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.
Abstract
As Artificial Intelligence (AI) transitions from experimental pilots to enterprise-wide deployment, the central managerial challenge has shifted from simple adoption to the complexities of effective scaling and integration. While existing AI readiness models provide a foundational understanding, they often treat readiness dimensions as static and were created during earlier waves of AI development. This study examines how contemporary deployment conditions, characterised by generative AI, AI-as-a-service and rapidly commoditised infrastructure, reshape what organisational readiness for AI actually entails. Drawing on socio-technical systems theory and dynamic capabilities theory, the study employs an abductive, template-analytic qualitative design combining a literature-derived initial template with fifteen semi-structured interviews with senior practitioners across corporate and Small and Medium companies (SMB) contexts. The analysis indicates that IT infrastructure and data maturity are perceived to have shifted from being differentiators of readiness to being baseline prerequisites, while governance and accountability, process discipline and behavioural alignment have emerged as the primary determinants of effective AI deployment. On this basis the study introduces the construct of Organisational AI Orchestration Capability, defined as the dynamic capacity to integrate, govern and operationalise heterogeneous AI resources, behaviours and processes into reliable enterprise outcomes. 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.
A framework in which the microfoundations of dynamic capabilities operate through organizational readiness to shape AI-driven industrial management capability and, in turn, operational and managerial performance outcomes is developed.
Hoogendijk Ha· Journal of Economic, Finance...· 0 citations
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.
Maria Kretschmer, Alice Coen, R. Orth· European Conference on Knowl...· 0 citations
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.
Bo Fan, Haibo Wang, Ruzhi Xu· Journal of Artificial Intell...· 0 citations
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.
Arno Onnen· European Conference on Knowl...· 0 citations
Despite the strategic priority of digital transformation and Artificial Intelligence (AI), many organizational initiatives fail to achieve sustainable outcomes due to insufficient institutional readiness and fragmented governance. To address this gap, this paper introduces the Abuhaimed Digital & AI Excellence Model (ADAIEM), a comprehensive conceptual framework designed to foster institutional readiness and guide enduring transformation. The framework integrates three interdependent pillars: Institutional Foundation: Governance, strategy, organizational structure, processes, knowledge management, and talent development, Digital Enablement: Core digital systems, data infrastructure, automation, analytics, and platforms, and AI Enablement: AI governance, intelligent agents, decision-support mechanisms, and enterprise-wide adoption. Central to the framework is the ADAIEM Conditional Transformation Logic (ACTL), which utilizes capability gates to enforce progression only when prerequisite maturity levels are met. Unlike traditional static maturity models, ACTL actively facilitates continuous capability development, mitigates execution risks, and reinforces operational sustainability. Grounded in Business Engineering and organizational capability theory, ADAIEM advances the literature on digital transformation and AI governance by offering a structured, risk-mitigated pathway toward high-maturity, AI-enabled enterprise operations. Building on the concepts of Business Engineering and based on a variety of organizational and transformation theories, ADAIEM brings together governance, organizational design, knowledge management, talent development, digital capabilities and AI enablement under a single transformation architecture. The proposed framework offers a real-world action plan for sustainable AI transformation and a theoretical understanding of the phenomenon of AI transformation.
M. Abuhaimed· Journal of Intelligent Decis...· 0 citations
This article proposes a competency-based framework for diagnosing organizational change trajectories driven by artificial intelligence (AI) integration. It extends a four-stage typology of AI-enabled organizational models by showing that each stage can be identified through the configuration of employee competencies, independently of technological indicators.
Drawing on structural-configurational, dynamic capabilities and sociomaterial perspectives, the article develops a five-stage competency assessment procedure and a classification matrix across twelve competency areas, and uses vertical coherence gaps and directional asymmetry of renewal to explain transitions between models.
Each organizational model type is associated with a distinct employee competency profile that serves as both a theoretical boundary marker and a diagnostic indicator. Transitions between models are not uniformly linear: their pace and stability depend on governance maturity, regulatory context and the clarity of an organization's strategic purpose regarding AI.
The framework is conceptual and requires empirical validation across sectors.
The framework gives change managers and human resource (HR) practitioners a structured basis for assessing an organization's AI integration stage and mapping competency gaps against a target model.
By foregrounding human competencies, the framework supports a human-centered path of AI adoption that preserves meaningful human agency in increasingly automated work.
This is the first study to link employee competency profiles systematically to discrete AI-enabled organizational model types, connecting stage models of AI adoption with competency management research.
Marek Jabłoński· Journal of Organizational Ch...· 0 citations
A new method, called CW-Net, translates the reasoning process of an autonomous vehicle’s AI system into understandable concepts that explain its behavior.
