Ready or Not, AI Comes—An Interview Study of Organizational AI Readiness Factors
Citation. Jöhnk, J., Weißert, M., & Wyrtki, K. (2021). Ready or Not, AI Comes—An Interview Study of Organizational AI Readiness Factors. Business & Information Systems Engineering, 63(1), 5–20. https://doi.org/10.1007/s12599-020-00676-7
Summary. This paper conceptualizes organizational AI readiness using interviews with 25 AI experts, triangulated with scientific and practitioner literature. It identifies five readiness categories — strategic alignment, resources, knowledge, culture, and data — containing 18 factors and 58 illustrative indicators. The central contribution is to treat AI readiness as purpose- and context-specific, continuously developing through the adoption process rather than as a static precondition.
Key claims
- AI readiness is constituted by five categories: strategic alignment, resources, knowledge, culture, and data.
- The 18 readiness factors are: AI-business potentials, customer AI readiness, top management support, AI-process fit, data-driven decision-making, financial budget, personnel, IT infrastructure, AI awareness, upskilling, AI ethics, innovativeness, collaborative work, change management, data availability, data quality, data accessibility, and data flow.
- AI adoption and AI readiness are mutually reinforcing: readiness fosters adoption, while smaller successful AI adoption experiences build readiness for later and broader adoption purposes.
- Because AI is a general-purpose technology, readiness assessment must be tied to the organization’s specific adoption purpose and context; the relevant factor weights and priorities shift over time.
- The paper’s indicators are illustrative and require future validation; the study is exploratory and based on expert interviews, literature triangulation, and a researcher focus-group card-sorting procedure.
Evidence & limitations
- Design: qualitative interview study with 25 AI experts; open and axial coding; triangulation with readiness/adoption literature and practitioner studies; focus-group card sorting with AI-related researchers to refine factors and indicators.
- Limitations: exploratory conceptual work rather than validated measurement; the illustrative indicators are not psychometrically validated; the paper does not estimate factor weights, prioritization, or causal effects on adoption success.