Adopting artificial intelligence in small and medium businesses: the knowledge-based perspective
Unlocking AI for SMB Growth: A Knowledge-Driven Approach
This study explores how Small and Medium Businesses (SMBs) can effectively adopt Artificial Intelligence (AI) by strategically combining internal investments in R&D and digital technologies with external knowledge sourcing, challenging traditional views on resource allocation.
Executive Impact: Strategic AI Adoption for SMBs
AI adoption significantly boosts SMB performance through enhanced productivity, innovation, and competitive advantage. Our research highlights the critical role of strategic knowledge investment and recombination, moving beyond isolated technology decisions to integrated capability building.
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Increase in Application-Oriented AI adoption from ICT investment
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Increase in Application-Oriented AI adoption from R&D investment
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SMBs adopting AI (Big Data) in 2019
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Increase in Application-Oriented AI adoption from Knowledge Spillovers
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Theoretical Foundation: The TOE Framework & Recombinant Knowledge
Our study adapts the Technology-Organization-Environment (TOE) theoretical framework, combined with the recombinant knowledge approach (RKA), to analyze AI adoption in Small and Medium Businesses (SMBs). The TOE framework posits that technological, organizational, and environmental contexts influence innovation adoption. We refine this by incorporating RKA, which emphasizes that innovation emerges from integrating diverse knowledge domains.
Specifically, the technological context refers to investments in digital and information technology (ICT), facilitating digital infrastructure. The organizational context encompasses investments in internal R&D, a crucial resource for adopting new innovative technologies. The environmental (external) context involves purchasing external knowledge, R&D, knowledge spillovers, and collaboration with external partners.
This integrated lens allows us to examine how SMBs, often constrained by limited resources, strategically combine these internal and external knowledge sources to foster AI adoption, rather than treating them as isolated inputs.
Enterprise Process Flow
Assess Internal Digital Infrastructure (ICT)
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Evaluate Internal R&D Capabilities
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Identify External Knowledge Sources
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Develop Absorptive Capacity
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Strategic Knowledge Recombination
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Successful AI Adoption
Quantitative Insights: Drivers and Inhibitors of AI Adoption
Our regression analysis, utilizing micro-level data from UK SMBs (2010–2020), reveals distinct patterns in the adoption of application-oriented AI (robotics) and learning-oriented AI (machine learning/big data analysis).
+75%
Increase in Application-Oriented AI adoption from ICT investment
+16%
Increase in Application-Oriented AI adoption from Internal R&D
These findings (H1 & H2) highlight the foundational role of internal technological and organizational capabilities in preparing SMBs for AI. However, direct external R&D purchases (H3) did not show a direct positive association with AI adoption without interaction terms.
Complex Interactions: Substitutes, Not Complements
Surprisingly, our results show that internal R&D and ICT investments are substitutes, not complements, for AI adoption (against H4). SMBs with limited resources often prioritize one over the other. Similarly, combining internal R&D/ICT with external knowledge (spillovers, collaboration) showed negative associations for AI adoption (against H5), indicating a substitution effect where external sourcing can overwhelm internal efforts or become less effective without sufficient internal absorptive capacity.
+12%
More likely to adopt Application-Oriented AI with knowledge spillovers
| Characteristic | Application-Oriented AI (Robotics) | Learning-Oriented AI (Big Data Analysis) |
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| ICT Investment Impact |
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| Internal R&D Investment Impact |
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| External R&D Purchase |
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| Knowledge Spillovers |
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| Knowledge Collaboration |
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| Age & Size |
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| STEM Degree Employees |
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| Internal R&D x ICT Interaction |
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| Internal x External Knowledge Interaction |
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Voices from the Field: SMB Leaders on AI Adoption
Interviews with Chief Information Officers (CIOs) from UK SMBs that adopted AI provide rich contextual understanding, complementing our quantitative findings. Leaders consistently emphasized the strategic importance of internal capabilities and external collaborations.
Internal Knowledge Investment is Paramount
"R&D is very important. AI is very costly, it requires investment in compute time and requires investment and data. It's also very important to sort of extend the existing knowledge of AI and particularly address particular areas that are important in healthcare."
— E3, Product Manager for AI Services
Strategic Recombination of R&D and ICT
"What we really need to get right is what we invest in R&D and what we invest in ICT. Our R&D unit is completely dedicated to the development of AI technology, but ICT is there to support it with specific software and platforms. Therefore, by recombining R&D and ICT that by combining both we are able to supply our R&D team focusing on AI development with an appropriate IT tool."
— E4, Co-Founder and Chief Executive Officer
Collaboration as a Key External Source
"It is true we are never limited by internal knowledge we are looking to engage with external partners in particular at the trade conferences and fairs. We use this opportunity both to learn from our competitors and to access their valuable knowledge..."
— E4, Co-Founder and Chief Executive Officer
Strategic Imperatives for SMBs
SMB managers must carefully balance internal investments (R&D, ICT) with external knowledge sourcing (spillovers, collaboration) due to observed substitution effects. Instead of pursuing all options simultaneously, a selective and context-sensitive strategy is crucial, aligning knowledge investments with specific innovation goals and AI technology types (application-oriented vs. learning-oriented).
Focus on building internal absorptive capacity, including human capital (STEM professionals) and digital infrastructure, which enables effective integration of external AI solutions. Engagement in deeper collaborations with external partners (suppliers, customers, competitors) is vital for accessing cutting-edge insights and fostering AI readiness.
Limitations and Future Research Directions
This study acknowledges limitations, primarily the reliance on robotics as a proxy for application-oriented AI in the full sample, which may under-measure broader forms of AI. Future research should leverage longitudinal data and alternative data sources (e.g., patent data, company websites) for a more comprehensive capture of AI adoption across various technological forms.
Further investigation into the identified substitution effects between knowledge sources, as well as the impact of global economic conditions on AI adoption, would enrich the understanding of strategic resource allocation for SMBs in a dynamic AI landscape.
Advanced ROI Calculator: Quantify Your AI Impact
Estimate the potential savings and efficiency gains your SMB could achieve through strategic AI adoption.
Annual Cost Savings
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Annual Hours Reclaimed
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Your Strategic AI Implementation Roadmap
A structured approach is key for SMBs to navigate AI adoption effectively, balancing internal capabilities with external opportunities.
Phase 1: Knowledge Audit & Strategy Alignment
Assess existing internal R&D, ICT infrastructure, and human capital. Define clear AI adoption goals aligned with business objectives, considering both application-oriented and learning-oriented AI types.
Phase 2: Targeted Knowledge Sourcing & Development
Identify critical knowledge gaps. Opt for selective external knowledge acquisition (spillovers, collaborations) or internal development (R&D, training) based on resource constraints and technology type, avoiding simultaneous pursuit where substitution effects are high.
Phase 3: Pilot Implementation & Absorptive Capacity Building
Pilot AI solutions with manageable scope. Actively build internal absorptive capacity through continuous learning and process adjustments. Emphasize digital readiness and data management for effective integration.
Phase 4: Scaling & Ecosystem Integration
Scale successful pilots across the organization. Foster deeper collaborations within the AI ecosystem (suppliers, customers, competitors) to leverage external expertise and overcome resource limitations, positioning your SMB as an AI innovator.
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