Interpretive Summary: Artificial intelligence for animal science: from applications to integrated knowledge systems

By: Mutian Niu, Chuanyi Guo, Victor E Cabrera

Implications

• AI is shifting from discrete tools to a system-level integrator, requiring a holistic approach to manage farm ecosystems rather than isolated disciplines.
• Advanced AI transforms farms into real-time living laboratories, accelerating knowledge creation and positioning AI as a co-producer of scientific discovery.
• The next frontier is a multiscale vision for AI, integrating data across molecular, animal, herd, and environmental levels to sustainably manage agricultural ecosystems.
• Responsible AI deployment requires co-developed frameworks for ethics, data governance, and equity to protect animal welfare, ensure farmer agency, and build trust.
• Practitioners should view integrated AI as a long-term investment. Emerging evidence shows measurable productivity and environmental gains over 3–5 year periods, but return of investment timelines will vary by farm.

Introduction

Animal science is at a critical juncture, faced with the challenge of providing sustainable nutrition for a growing global population. Against this backdrop, Artificial Intelligence (AI) has emerged as a powerful transformative force, offering the potential to enhance efficiency, improve animal welfare, and reduce environmental footprints (Distante et al., 2025). To date, the application of AI has largely taken the form of discrete, stand-alone solutions, with specific tools successfully addressing challenges in areas such as health monitoring and feed optimization. This toolbox paradigm has yielded significant gains and demonstrated the clear value of data-driven management (Cabrera, 2024; Menezes et al., 2024).

However, a fundamental leap in the technology itself now invites a more ambitious vision. Whereas early AI was primarily analytical, tasked with identifying patterns in existing data, contemporary generative approaches, driven by large language models, vision models, and AI agents (systems that can perceive their environment and take autonomous actions), have begun to demonstrate synthetic reasoning and proactive planning capabilities (Li et al., 2025). Such a qualitative progression, from analytical to generative AI, has the potential to transform the concept of an integrated knowledge system from a distant aspiration into an attainable reality.

It is this transition and its profound implications that this review seeks to explore.

Read more in Animal Frontiers: From Isolated Data to Integrated Ecosystems.