Journal of Small Business Management

La Sapienza Colloquium: Reimagining Small Business Futures - AI, Inclusion, and Territorial Innovation

The global innovation landscape is undergoing a profound restructuring. Collaboration among geographically distributed innovation ecosystems is intensifying. At the same time, the rapid advancement and diffusion of artificial intelligence (AI) are reshaping the dynamics of global industrial competition (Secundo et al., 2025). New innovation hubs are emerging across the Asia-Pacific region, Europe, and several emerging economies. In parallel, digital technologies, platformization, and the decentralization of computing power are reducing traditional geographical and scale barriers. As a result, the conditions for competition and innovation are being fundamentally redefined. In this context, small and medium-sized enterprises (SMEs) are no longer structurally disadvantaged. Instead, they can increasingly act as agile and innovation-driven players within global value chains (Schwaeke et al., 2025). AI-enabled technologies offer SMEs unprecedented opportunities. They can enhance productivity, improve decision-making, automate processes, and reconfigure business models toward more scalable and data-driven architectures (Kopka & Fornahl, 2024; Schwaeke et al., 2025; Colombelli et al., 2026). At the same time, AI adoption poses significant challenges related to investment capacity (Lui et al., 2022), skills gaps (Brynjolfsson et al., 2021), cybersecurity risks (Novelli et al., 2024), ethical concerns (Berente et al., 2021), and unequal access to digital infrastructures (Varriale et al., 2026). Unlike large corporations, SMEs operate as socio-economic infrastructures deeply intertwined with local production systems, community networks, and place-based development trajectories (Cainelli et al., 2012; Forti et al., 2026).

We aim to bridge these conversations by examining how AI can reshape the future of small and medium-sized enterprises (Oldemeyer et al., 2025) through three interconnected lenses:

• Technological transformation: In a complex environment of accelerated global technological iteration and increasingly tight resource constraints, how can SMEs, leveraging their own strengths and regional industrial foundations, scientifically adopt and adapt AI technologies, and deeply integrate them into daily operations, medium- and long-term development strategies, and business model innovation to achieve technological transformation and sustainable growth?
• Inclusive innovation: How can AI, by broadening pathways such as financing support, knowledge sharing, market access, and digital infrastructure integration, build a universally beneficial digital ecosystem for diverse entrepreneurial entities and disadvantaged or marginalized entrepreneurial groups? This can effectively alleviate structural inequalities in the market, capital, and capabilities, and promote more equitable and inclusive innovative development.
• Territorial and ecosystem development: How can AI-enabled SMEs, through innovation spillovers, industrial collaboration, and value co-creation, enhance territorial economic resilience, improve territorial innovation systems, support the implementation of local intelligent specialization strategies, and provide a solid micro-foundation and innovative impetus for territorial characteristic-based development policies and territorial governance?

We particularly encourage contributions that move beyond technology adoption perspectives to explore how AI reconfigures organizational capabilities, entrepreneurial cognition, stakeholder relationships, and value creation mechanisms in SMEs.

Key themes

• Possible themes include, but are not limited to:
• AI-driven business model innovation in SMEs
• Digital transformation pathways under technological turbulence
• AI and sustainable competitive advantage in resource-constrained firms
• AI-enabled work redesign and organizational reconfiguration in SMEs
• Inclusive AI ecosystems for minority, women, migrant, and informal entrepreneurs
• Algorithmic Bias and transparency in AI-assisted SMEs
• AI-enabled territorial innovation and smart regional development
• Public policies supporting equitable AI adoption among SMEs
• AI, cybersecurity, and risk governance in small businesses
• Human–AI collaboration and entrepreneurial decision-making
• Data strategies and knowledge management in SMEs
• Data ownership and territorial digital autonomy in AI-driven SME development
• Ethical, social, and governance implications of AI adoption
• AI platforms and SME participation in digital value chains
• AI for local resilience, crisis response, and adaptive entrepreneurship

References

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• Brynjolfsson, E., Rock, D., & Syverson, C. (2021). The productivity J-curve: How intangibles complement general purpose technologies. American Economic Journal: Macroeconomics, 13(1), 333-372.
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• Schwaeke, J., Peters, A., Kanbach, D. K., Kraus, S., & Jones, P. (2025). The new normal: The status quo of AI adoption in SMEs. Journal of Small Business Management, 63(3), 1297-1331.
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