From Readiness to Deployment: Evaluating AI Capabilities in Traditional Enterprises

Abstract

The adoption of artificial intelligence (AI) in traditional enterprises remains challenging despite significant investments. While research indicates that many traditional enterprises demonstrate considerable AI readiness, a substantial gap exists between readiness levels and actual AI implementation. This paper proposes a novel Multi-Dimensional AI Readiness Assessment (MDARA) framework that bridges this gap by integrating technological infrastructure, organizational capabilities, data readiness, and implementation strategy dimensions. The framework incorporates a dynamic scoring mechanism that not only assesses current readiness but also provides actionable pathways to implementation. Through a systematic literature review and case study analysis, we identify 28 key indicators across 4 dimensions and develop a weighted assessment model. The proposed framework addresses a critical research gap by providing traditional enterprises with a structured approach to AI adoption, moving beyond a static readiness assessment to enable dynamic capability development. Our contributions include a comprehensive multi-dimensional framework for AI readiness assessment, a dynamic scoring mechanism that accounts for implementation barriers, and practical guidelines for traditional enterprises to transition from readiness to implementation.