Multidimensional AI Readiness Framework for Small and Medium-sized Enterprises in an Emerging Economy: Evidence from Pakistan

Abstract

Artificial Intelligence (AI) holds transformative potential for small and medium-sized enterprises (SMEs), yet its adoption in emerging economies remains low due to context-specific barriers. Existing AI readiness models, predominantly derived from developed economies, overemphasize technological factors. To address this gap, this study developed a comprehensive, empirically-grounded framework that captures the crucial strategic, organizational, and regulatory challenges salient in resource-constrained contexts, explicitly elevating 'Regulatory' to a standalone dimension. Evidence was drawn from Pakistani SMEs. A qualitative, multiple-case study methodology was employed, drawing on data from semi-structured interviews with management across three SME sectors (manufacturing, services, and primary). Data was analyzed using NVivo-12 for thematic coding and cross-case analysis. The research culminates in a multidimensional AI readiness framework comprising five critical dimensions: (1) Strategic, (2) Technological, (3) Organizational, (4) Environmental, and (5) Regulatory. The findings revealed a significant AI readiness gap among Pakistani SMEs, characterized by a universal lack of regulatory guidance, varying levels of technological infrastructure, and a strong dependence on top management support. Across the three sectors, only one SME progressed beyond the initial AI readiness stage, while all three showed very low regulatory preparedness, indicating a severe readiness deficit. Technological maturity ranged from minimal digitization to partial AI experimentation, indicating a pronounced technology readiness gap across these sectors. The proposed framework would provide SME managers and policymakers with a practical diagnostic tool to systematically evaluate their organizational AI readiness. Furthermore, it would also provide clear levers for intervention. This highlights the need for strategic vision, skills development, and the creation of internal AI policies in the absence of robust national frameworks.