Artificial Intelligence (AI) Maturity in Small and Medium-Sized Enterprises: A Framework of Internalized and Ecosystem-Embedded Capabilities

TL;DR

This paper introduces a new multidimensional AI maturity framework tailored for SMEs, emphasizing ecosystem integration and organizational realities to better assess and guide AI development in smaller enterprises.

Contribution

It develops a novel SME-specific AI maturity model with eight capability dimensions, five levels, and four development pathways, addressing gaps in existing enterprise-centric frameworks.

Findings

Framework captures heterogeneity in SME AI adoption trajectories.

Emphasizes resource constraints and ecosystem dependence in AI maturity.

Provides a basis for future empirical validation and comparative studies.

Abstract

Artificial intelligence (AI) maturity models have proliferated, yet prevailing frameworks remain largely enterprise-centric, linear, and weakly aligned with the organizational realities of small and medium-sized enterprises (SMEs). This study develops a conceptual AI maturity framework explicitly grounded in SME contexts. Drawing on organizational capability theory, maturity model research, and the SME digital transformation literature, the framework reconceptualizes AI maturity as a multidimensional, non-linear, and ecosystem-embedded capability. It comprises eight interrelated capability dimensions, five maturity levels, and four archetypal development pathways, capturing heterogeneity in SME AI adoption trajectories. By foregrounding resource constraints, informal governance, owner-manager dominance, and external ecosystem dependence, the framework extends existing AI maturity theory and responds to calls for context-sensitive conceptualization of AI capability development. The study provides a foundation for future empirical validation and comparative research on AI maturity in SMEs, to measure their competitiveness, potential in self-development, and driving force in the SME context.