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.
Abstract
Artificial intelligence (AI) has become an essential component of organizational strategy; however, the knowledge generated during AI projects, including design decisions, model behavior, development processes, and practitioner insights, is often insufficiently documented. This study aimed to examine how knowledge documentation is currently practiced in organizations implementing AI. Using a systematic literature review approach, this study analyzed twenty peer-reviewed studies published between 2020 and 2026. The articles were collected from five major academic databases, namely Scopus, ScienceDirect, ACM Digital Library, IEEE Xplore, and Emerald Insight, and were selected following the PRISMA 2020 guidelines. Based on five research questions structured using the PICOC framework, several key findings emerged. First, knowledge management in AI contexts has shifted from static repositories toward more dynamic and AI-supported systems, although this transition introduces challenges such as model drift, inconsistent documentation practices, and difficulties in capturing tacit knowledge. Second, the literature presents various knowledge documentation methods and frameworks, indicating that the field remains in development without a universally accepted standard. Third, structured documentation has been shown to positively influence organizational learning, knowledge reuse, and the overall effectiveness of AI initiatives. Fourth, despite these benefits, significant gaps remain, particularly regarding the absence of standardized AI/machine learning (ML) documentation practices and the limited integration of documentation throughout the AI lifecycle. In response to these challenges, this study proposed the Knowledge Documentation Framework for AI Initiatives (KDF-AI), consisting of twelve components organized into five phases and supported by different maturity levels. Overall, this review highlighted the increasing importance of knowledge documentation as a core capability in AI-driven organizations and provided a foundation for future research and practical implementation.
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.
Fitria Handayani, Finannisa Zhafira, D. Sensuse et al.· Jurnal Impresi Indonesia· 0 citations
Aim/Purpose
To systematically review the literature on artificial intelligence (AI) and knowledge sharing in order to identify AI that support organizational knowledge sharing, examine organizational factors that influence their effectiveness, and assess the resulting organizational performance outcomes.
Background
Organizations increasingly rely on AI to enhance knowledge sharing processes. While prior research has examined AI, knowledge sharing, and organizational performance as separate constructs, there is limited synthesis regarding how AI techniques, organizational conditions, and knowledge sharing practices collectively affect organizational performance.
Methodology
The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA 2020) guidelines were followed in this study. Articles published between 2015 and 2025 were retrieved from the Scopus and IEEE Xplore databases using predefined search criteria. After screening titles, abstracts, and full texts, 32 peer-reviewed studies that met the inclusion criteria were analyzed.
Contribution
This study integrates four dimensions of AI-enabled knowledge sharing into a single conceptual framework by synthesizing AI techniques, organizational moderating factors, AI–knowledge-sharing relationship types, and organizational performance outcomes. The study provides a comprehensive overview of current research while identifying important gaps for future investigation.
Findings
The findings indicate that Generative AI, machine learning, fuzzy logic, natural language processing, recommender systems, support vector machines, semantic AI, and random forests as the principal artificial intelligence techniques. Trust, leadership support, employee engagement, and role clarity are the most frequently reported organizational moderating factors. AI-enabled knowledge sharing is primarily associated with transformational organizational change and is linked to enhanced decision-making, innovation, and productivity.
Recommendations for Practitioners
This study offers specific and practical insights into the impact of AI-based knowledge sharing systems on business performance. Practitioners and organizational leaders are encouraged to evaluate the integration of AI into knowledge-sharing processes to enhance business outcomes.
Recommendations for Researchers
Subsequent research should validate and expand upon the relationships identified in this review by employing longitudinal and empirical research designs across a range of industries and organizational contexts.
Impact on Society
As organizations increasingly integrate AI into knowledge management, understanding how AI enhances knowledge sharing can improve organizational learning, innovation, and decision-making.
Future Research
Future research should investigate industry-specific AI-enabled knowledge-sharing practices, emerging generative AI applications, governance mechanisms, and longitudinal organizational outcomes associated with AI adoption.
The study develops a lightweight, information systems-supported knowledge management strategy organised around three conceptual building blocks: Knowledge Hubs, Knowledge Capsules, and workflow-based safeguarding mechanisms, all supported by an enabling digital, governance and motivation layer.
Moritz Rau, E. Perjons· European Conference on Knowl...· 0 citations
Software development is a knowledge-intensive activity, and managing knowledge effectively is essential to ensuring productivity and quality. Software reuse (SR) is a strategic approach that improves efficiency and return on investment, but sustaining reuse programs remains challenging. Organisations often struggle to manage the knowledge embedded in software assets such as code, designs, and test cases. Knowledge management (KM) processes—identification, acquisition, sharing, storage, codification, creation, application, and evaluation—offer a promising lens for addressing these challenges. Yet, recent work has paid limited attention to their explicit role in SR. This exploratory work analyses the recent literature on SR from a KM perspective, seeking to understand how the main knowledge processes support SR. To this end, a rapid literature review was conducted to identify studies that explicitly discuss KM aspects of SR. The review applied inclusion criteria focused on studies that explicitly discuss KM for SR, and exclusion criteria that removed unavailable studies, review-only studies, and studies that did not answer the research questions. Each selected study was then mapped, categorised, and analysed using a protocol aligned with the research questions, considering the KM processes addressed, the types of assets reused at each reuse level (specification reuse, design reuse, code reuse, application system reuse, and test reuse), and the research method employed. By structuring the evidence around these dimensions, the review helps characterise how knowledge processes are discussed in relation to different forms of reuse, different reusable artefacts, and both operational and strategic reuse concerns. Rather than consolidating a mature theory, the results indicate tentative paths for connecting KM and SR research. In particular, the analysis indicates a predominance of storage and codification, followed by application and sharing. At the same time, the findings highlight the need to deepen research on knowledge evaluation, test reuse, and the organisational conditions that help sustain software reuse initiatives over time.
Emilly Lopes, C. E. Barbosa, M. Argôlo et al.· European Conference on Knowl...· 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
The increasing availability of Artificial Intelligence (AI) tools has generated significant interest within the project management community; however, structured guidance tailored specifically to Project Managers remains limited. This paper proposes a standardized AI-enabled prompt architecture aligned with the PMBOK® 8E performance domains and processes. The framework consists of a Master Prompt, Task-Level Executable Prompts, and Refine Prompts designed to create bounded, context-aware interactions between Project Managers and AI systems. The proposed architecture embeds PMBOK-aligned terminology and follows the Inputs–Tools–Outputs (ITTO) logic. The AI system functions as an analytical and generative tool within this structure, processing structured and unstructured inputs—including expert judgment—and producing standardized outputs for managerial review and refinement. The architecture is designed to be generalized and extensible across all forty project management processes. This study does not present empirical performance metrics; rather, it introduces a structured conceptual framework intended for practical application and future validation. Practitioners are encouraged to implement the architecture in real project environments to evaluate measurable improvements and contribute to further academic development in AI-enabled project management.
Vittal Anantamula, Rajendra Harsh· 2026 6th International Confe...· 0 citations