Human-centered AI adoption for small and medium-sized enterprise competitiveness: a methodological perspective on digital capabilities and organizational adaptation

Abstract

Small and medium-sized enterprises (SMEs) are widely expected to benefit from artificial intelligence (AI), yet adoption remains uneven and many firms stall after an initial pilot. Capability-based research has established that internal digital and dynamic capabilities predict adoption and performance more strongly than external support, but it rarely specifies the design conditions under which employees keep using a tool long enough for capability to form, and the organizational adaptation through which capability becomes performance is usually inferred rather than observed. This Perspective proposes the Human-Centered AI Adoption Framework to join those literatures. The framework links four human-centered principles, a deliberately restricted subset concerned with control, comprehension, appropriate use and accountability, to the capability microfoundations of sensing, seizing and reconfiguring; treats structural, cultural and human-capital adaptation as the realized organizational state that reconfiguring produces, distinct from the activity itself; and distinguishes four dimensions of competitiveness that realize in a characteristic order, alongside worker outcomes treated as ends rather than only as means. A cross-cutting safeguards dimension records the wider requirements of responsible AI whose SME-scale mechanisms are not yet specified. Two objectives are expressed as six propositions, each paired with candidate indicators and a design capable of testing it. Because the article presents no original data, the propositions are candidates for validation rather than results.