From AI Readiness to Knowledge Capability: Competency Models for Generative AI Implementation
TL;DR
A competency-based AI readiness framework is developed that links implementation challenges with readiness requirements and role-specific competencies and develops conceptual propositions that can be examined in future empirical research.
Abstract
Generative artificial intelligence (GenAI) is changing how organisations create, share, apply and govern knowledge. However, its organisational value does not follow automatically from tool availability. Many GenAI initiatives remain limited to pilots, isolated experiments or local productivity gains because the knowledge resources, competencies and governance routines required for reliable use are not sufficiently defined. This conceptual paper examines how AI readiness and competency models can support knowledge-based GenAI implementation. It asks how recurring implementation challenges can be translated into organisational readiness requirements and then into role-specific competency dimensions. The paper draws on a conceptual synthesis of scholarly literature on AI implementation, AI readiness, AI capability, knowledge management, AI literacy, human-AI collaboration, organisational learning and AI governance. The synthesis is complemented by selected practice-oriented reports and anonymised insights from prior exploratory qualitative research on digital and AI-related competencies. The paper develops a competency-based AI readiness framework that links implementation challenges, including unclear problem definition, weak workflow integration, data quality and availability issues, governance gaps and resistance to change, with strategic, process-related, data-related, governance-related and human capability requirements. Its central argument is that competency models can function as knowledge management instruments. They make implicit GenAI-related knowledge requirements visible and translate them into technical, strategic, communicative and ethical-governance competencies. Existing approaches mainly catalogue organisational readiness factors, capability resources or individual AI literacy skills. The proposed framework goes one step further by linking implementation challenges with readiness requirements and role-specific competencies. In this way, competency models connect AI readiness with capability development, organisational learning, responsible use and sustainable value creation. By focusing on GenAI-supported knowledge work, the paper connects AI readiness and AI capability research with knowledge management. It proposes a framework for operationalising AI readiness through competency models and develops conceptual propositions that can be examined in future empirical research. The framework highlights how systematic competency development can strengthen knowledge flows, human-AI collaboration and the responsible use of GenAI in organisations.