SMEs in one of the EU's poorest regions say lack of skills, money and social networks — not just technology — blocks AI adoption; without targeted investment in digital literacy and civic intermediation, AI risks entrenching rather than alleviating local employment inequalities.

Main Finding

AI adoption in peripheral European economies (Eastern Macedonia and Thrace) is driven less by pure technology costs and more by social and human capital constraints. Knowledge deficits, financial limits, brain drain, weak institutions, and thin civic networks block SMEs from realizing AI's benefits; without concurrent investment in digital literacy, organizational culture, and inter-firm/social capital, AI will tend to reproduce rather than reduce regional employment and development inequalities.

Key Points

• Research gap: Most studies focus on advanced urban economies; peripheral SMEs in weak-capital, thin-infrastructure regions are underexplored.
• Primary barriers to AI adoption: knowledge/skills deficits and financial constraints.
• Enablers: external technology partnerships, targeted education/training, and economic incentives.
• Mediators: availability of social and human capital, institutional thickness, and civic intermediation strongly shape adoption trajectories.
• Socioeconomic context: ongoing brain drain and institutional thinness create a biocultural environment that hampers sustainable AI diffusion.
• Distributional risk: Without parallel investments in human/social capital and networks, AI adoption risks reinforcing employment inequalities in disadvantaged regions.
• Policy alignment: Findings have direct relevance for EU Cohesion programming and Sustainable Development Goals 4 (education), 8 (decent work), 9 (industry/innovation), 10 (reduced inequalities), and 17 (partnerships).

Data & Methods

• Setting: Eastern Macedonia and Thrace (EMT), one of the EU's least developed regions.
• Sample: Twelve semi-structured interviews with SME owners and managers, conducted in early 2025.
• Analytical framework: Bitsani’s Biocultural City framework — treats human, social, and cultural capital as interdependent dimensions of regional sustainability.
• Analysis: Thematic qualitative analysis using Atlas.ti; produced 19 codes organized into six higher-level categories (capturing barriers, enablers, mediators, workforce dynamics, institutional context, and policy/SDG links).
• Limitations: Small, qualitative sample; findings are context-specific and hypothesis-generating rather than statistically generalizable.

Implications for AI Economics

• Models of AI diffusion should include social capital, brain drain, institutional thickness, and organizational culture as core explanatory variables, not just costs and productivity parameters.
• Policy design: Effective AI-supportive interventions in peripheral regions must be bundled — technology subsidies alone are insufficient. Complementary measures should include:
  • targeted digital literacy and upskilling programs tailored to SMEs,
  • incentives that lower upfront adoption risk for small firms,
  • facilitation of external technology partnerships and knowledge exchange,
  • investments in civic intermediaries and inter-firm networks to sustain social capital.
• Distributional outcomes: Economists and policymakers should expect heterogeneous labor impacts across regions; without targeted human-capital interventions, AI may exacerbate regional unemployment and inequality.
• Evaluation & funding priorities: EU Cohesion funds and SDG-related programs should prioritize integrated interventions that link AI investment to education, local institution-building, and partnership development.
• Research agenda: Need for mixed-methods and longitudinal studies measuring how social-capital interventions alter AI uptake, productivity gains, and employment outcomes in peripheral SMEs.