Aug 2026· Jurnal Impresi Indonesia· Vol 5, pp. 4100-4117· 0 citations· 20 references
TL;DR
The study contributed a validated lifecycle-integrated KD framework for AI initiatives; a taxonomy of ten systematically identified gaps in current AI KD practices; and a methodological demonstration of mixed-method CVI validation for framework development in information systems research.
Abstract
Despite the growing organizational reliance on artificial intelligence (AI) systems, knowledge documentation (KD) practices in AI initiatives remain largely ad hoc, unstandardized, and disconnected from project lifecycle management. This study addressed this gap by proposing and validating the Knowledge Documentation Framework for AI Initiatives (KDF-AI), a twelve-component, five-phase, maturity-tiered framework synthesized through a literature review of peer-reviewed studies on KD practices in organizations implementing AI. Using Design Science Research (DSR) as the methodological paradigm, the study completed two iterative cycles: a literature-based synthesis that produced KDF-AI v1 and an expert evaluation cycle that resulted in the refined KDF-AI v2. Expert content validation was conducted with three domain validators representing academic, governance, and AI practitioner perspectives using a mixed-method approach that combined quantitative Content Validity Index (CVI) assessment with deductive thematic analysis of semi-structured interviews. The results showed that 57 of 66 items (86.4%) achieved universal inter-rater agreement, producing S-CVI/UA = 0.864 and S-CVI/Ave = 0.955, both exceeding the recommended threshold of 0.80. The nine items that did not meet the threshold consistently reflected issues of clarity rather than relevance, indicating strong conceptual acceptance of the framework while highlighting the need for more operationally specific articulation in several Advanced-tier components. Nine targeted revisions resulted in the development of KDF-AI v2. The study contributed: (1) a validated lifecycle-integrated KD framework for AI initiatives; (2) a taxonomy of ten systematically identified gaps in current AI KD practices; and (3) a methodological demonstration of mixed-method CVI validation for framework development in information systems research.
The Knowledge Documentation Framework for AI Initiatives (KDF-AI), consisting of twelve components organized into five phases and supported by different maturity levels, is proposed, highlighting the increasing importance of knowledge documentation as a core capability in AI-driven organizations and provided a foundation for future research and practical implementation.
Finannisa Zhafira, Fitria Handayani, Dana Indra Sensuse et al.· Jurnal Impresi Indonesia· 0 citations
This study develops an evidence-based implementation framework and maturity model for organizations adopting agentic artificial intelligence (AI) in project management, synthesizing insights from academic research and industry implementations.
Following PRISMA 2020 guidelines, we systematically reviewed 97 sources spanning 2019–2025, including 52 peer-reviewed articles and 45 rigorously screened gray literature sources (industry reports, technology documentation and professional body publications) across construction, software development and other sectors.
We identify five levels of AI autonomy currently deployed in practice, three dominant implementation pathways and critical success factors including data infrastructure readiness, organizational change capacity and governance mechanisms. Organizations report 30–50% productivity improvements, though quantified cost-reduction data remain notably absent from the published literature, suggesting either competitive sensitivity or measurement challenges in this emerging field.
The evidence base is dominated by recent publications (65% from 2024–2025) and gray literature (41%), which limits the generalizability and durability of the findings. Quantified cost-reduction data specific to agentic AI are entirely absent from published sources. Future research should prioritize controlled empirical studies comparing agentic and traditional project management approaches, longitudinal tracking of human–AI team evolution, cross-cultural adoption studies and development of standardized reporting frameworks for agentic AI implementations.
The study provides: (1) a maturity assessment tool for organizational readiness, (2) an implementation roadmap with phase-gates and risk mitigation strategies, (3) a governance framework for human–AI collaboration and (4) an organizational performance measurement framework with validated Key Process Indicators (KPIs).
This is the first comprehensive analysis specifically focused on agentic AI in project management, providing actionable frameworks tested across multiple industries. The maturity model and implementation pathways offer immediate business value for organizations navigating AI transformation.
Ravi Kalluri· International Journal of Man...· 0 citations
Generative artificial intelligence (AI) is increasingly transforming knowledge management (KM) in knowledge-based organizations (KBOs). This study examines the integration of generative AI into KM within the United Arab Emirates (UAE), focusing on the technological, human, organizational, cultural, and regulatory factors influencing adoption.
The study aims to identify the key enablers and barriers affecting generative AI adoption in KM and to examine how UAE-specific factors, including national AI initiatives, public–private partnerships, workforce diversity, and SME characteristics, shape adoption pathways.
A mixed-methods approach combined thematic analysis of government strategies, organizational reports, industry publications, and peer-reviewed literature with ML-assisted conceptual coherence assessment. The procedure examined framework relationships without empirical validation, causal testing, or prediction. The framework was informed by Knowledge-Based View, Socio-Technical Systems, and Technology Acceptance Models.
Effective AI-enabled KM depends on technological capabilities, human expertise, organizational culture, leadership, and regulatory conditions. UAE-specific factors, including the national AI agenda, institutional environment, workforce characteristics, and organizational differences, influence adoption, indicating that technological readiness alone is insufficient without effective socio-technical alignment.
The study concludes that generative AI-enabled KM requires a balanced socio-technical approach integrating technology, human expertise, organizational practices, and governance. The proposed framework supports responsible AI adoption in UAE organizations and contributes to understanding AI-enabled KM in emerging economies.
Unknown authors· Human Systems Management· 0 citations
A research framework for analysing the added value of generative AI in logistics organizations, with a focus on “difficult to automate” tasks and processes is presented.
Gerald Schneikart, Walter Mayrhofer· Engineering review· 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
An exploratory literature review of the socio-technical issues involved in the adoption of GenAI tools in project documentation and governance and suggests a socio-technical conceptual framework that integrates these aspects into a unified view to inform people's understanding of responsible Human-AI collaboration in project settings.
Bela Lestari Dwireja, F. Abdalla, Yuhang Liu et al.· International journal of res...· 0 citations