoring system detecting behavioral anomalies (e.g. restlessness, isolation, lying patterns). Generates health reports (lameness and body condition scores; see supplementary material 4–5) and recommends tailored interventions | Established Agri-tech company providing farm-monitoring systems | User and provider interviews (March 2025), on-site observations and follow-up user interview (October 2025) (see supplementary materials 8) | Provider-user recalibration improved report relevance and staff interpretation. GenAI became integrated into monitoring routines, enabling earlier health interventions and reducing supervision |
| C – crop farming (hay and silage), Switzerland | Family-run crop enterprise with ∼8 employees producing hay, silage and seasonal crops for regional feed markets | GenAI chatbot for crop management and nutritional analysis trained on historical yield and feed data; text-based recommendations (e.g. fertilizer ratios, storage practices). | Tech start-up developing conversational GenAI for small farms | User and provider interviews (March 2025), on-site observations (see supplementary materials 8) | Limited provider interaction prevented contextual adaptation. Generic recommendations reduced user trust, leading to discontinuation |
| D – crop farming (grains), Switzerland | Family-run grain producer (∼5 employees) cultivating wheat and barley on mixed terrain. Modernized equipment fleet and strong focus on sustainability certification | GenAI-enhanced agricultural machinery integrating lidar, radar and soil sensors to optimize seeding, tillage depth and pesticide application. Produces detailed agronomist advice on field efficiency and input use (see supplementary materials 6) | Established European machinery manufacturer cooperating with a startup for GenAI technologies | User and provider interviews (March 2025), on-site observations and follow-up user interview (October 2025) (see supplementary materials 8) | Continuous provider collaboration optimized system parameters for local conditions. GenAI became embedded in planning and sustainability reporting, reducing inputs and improving efficiency |
| Case | Context | GenAI functionality & data used | Provider type | Data collected | Iterative improvement process and outcomes |
|---|---|---|---|---|---|
| A – livestock (horses), Germany | Family-run training and breeding stable in Germany with ∼5 employees managing sport horses and youngstock | Wearable GenAI monitoring sensors generating movement, heart-rate, temperature and behavioral analytics (see | Start-up specializing in livestock analytics for equine sector | User and provider interviews (March 2025), on-site observations and follow-up user interview (October 2025) (see | Provider feedback recalibrated alert thresholds, improving contextual accuracy. GenAI became embedded in daily monitoring, enabling earlier interventions and improved animal welfare |
| B –livestock (cows, horses and chickens), Switzerland | Family-run mixed livestock farm in Switzerland (∼4 employees) raising cows, horses and chickens | GenAI camera-based monitoring system detecting behavioral anomalies (e.g. restlessness, isolation, lying patterns). Generates health reports (lameness and body condition scores; see | Established Agri-tech company providing farm-monitoring systems | User and provider interviews (March 2025), on-site observations and follow-up user interview (October 2025) (see | Provider-user recalibration improved report relevance and staff interpretation. GenAI became integrated into monitoring routines, enabling earlier health interventions and reducing supervision |
| C – crop farming (hay and silage), Switzerland | Family-run crop enterprise with ∼8 employees producing hay, silage and seasonal crops for regional feed markets | GenAI chatbot for crop management and nutritional analysis trained on historical yield and feed data; text-based recommendations (e.g. fertilizer ratios, storage practices). | Tech start-up developing conversational GenAI for small farms | User and provider interviews (March 2025), on-site observations (see | Limited provider interaction prevented contextual adaptation. Generic recommendations reduced user trust, leading to discontinuation |
| D – crop farming (grains), Switzerland | Family-run grain producer (∼5 employees) cultivating wheat and barley on mixed terrain. Modernized equipment fleet and strong focus on sustainability certification | GenAI-enhanced agricultural machinery integrating lidar, radar and soil sensors to optimize seeding, tillage depth and pesticide application. Produces detailed agronomist advice on field efficiency and input use (see | Established European machinery manufacturer cooperating with a startup for GenAI technologies | User and provider interviews (March 2025), on-site observations and follow-up user interview (October 2025) (see | Continuous provider collaboration optimized system parameters for local conditions. GenAI became embedded in planning and sustainability reporting, reducing inputs and improving efficiency |
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