Aug 2026· European Conference on Knowledge Management· 0 citations· 52 references
TL;DR
The study identifies 372 AI readiness factors and synthesises them into the TOP-L framework consisting of four dimensions and 20 factor clusters, thereby laying the foundation for a practical assessment approach, particularly suited for SMEs.
Abstract
The growing importance of artificial intelligence (AI), particularly Generative AI (GenAI), is opening up significant potential for corporate knowledge management (KM) and knowledge-intensive work. To remain competitive, organisations must effectively implement these technologies. AI readiness, understood as preparedness and capacity to successfully implement and use AI in a value-creating way (Ali & Khan, 2025; Alsheibani et al., 2018), has therefore become a critical concept. However, the underlying factors remain contested, and existing research is fragmented, with a strong focus on technical and environmental aspects, while human factors and organisational learning (OL) are underrepresented. In addition, practical assessment tools are still limited, particularly for small and medium-sized enterprises (SMEs). To address this gap, this paper presents a systematic literature review of AI readiness. A total of 34 frameworks and assessment instruments were analysed to identify key factors and evaluate existing approaches. Building on the socio-technical TOP framework (Kretschmer & Orth, 2025), the Technology-Organisation-People-Learning (TOP-L) framework is developed, integrating OL as a dynamic capability for continuous adaptation. The study identifies 372 AI readiness factors and synthesises them into the TOP-L framework consisting of four dimensions and 20 factor clusters, thereby laying the foundation for a practical assessment approach, particularly suited for SMEs.
It is asserted that policymakers and SME managers need to prioritise training, infrastructure and digital readiness to ease the path for AI adoption and the literature affirms that AI adoption increases the marketing performance of SMEs.
Salma El-Gohary, M. B. Ben Mimoun, Hatem El-Gohary· Journal of Cultural Analysis...· 0 citations
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
Technology adoption plays a central role in the success of organisations, where organisational maturity can only be achieved based on learning organisation principles and agile teams for cross-functional collaboration. These constructs are seldom examined in tandem, and are more often approached as separate aspects in the complex adaptive systems in today’s organisations. Recent AI developments necessitate a re-evaluation of how learning organisations are optimised in digitally transformed environments. Knowledge management (KM) has evolved into a strategic enabler of organisational learning and adaptive capacity. This paper reports on findings of a scoping review that explored trends in organisational maturity, conceptualised as the extent to which organisations institutionalise continuous learning, reflective practice, and knowledge integration across systems, culture, and leadership. Databases Google Scholar, Scopus, Web of Science and ProQuest Central were searched and sources published from 2009 to 2025 were selected for thematic analysis. Using the PRISMA-ScR reporting guidelines and Covidence for data extraction, 37 articles were selected and analysed to identify trends and gaps in research. Findings indicate that sustainable competitive advantage emerges when organisational maturity, AI integration, and continuous learning are aligned to support anticipatory, evidence-based decision-making in complex environments.
Brenda van Wyk· European Conference on Knowl...· 0 citations
Approximately 70% of organisational development (OD) initiatives based on methodologies such as Total Quality Management (TQM), Business Process Reengineering (BPR), Lean, and Six Sigma are reported to fail. Critical Systems Thinking (CST) attributes many of these failures to poor implementation and inadequate attention to organisational context, emphasising the need for critical awareness, political agency, and multimethodology. However, when OD work itself becomes a “bullshit job”, even CST’s principles may struggle to take hold. This paper develops Scientific Self-Management (SCSM), inspired by Community Operational Research (COR) and informed by Complex Adaptive Systems (CAS), as a bottom-up systems approach to such situations. A 42-month first-person action research study of a public-sector OD programme examines the practical feasibility of SCSM in a complex organisational setting. The study shows how bottom-up learning and self-directed engagement can restore meaning and generate local organisational improvements where conventional approaches struggle to gain traction, while also highlighting the political challenges of sustaining such initiatives over time. It contributes to the CST and COR literatures by extending systems practice to situations in which management is incompetent or hostile and organisational culture has become paralysed by pretence. The findings suggest that successful organisational development depends less on the choice of methodology than on creating organisational conditions that enable meaningful participation, learning, and continuous improvement.
Petter Øgland, Gary Alan Evans· Systems· 0 citations
Generative artificial intelligence (Gen AI) and large language models (LLMs) offer substantial potential to improve how organisations capture, organise, retrieve and reuse knowledge. Existing knowledge management (KM) frameworks, however, seldom integrate Gen AI/LLM-specific processes, data governance, and ethical requirements in highly regulated public-sector settings, and few have been empirically evaluated. This study assesses the feasibility of adopting GenAI and LLMs for organisational KM and develops a corresponding Knowledge Management and Artificial Intelligence (KMAI) framework for a national communications regulator. Guided by Diffusion of Innovation (DOI) theory, the study employed a multi-method qualitative design comprising a literature review, semi-structured interviews with six KM representatives, a focus group discussion involving fourteen participants using the LEIQ™ model for SWOT analysis, and a content-validity evaluation by three experts. Interview data were analysed thematically, SWOT findings were transformed into strategies through TOWS analysis, and framework relevance was assessed using item- and scale-level content validity indices. The findings indicate that adoption is feasible in this setting: participants perceived clear relative advantage, compatibility, trialability and observability, while complexity was manageable when supported by adequate skills, data classification, secure infrastructure and governance. The principal risks concerned data quality, privacy, security, misinformation, over-reliance on AI, and organisational resistance. The proposed KMAI framework achieved acceptable content validity across all clusters, with S-CVI/Ave values ranging from 0.89 to 1.00, and was refined into four strategic thrusts: People, Process, Technology and Data. Because the evidence derives from one organisation and a three-member expert panel, the framework is presented as an empirically grounded and internally validated proposition whose extension to other agencies is analytic rather than statistical; the boundary conditions governing such extension are stated explicitly, and confirmatory validation with a larger expert panel and independent organisational sites is identified as the necessary next step. The study extends DOI-based adoption analysis by showing that data governance and ethics condition Gen AI/LLM adoption in organisational KM and provides a practical, staged framework for regulated public-sector organisations.
Surya Sumarni Hussein, Nur Azaliah Abu Bakar, S. Hamidi et al.· International Journal of Adv...· 0 citations
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.
Arno Onnen· European Conference on Knowl...· 0 citations
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