Enablers of artificial intelligence adoption and implementation in production systems

Artificial intelligence (AI) is considered a mechanism that can improve supply chain resilience. Organisations around the world are investing in implementing AI systems to improve their supply chain and become more resilient to pandemics and disruption. At the same time, practitioners are not fully aware of the factors that impact the implementation of these systems. Alongside this, the extant literature lacks a comprehensive study that evaluates the enablers impacting the implementation of AI in production systems. This research fills this gap by identifying, defining, and evaluating the critical enablers influencing the adoption and implementation of AI in production systems. We extracted twelve enablers, created a conceptual model, and categorised the enablers based on the Technology, Organization, and Environment (TOE) framework. After categorisation, we used the analytical hierarchy process to assess the importance of the enablers presented in the model using data collected from eight experts. The results revealed that technology, as a category, is more crucial than organisation or environment. The findings also indicated that project management is the most critical of all twelve enablers. We discuss the implications of the analysis for practitioners and researchers. We also offer twelve propositions that researchers can empirically assess in future studies.

Automated summary

Artificial intelligence (AI) is seen as a way to enhance supply chain resilience, with organizations investing in AI systems for this purpose. However, there is a lack of understanding about the factors that affect the implementation of these systems. This study fills this gap by identifying and evaluating the critical enablers of AI adoption and implementation in production systems. The findings highlight the importance of technology and project management in this process, and offer propositions for future empirical research.