Farmers Must Lead Livestock AI

Farmers First: Building AI That Works on Real Farms

At AI(Live), the UK's first conference dedicated to AI in livestock farming, the message was clear: farmers must be at the center of system design if AI is going to deliver real value on the ground.

More than 300 attendees from industry bodies, government, vendors, academia, and farms aligned on one point-AI should serve farmers, not push them into someone else's roadmap.

What developers need to know

"Every farm and every farmer is unique," said technology futurist Dan Sodergren. The takeaway for product teams: optimize for context, not generic use cases. If you can't show ROI for the person using it in a muddy yard at 5am, it won't stick.

• Design with farmers, not just for them: regular field trials, feedback cycles, and co-created feature sets.
• Offline-first by default: intermittent rural connectivity is normal, not an edge case.
• Interoperability: APIs that plug into existing tools (herd management, milking systems, feed data, vet systems) without heavy integration work.
• Transparent value: simple dashboards that map AI outputs to cost, time, welfare, and yield-no black box theatre.
• Data rights and trust: clear ownership, security, and consent flows.

Adoption reality check

A new survey shared at the event showed 52% of farmers regularly use data, but only 10% currently use AI to run their businesses. And 57% want more education and training.

That gap isn't a "lack of interest." It's a product-market fit problem. Reliability, cost, trust, and data security are still blockers.

Infrastructure still decides outcomes

AI won't deliver if farms can't connect. Rural broadband remains a constraint. If you build for always-on cloud, you'll fail fast in the field.

Plan for sync queues, edge inference, and resilient data pipelines. For context on UK connectivity efforts, see Project Gigabit.

Design principles that ship

• Edge + cloud hybrid: run critical inference locally; batch non-urgent uploads.
• Human-in-the-loop: let farmers correct labels and outcomes; use that data to improve models.
• Clear TCO: show payback periods, maintenance costs, sensor replacement cycles, and support levels.
• Model robustness to variability: breeds, feed regimes, housing types, weather, and seasonality all shift baselines.
• Open data contracts: define schemas and permissioning up front to avoid lock-in fights later.

Measuring ROI that matters on farms

• Animal health: earlier detection, fewer false alarms, lower treatment costs.
• Yield and quality: milk output, weight gain, fertility metrics.
• Resource use: feed efficiency, energy use, medicine usage.
• Time saved: fewer manual checks, faster decision cycles.
• System reliability: downtime, missed events, and maintenance workload.

Trust is a feature

Build credibility with third-party audits, clear model cards, and security baselines. A helpful reference: the NIST AI Risk Management Framework.

Make it easy to export all data and leave. Paradoxically, that's what keeps users.

What the event asked of the industry

Conference co-organiser Paul Horwood (IVC Farm Vets) underscored the priority: the industry should steer which systems get built, educate farmers, support deployment, and build trust. AI is already touching every part of farm businesses, but direction must come from the people who run them.

Sponsors included HerdVision, VetImpress, Vetoquinol, OvaCyte, MSD Animal Health, IVC Farm Vets, Herdwatch, and Kynetec-signalling strong cross-sector interest in practical, farm-ready tools.

Action items for AI and engineering teams

• Ship a pilot with three farms before a full launch; iterate on their weekly feedback.
• Deliver offline-first UX and data sync that can survive days without a connection.
• Expose APIs and webhooks for farm software and vet systems.
• Price on outcomes (per healthy animal, per accurate alert) where feasible.
• Bundle training and support; 57% of farmers asked for it.

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