Artificial intelligence and machine learning technologies are transforming the ability to monitor animal welfare at scale, enabling earlier detection of health and welfare problems across multiple species.
AI welfare monitoring represents one of the most promising near-term opportunities to improve farm animal welfare at scale. Traditional welfare assessment relies on periodic inspection by trained staff, creating long windows during which welfare problems go undetected. Continuous automated monitoring can flag individual animals showing abnormal behaviour, vocalisation patterns, or physiological indicators within minutes of problem onset. The welfare gain from earlier intervention is potentially very large, particularly for painful conditions like lameness, mastitis, and respiratory disease. However, technology is not a substitute for adequate stockpersonship - the most effective systems combine automated detection with rapid human response protocols.