Smart Barns, Digital Kennels: How AI is quietly revolutionising animal care

In September 2021, Welsh dairy farmer Ceredig Evans glanced at his phone just as the morning milking finished. An overhead AI (artificial intelligence) camera from the start-up, CattleEye had quietly flagged the faintest hitch in a Holstein’s stride, marking her “at risk of lameness”. Evans trimmed the hoof that same afternoon, preventing a painful lesion and an expensive drop in milk yield days before a human eye would have noticed.

AI is helping us understand animals in a way that was once unimaginable. From large cattle farms to remote wildlife habitats, AI enables continuous, real-time monitoring, offering a powerful new lens into the health and behavior of animals. 

Using sensors, cameras, and computer vision, AI can track animal movements, detect signs of discomfort, or even assess emotional states. For instance, gait analysis powered by AI can recognize lameness in cows by analysing subtle changes in how they walk. In poultry farming, smart systems monitor crowd density and behaviour to detect early signs of disease or aggression, improving welfare and reducing losses. 

Animal emotion recognition is also advancing rapidly. Machine learning models have been used to analyse facial expressions in cats and horses to detect pain, using tools like the Horse Grimace Scale and facial landmark detection.

One study used deep learning to accurately identify pain levels in cats based on facial features, achieving over 72% accuracy, a step forward in ensuring silent suffering doesn’t go unnoticed. 

AI also assists in livestock management through smart farming platforms. In dairy herds, behaviour data collected over years have been used to predict daily milk yield and optimise feeding routines, reducing stress and improving productivity. Drones and robots now work alongside AI to monitor large herds, spot sick animals, and ensure proper environmental conditions, like temperature and air quality. 

Beyond agriculture, AI-powered identification systems based on facial recognition have been tested on wildlife such as giant pandas, with over 90% accuracy, offering a boon for conservation efforts. By offering a 24/7 watchful eye, AI doesn’t replace the human-animal bond, it strengthens it, helping us detect issues earlier, respond faster, and ultimately create safer, more humane environments for animals. 

AI offers tremendous promise for animal health, but “with great power comes great responsibility, for the veterinarian, the farmer and the coder alike.”

As technology advances, so do the ethical questions it raises: How do we ensure transparency? Who owns the data? And how do we balance automation with human touch? One of the biggest challenges is data quality. AI depends on large, clean datasets to learn and make accurate predictions. Yet in veterinary medicine, data are often inconsistent, fragmented, and species-specific, which can limit model accuracy and fairness. This becomes especially critical when AI systems are used for decisions that directly affect animal welfare, such as pain detection or treatment recommendations. 

Another concern is the “black box” nature of some AI systems. Deep learning models can make impressively accurate predictions, but often without explaining how they got there. This lack of interpretability makes it difficult for veterinarians to trust AI-assisted diagnoses or treatments, especially in complex or high-stakes cases. 

Privacy and security also matter, particularly as more farms and clinics adopt wearable tech, sensors, and video systems. The storage and use of animal health data must be protected, especially when tied to proprietary systems or linked to broader food supply chains. 

Equally important, AI should augment, not sideline, veterinary expertise, freeing clinicians to focus on nuanced judgment and the human–animal bond. Still, with careful design, AI can become an ethical tool. Tools like Explainable AI (XAI) are being developed to help practitioners understand model decisions. AI is also helping reduce animal suffering, enabling early intervention, improving welfare, and even guiding breeding to avoid inherited disorders.

Going forward, interdisciplinary collaboration will be key. Veterinarians, data scientists, ethicists, and policymakers must work together to ensure AI serves not just efficiency, but compassion. With the right guardrails, AI can help us not only do more but do better for animals.