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Proposing an artificial intelligence maturity model to illustrate a road map for cleaner animal farming management

Author

Listed:
  • Erfan Shakeripour

    (Islamic Azad University of Kazeroon)

  • Mohammad Hossein Ronaghi

    (Shiraz University)

Abstract

Traditional agriculture has jeopardized national resources given the limited availability of natural resources. On the other hand, artificial intelligence (AI) has resulted in more efficient resource utilization. Nowadays, animal agriculture is much more sustainable with the help of artificial intelligence. Furthermore, the rate of AI maturity in animal agriculture provides a roadmap for optimizing its integration into it, which is of great concern to enterprise managers and policymakers. According to the literature, there is no AI maturity model in the animal agriculture sector to assess the latter. The current study was carried out in four phases. First, the literature shed light on the dimensions of AI and its applications in animal agriculture. Second, animal agricultural experts ranked the AI dimensions using the Best-Worst Method (BWM). In the third phase, a model was developed to assess AI maturity across all dimensions of AI technology and AI applications in animal agriculture. Finally, a company maturity assessment tested the proposed model by questionnaire. The research findings show that health monitoring is the most important AI application in animal agriculture. Also, the company under study showed great individual identification maturity. The research is original in that it determines the importance of AI in animal agriculture and introduces an AI maturity model in the animal agriculture sector.

Suggested Citation

  • Erfan Shakeripour & Mohammad Hossein Ronaghi, 2024. "Proposing an artificial intelligence maturity model to illustrate a road map for cleaner animal farming management," Operations Management Research, Springer, vol. 17(4), pages 1257-1269, December.
    • Handle: RePEc:spr:opmare:v:17:y:2024:i:4:d:10.1007_s12063-024-00502-3
    DOI: 10.1007/s12063-024-00502-3
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