Artificial intelligence implementation in manufacturing SMEs: A resource orchestration approach
2024 (English) In: International Journal of Information Management, ISSN 0268-4012, E-ISSN 1873-4707, Vol. 77, article id 102781Article in journal (Refereed) Published
Abstract [en]
Artificial intelligence (AI) is playing a leading role in the digital transformation of enterprises, particularly in the manufacturing industry where it has been responsible for a profound transformation in key business and production operations. Despite the accelerated growth of AI technologies, knowledge of the implementation of AI by small and medium-sized enterprises (SMEs) remains underexplored. Thus, this study seeks to examine how manufacturing SMEs orchestrate resources for AI implementation. Building on the resource orchestration (RO) theory and recent work on AI implementation, we investigate multiple case studies involving manufacturing SMEs in Sweden operating in the packaging, plastic, and metal sectors. Our findings indicate that SMEs structure a portfolio based on acquiring and accumulating AI resources. AI resources are bundled into learning and governance capabilities to leverage configurations for AI implementation. Through a dynamic process of AI resource orchestration, SMEs effectively leverage AI resources and capabilities by mobilising technologies, coordinating manufacturing processes, and empowering skilled people. This research contributes to existing practice and the academic literature on AI implementation, highlighting how SMEs orchestrate AI resources and capabilities to drive an organisation’s digital transformation whilst creating a competitive advantage.
Place, publisher, year, edition, pages Elsevier, 2024. Vol. 77, article id 102781
Keywords [en]
AI, Artificial intelligence, Resources, Capabilities, Manufacturing, Resource orchestration, Digital transformation, Small and medium-sized enterprises (SMEs), Competitive advantage
National Category
Computer Sciences Business Administration
Identifiers URN: urn:nbn:se:hj:diva-63931 DOI: 10.1016/j.ijinfomgt.2024.102781 ISI: 001222220800001 Scopus ID: 2-s2.0-85189496050 Local ID: HOA;intsam;944528 OAI: oai:DiVA.org:hj-63931 DiVA, id: diva2:1848407
2024-04-032024-04-032024-09-10 Bibliographically approved