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Paper short abstract:

Adopting AI technologies is complex and requires careful consideration of positive and negative consequences. To better understand AI adoption, we present a framework that examines multiple nested factors, from the individual level to the enabling environment and technology characteristics.

Paper long abstract:

Ataharul Chowdhury & Uduak Edet

The adoption of artificial intelligence (AI) in Ontario’s agri-food sector is inevitable and will enhance competitiveness, create market opportunities, and strengthen the industry. However, without responsible innovation that considers societal concerns, the sector may encounter challenges such as apathy toward adoption, job displacement, inequality, and ethical dilemmas. The extent of AI adoption varies across different sectors and value chains, requiring distinct skill sets for workers depending on the specific AI technologies utilized. This literature review examines various AI technologies and frameworks sourced from scholarly databases, reports, and government publications, focusing on their applications in the Canadian horticulture and livestock sectors. As part of an exploratory phase in a broader research project, this review establishes criteria for assessing the factors that influence the adoption of emerging AI technologies. It employs an analytical framework to evaluate AI tools based on their descriptive, diagnostic, predictive, and prescriptive features. Key AI technologies highlighted in this review include tools for crop and livestock disease detection, as well as technologies aimed at improving breeding and feeding efficiency. These technologies are classified into content layer categories, and potential barriers to adoption are identified, taking into account individual, social, environmental, and institutional factors. This review proposes a framework for analyzing the adoption of AI technologies, emphasizing the importance of responsible innovation, technological characteristics, and systemic change.