Artificial intelligence implementation in manufacturing SMEs: A resource orchestration approach
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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.
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Process innovation has a significant impact on the sustainability of SMEs.
“process innovation was also found to have a significant impact on the sustainability of SMEs”
From Innovation as A Driver of SMEs Sustainability: A Systematic Review of the Literature (2025) · cites this paper as Peretz-Andersson et al., 2024
The discussion passage says AI implementation can make manufacturing SMEs more sustainable, and the passage on sustainable solutions describes reduced material use. The paper does not establish that process innovation generally has a significant impact on SME sustainability; its finding concerns AI implementation in the manufacturing SMEs studied, without a reported significance test. (LLM, conf=0.86)
Experimental — accuracy not yet measuredAI application drives company productivity and performance and forms business models and core processes that increase competitiveness in SMEs.
“Based on research (Peretz-Andersson et al., 2024) which examines Innovationinnovation for SMEs sustainability, especially through the application of AI that drives company productivity and performance and forms business models and core processes that increase competitiveness.”
From Innovation as A Driver of SMEs Sustainability: A Systematic Review of the Literature (2025) · cites this paper as Peretz-Andersson et al., 2024
Section 5 describes AI improving manufacturing SMEs’ internal processes, efficiency, performance, and competitive advantage. However, the paper does not establish that AI reshapes their business models; on p. 13, it explicitly leaves open whether the AI-supported practices improve productivity. Those parts of the citing sentence overstate the findings. (LLM, conf=0.86)
Experimental — accuracy not yet measuredTechnologies such as 3D printing, automation, and AI can be used to increase production speed and precision, thereby reducing waste and improving product consistency.
“Technologies such as 3D printing, automation, and artificial intelligence (AI) can be used to increase production speed and precision, thereby reducing waste and improving product consistency (Peretz-Andersson et al., 2024).”
From Innovation as A Driver of SMEs Sustainability: A Systematic Review of the Literature (2025) · cites this paper as Peretz-Andersson et al., 2024
The discussion of production processes (p. 13) says AI can reduce time wasted in production and that sensors and production data can reduce scrap from faulty manufacturing. A manager also describes greater precision between robotic and machine interfaces. But the paper mentions 3D printing and automation as technologies that may complement AI in future research (p. 20); it does not establish that they deliver the claimed speed, precision, waste reduction, or product consistency. Improved product quality is reported, but product consistency is not specifically addressed. (LLM, conf=0.87)
Experimental — accuracy not yet measuredThe application of AI drives enterprise productivity and performance and shapes core business models and processes that enhance competitiveness.
“The application of AI drives enterprise productivity and performance and shapes core business models and processes that enhance competitiveness.”
From Innovation as A Driver of SMEs Sustainability: A Systematic Review of the Literature (2025) · cites this paper as Peretz-Andersson et al., 2024
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- Authors
- Peretz-Andersson, E., Tabares, S., Mikalef, P., & Parida, V.
- Affiliations
- Jönköping University (SE)
- Luleå University of Technology (SE)
- Norwegian University of Science and Technology (NO)
- Corresponding author
- Peretz-Andersson, Einav
- Venue
- International Journal of Information Management
- Publisher
- Elsevier BV
- Published
- 2024-08
- Volume
- 77
- Pages
- 102781
- ISSN
- 0268-4012 (ISSN-L)
- OpenAlex
- W4393910198
- Cited by
- 218
- References
- 65
- Citations by year
- 2026: 93 · 2025: 98 · 2024: 27
- FWCI
- 66.31
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- International Journal of Information Management (publishedVersion, cc-by)
- Osuva (University of Vaasa) (submittedVersion, cc-by)
- Epubl LTU (submittedVersion, cc-by)
- Funders
- Knowledge Foundation
- Topics
- Social Sciences › Decision Sciences › Management Science and Operations Research › Impact of AI and Big Data on Business and Society
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Digital Transformation in Industry
- Social Sciences › Business, Management and Accounting › Strategy and Management › Innovation and Knowledge Management
- Keywords
- AI implementation, small and medium-sized enterprises, manufacturing SMEs, resource orchestration theory, learning ability, governance capacity, digital transformation, competitive advantage, packaging industry, plastics industry, metal industry
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