Aug 2026· European Conference on Knowledge Management· 0 citations· 28 references
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.
Organizations increasingly recognize Artificial Intelligence (AI) as a strategic capability, yet many struggle to assess and understand their true readiness to adopt and scale AI solutions. Existing AI readiness assessments rely mostly on static surveys and internally collected data, which often fail to capture organizational dynamics and cross-functional dependencies. They also impose substantial time demands while delivering limited perceived value to participants, particularly in Small Medium Business (SMB) companies. Consequently, assessment outcomes frequently generate fragmented insights, limiting their usefulness for strategic forecasting and organizational learning. This study builds on conceptual framework proposing multi-agent conversational diagnostic system, which combines dynamic interview dialogues and public data (OSINT) to acquire richer knowledge about AI readiness and generate actionable recommendations for AI implementation. To refine and validate the proposed approach, we conducted 15 semi-structured expert interviews with senior practitioners involved in AI transformation, including CEOs, sales and technical directors. The sample represents a balanced cross-section of large enterprises and SMBs. The interviews explored experts’ perceptions of current knowledge management practices in organizational assessments and expectations toward agentic AI systems capable of dynamic dialogue based diagnostics and recommendations. Results indicate expert agreement that static interview and survey approaches inadequately capture contextual organizational knowledge. Experts expressed interest in dynamic dialogue mechanisms that enable clarification and contextual probing to improve knowledge acquisition processes. At the same time, participants highlighted risks associated with agentic AI usage, including concerns regarding diagnostic quality, replicability, transparency and perceived value for participants. Experts further emphasized that integrating internal organizational knowledge with external public data can improve the results. The study contributes to Knowledge Management by reframing AI readiness assessment as a dynamic knowledge acquisition rather than a static task. Using a Design Science Research approach, the proposed agentic AI artefact offers a foundation for developing adaptive diagnostic systems that support strategic decision-making process, enhance organizational learning and improve the actionability of AI readiness assessments.
K. Jonak, Andrzej Wodecki· European Conference on Knowl...· 0 citations
The findings show that AI-related competency change extends beyond technical skills toward hybrid and portfolio-based configurations that integrate technical understanding, managerial judgment, learning agility, governance capabilities, and psychological readiness, and demonstrates that no single framework sufficiently explains these shifts.
The study contributes to information systems research by reframing AI readiness from a static resource inventory to an evolving organisational capability and offers managers a diagnostic logic for sequencing AI investments and avoiding premature scaling.
K. Jonak, Andrzej Wodecki· Discover Artificial Intellig...· 0 citations
A structured framework for the literature and theory is built, showing how AI capability fuels innovation via interconnected knowledge processes across the organization, putting human judgment, epistemic governance, and knowledge validation at the center of how organizations learn and innovate with AI.
Vaivaw Kumar Singh· International Journal of Lat...· 0 citations
This study investigates how organizational members concurrently perceive the benefits of artificial intelligence (AI) for knowledge management processes (KMPs) and the challenges involved in implementing AI within knowledge management systems (KMSs). Based on survey data from 378 respondents across diverse sectors and roles, the research employs validated instruments measuring perceptions of AI’s contribution to knowledge acquisition, documentation, sharing, and application, as well as perceived human, technological, financial, and ethical‑regulatory barriers. The results show a consistent positive relationship between perceived AI usefulness and perceived implementation barriers: individuals who attribute greater value to AI-enhanced knowledge processes also express heightened awareness of the complexities required to integrate AI into organizational systems. Knowledge documentation presents the strongest associations with all barrier categories, while knowledge sharing exhibits the weakest. Human‑related barriers emerge as the most pervasive across all processes, indicating the central role of employee readiness and organizational culture in shaping AI-enabled KM. These findings reveal a dual perception in which optimism regarding AI’s potential coexists with recognition of the organizational adjustments it demands. The study contributes to a more integrated understanding of AI adoption in KM, emphasizing that effective implementation requires aligning technological capabilities with human, cultural, and governance considerations.
M. Nakash, E. Bolisani· European Conference on Knowl...· 0 citations
As artificial intelligence (AI) tools become embedded in everyday work, employees increasingly engage in adaptive, experiential collaboration with AI systems. While such collaboration often generates valuable employee-level tacit knowledge, organizations struggle to translate this learning into strategic capabilities. This study examines how organizations can harness the knowledge emerging from employee–AI collaboration to build knowledge-based dynamic capabilities (KBDCs).
We conducted an inductive, qualitative study based on 29 semi-structured interviews with frontline employees, middle managers and senior leaders across multiple industries.
We develop a process model showing how tacit knowledge generated through micro-level human–AI collaboration is externalized as articulated experiential insights, validated and simplified into shared heuristics, and ultimately codified and embedded in organizational routines. This bottom-up transformation may help firms to renew internal knowledge resources and enhance KBDCs. The process is contingent on enabling conditions such as managerial support, organizational culture and employees' perceived agency in interacting with AI.
Organizations seeking to build dynamic capabilities from AI use should create structures that facilitate articulation, validation and dissemination of employee-level AI insights. Leaders play a critical role in translating individual experimentation into collective learning. Firms can assess their position in the transformation process and invest in systems that support phase-to-phase transitions.
While prior research has emphasized acquiring codified or external knowledge for dynamic capabilities, this study shifts attention to the internal, tacit and evolving knowledge that arises from employee–AI collaboration. We advance theory by unpacking a bottom-up pathway of KBDC formation through which such knowledge is articulated, validated and embedded over time. In doing so, the study also identifies a recursive organizational learning mechanism suited for high-velocity, AI-enabled environments.
Erica Wen Chen, Hongkun Tang, Ben Nanfeng Luo et al.· Management Decision· 0 citations
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