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Purpose There is a growing body of conceptual work considering the potential of AI in supply chain and procurement, and there is great interest in AI among managers. But, according to a recent study, digital strategies for procurement are often missing or not satisfactory. Literature offers conflicting guidance on possible adoption areas for AI in core procurement processes. Given the need for better digital strategies for procurement and the need to further develop the understanding of adoption potential, the purpose of this paper is to explore actual adoption levels, experienced benefits, readiness levels and barriers to implementation in industry. This informs nuanced, not hyped, managerial consideration and identifies further research opportunities. Design/methodology/approach Leveraging items used in literature to study adoption of other technologies, the authors conduct the first empirical exploration of actual adoption levels of AI in procurement. The authors do so by collecting survey responses in three manager workshops, and the authors use the workshops to seek manager input in the interpretation of findings and the identification of implications for managers and researchers. Findings There appears to be less consideration given to AI in procurement than interest in the topic might imply. Adoption levels are generally low, implying that there is a lot of room for the development of consideration, use cases and possible pilots by managers and researchers. The authors find procurement benefits of AI adoption to be broader than costs and productivity alone, including visibility and innovation. But, readiness appears to be at relatively low levels with factors commonly considered in literature, such as executive support and willingness to invest, less relevant than less widely considered elements such as human sense making and supplier readiness. Originality/value This first empirical exploration moves past conceptualization and the study of potential adoption into the study of actual adoption levels in different procurement core processes. The authors expand the consideration of readiness by including additional items of human sense making as called for in literature. The authors also include and develop supplier readiness consideration, which is often missing from research. With the help of participating managers, the authors are able to develop a more comprehensive framework for the consideration of AI adoption. This can help bring nuance, not hype, to consideration and provides a rich portfolio of research items and constructs to further explore.
Purpose There is a growing body of conceptual work considering the potential of AI in supply chain and procurement, and there is great interest in AI among managers. But, according to a recent study, digital strategies for procurement are often missing or not satisfactory. Literature offers conflicting guidance on possible adoption areas for AI in core procurement processes. Given the need for better digital strategies for procurement and the need to further develop the understanding of adoption potential, the purpose of this paper is to explore actual adoption levels, experienced benefits, readiness levels and barriers to implementation in industry. This informs nuanced, not hyped, managerial consideration and identifies further research opportunities. Design/methodology/approach Leveraging items used in literature to study adoption of other technologies, the authors conduct the first empirical exploration of actual adoption levels of AI in procurement. The authors do so by collecting survey responses in three manager workshops, and the authors use the workshops to seek manager input in the interpretation of findings and the identification of implications for managers and researchers. Findings There appears to be less consideration given to AI in procurement than interest in the topic might imply. Adoption levels are generally low, implying that there is a lot of room for the development of consideration, use cases and possible pilots by managers and researchers. The authors find procurement benefits of AI adoption to be broader than costs and productivity alone, including visibility and innovation. But, readiness appears to be at relatively low levels with factors commonly considered in literature, such as executive support and willingness to invest, less relevant than less widely considered elements such as human sense making and supplier readiness. Originality/value This first empirical exploration moves past conceptualization and the study of potential adoption into the study of actual adoption levels in different procurement core processes. The authors expand the consideration of readiness by including additional items of human sense making as called for in literature. The authors also include and develop supplier readiness consideration, which is often missing from research. With the help of participating managers, the authors are able to develop a more comprehensive framework for the consideration of AI adoption. This can help bring nuance, not hype, to consideration and provides a rich portfolio of research items and constructs to further explore.
This study investigates artificial intelligence and how it can be leveraged upon in order to re-engineer existing business models, towards achieving sustainable development goals. The study analysed factors necessary to ensure an artificial intelligence assisted sustainable business model innovation to provide a framework for businesses in emerging economies. A systematic literature review of existing literature is undertaken using the Scopus database. Filtering processes are employed to arrive at 94 journal articles. The study adopts the PRISMA protocol to allow a comprehensive disclosure of the review process. The review process resulted in the development of thematic areas which provided a framework that guided the study. Case studies were also undertaken, analysed in a qualitative manner in order to capture the perceptions and experiences as well as the problems associated with leveraging artificial intelligence in sustainable business model innovation. The research output includes recommendation and policy implication.
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