The intersection of artificial intelligence, animal science, and business innovation stands as one of the most significant but underexplored areas of the 21st century. This paper presents the conceptual and integrative framework of BioCommerce Intelligence (BCI) —a transdisciplinary model where AI systems are developed, fine-tuned, and deployed using data from animal biology, ecology, and behaviour to generate quantifiable commercial value across pharmaceuticals, aquaculture, wildlife conservation finance, agribusiness, ecotourism, and supply chain management. This study constitutes a narrative integrative review with conceptual synthesis spanning six industry sectors. It integrates recent advances in computer vision, natural language processing, reinforcement learning, and generative AI applied to zoological datasets, and proposes a governance framework for responsible BCI deployment. The results indicate that AI models guided by zoological information outperform traditional business intelligence tools on key commercial metrics, while simultaneously enhancing conservation outcomes. The institutional divide between biological science and business has artificially constrained AI’s potential, and that BCI represents the next evolution of applied intelligence.
Sreevidhya, P. & Priya, P. (2026). BioCommerce Intelligence: Leveraging Artificial Intelligence at the Nexus of Zoology and Commercial Science. International Journal of Advanced Research in Commerce, Management & Social Science, 09(02(III)), 119–128. https://doi.org/10.62823/IJARCMSS/9.2(III).8983
- Ahmad, M., Nouh, A., Kalra, N., &Alqaoud, A. (2023). AI-driven livestock identification and insurance management system using muzzle pattern recognition. Birmingham City University Open Access Repository. https://www.open-access.bcu.ac.uk/14594/
- AI Singapore. (2023). Anchoring deep national capabilities in AI: 5-year report.https://aisingapore.org/wp-content/uploads/2024/01/AISG-5-year-report.pdf
- Berckmans, D. (2017). General introduction to precision livestock farming. Animal Frontiers, 7(1), 6–11. https://doi.org/10.2527/af.2017.0102
- Drucker, P. P., et al. (2024). Transformers enable accurate prediction of acute and chronic chemical toxicity in aquatic organisms. Environmental Science & Technology Letters, 11 (4), 284-292. https://doi.org/10.1021/acs.estlett.4c00123
- International Fund for Animal Welfare. (2024). Disrupting wildlife trade with advanced AI solution [Press release]. https://www.ifaw.org/press-releases/disrupting-wildlife-trade-advanced-ai-solution
- IPBES. (2023). Summary for policymakers of the methodological assessment of the diverse values and valuation of nature of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services.https://www.ipbes.net
- Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., Žídek, A., Potapenko, A., Bridgland, A., Meyer, C., Kohl, S. A. A., Ballard, A. J., Cowie, A., Romera-Paredes, B., Nikolov, S., Jain, R., Adler, J.,… Hassabis, D. (2021). Highly accurate protein structure prediction with AlphaFold. Nature, 596(7873), 583–592. https://doi.org/10.1038/s41586-021-03819-2
- Mozer, A., & Prost, S. (2023). An introduction to illegal wildlife trade and its effects on biodiversity and society. Forensic Science International: Animals and Environments, 3, Article 100064. https://doi.org/10.1016/j.fsiae.2023.100064
- OECD. (2024). Tracking economic instruments and finance for biodiversity 2024. Organisation for Economic Co-operation and Development. https://www.oecd.org/en/data/insights/data-explainers/2024/10/tracking-economic-instruments-and-finance-for-biodiversity-2024.html
- Ragab, S., et al. (2025). Overview of aquaculture artificial intelligence (AAI) applications. Open Agriculture. Advance online publication. https://doi.org/10.2478/aoas-2024-0075
- UNODC. (2024). World Wildlife Crime Report 2024: Trafficking in protected species. United Nations Office on Drugs and Crime. https://www.unodc.org/documents/data-and-analysis/wildlife/2024/WWCR4_english.pdf
- Wu, Z., Zhang, C., Gu, X., Duporge, I., Hughey, L., Stabach, J., Skidmore, A., Hopcraft, J. G. C., Lee, S., Atkinson, P. M., ... Wang, T. (2023). Deep learning enables satellite-based monitoring of large populations of terrestrial mammals across heterogeneous landscapes. Nature Communications, 14 (1), Article 3181. https://doi.org/10.1038/s41467-023-38901-y.
