2024· International Journal of Artificial Intelligence & Digital Transformation· Vol 7, pp. 01-15· 0 citations
TL;DR
The architecture of AI-KMS is examined, focusing on components like knowledge acquisition modules, inference engines, and user interfaces, along with the integration of deep learning and ontologies for improved knowledge representation, which shows improved accuracy in knowledge retrieval and decision-making efficiency.
Abstract
The rapid development of Artificial Intelligence (AI) has significantly transformed organizational operations, particularly in Knowledge Management Systems (KMS). In today’s dynamic and data-driven environment, competitive advantage depends on effectively capturing, storing, retrieving, and utilizing knowledge. AI-based KMS (AI-KMS) represent an evolution from traditional systems, shifting from rule-based repositories to intelligent, adaptive, and context-aware platforms that enhance organizational intelligence. Traditional KMS faced limitations in scalability and efficiency due to reliance on structured data and manual input. In contrast, AI-enabled systems leverage machine learning, natural language processing, and data analytics to process unstructured data such as documents, emails, and multimedia, enabling semantic search, personalized recommendations, and predictive insights. These systems also support continuous learning by automatically updating knowledge bases through user interactions. This paper examines the architecture of AI-KMS, focusing on components like knowledge acquisition modules, inference engines, and user interfaces, along with the integration of deep learning and ontologies for improved knowledge representation. It also addresses key challenges including data quality, privacy, scalability, and ethical concerns. A detailed literature review traces the evolution of KMS and AI integration prior to 2018. The proposed methodology uses a hybrid model combining supervised and unsupervised learning for knowledge extraction and classification. Experimental results show improved accuracy in knowledge retrieval and decision-making efficiency compared to traditional systems, supported by quantitative analysis. Overall, AI-KMS enhance organizational intelligence by accelerating decisions, fostering collaboration, and driving innovation. The paper concludes by recommending future research in explainable AI, governance frameworks, and integration with emerging technologies like IoT and blockchain.
Enterprise Knowledge Management (EKM) plays a vital role in leveraging organizational knowledge, improving decision-making, and maintaining competitive advantage. Traditional knowledge management systems struggle with scalability, real-time adaptability, and contextual understanding. AI-driven digital assistants address these limitations by using technologies like Natural Language Processing (NLP), deep learning, and knowledge graphs to enable intelligent automation and semantic understanding. These assistants support conversational interactions, making knowledge access and contribution more intuitive. The study proposes a modular architecture with components such as data ingestion, semantic processing, knowledge repositories, and user interfaces, enhanced by reinforcement learning for continuous improvement. Hybrid models combining rule-based and learning-based approaches improve reliability and interpretability. Findings show that AI-based assistants enhance knowledge retrieval accuracy, reduce response time, and increase user satisfaction. They are widely applicable in areas like customer support, internal knowledge discovery, and decision support. Overall, AI-powered digital assistants improve efficiency and promote knowledge democratization, with future research focusing on explainability, ethical AI use, and integration of multimodal data.
Rebecca Green· International Journal of Art...· 0 citations
Generative AI is changing the role of knowledge in organisations. Traditional knowledge management (KM) systems have primarily supported storage, access and retrieval, assuming that knowledge is interpreted and applied by human users. In AI-enabled environments, however, organisational knowledge increasingly becomes a direct input into execution, shaping generated proposals, analyses, summaries, recommendations and other workflow outputs. This shift exposes a limitation of retrieval-oriented KM: fragmented, outdated or weakly governed knowledge can be amplified through AI-generated outputs, reducing consistency, reliability and trust. This paper introduces executable knowledge systems as a conceptual model for structuring organisational knowledge to support reliable human and AI-assisted execution. The term executable is used in a socio-technical sense. Knowledge does not necessarily become code, but is curated, validated and embedded into workflows so that it can guide outputs, decisions and actions. The paper distinguishes this concept from prior work on executable knowledge graphs and executable knowledge bases, which primarily focus on deterministic execution through rules, scripts or formalised representations. The paper further develops a framework of decay and compounding loops to explain how AI-enabled knowledge systems evolve over time. In decay loops, AI-generated outputs re-enter the knowledge environment without sufficient validation, allowing inconsistency and low-quality knowledge to accumulate. In compounding loops, curated knowledge assets are refined through governed feedback, domain ownership and controlled reuse, enabling improvements in reliability over time. The framework is informed by an exploratory case study within a global professional services organisation, where a curated knowledge environment was introduced to support AI-assisted workflows in the Retail, Consumer Products, Travel and Transportation domain. The evaluation compared outputs generated from a controlled, subject matter expert (SME)-validated knowledge dataset with outputs generated from an unconstrained organisational knowledge base. Findings indicate improved retrieval relevance and output quality when AI systems operate on validated knowledge assets. The paper contributes to KM research by reframing KM as a system design challenge for AI-enabled execution and by positioning governance, validation and feedback control as central mechanisms for reliable organisational knowledge use.
Sara Michelazzo, Parmeet Kaur, Saurabh Saxena· European Conference on Knowl...· 0 citations
The paper concludes that the future of KMS lies not in more sophisticated repositories, but in intelligent systems capable of dynamic codification, contextual reasoning, and continuous organisational learning, redefining the balance between human and machine agency in organisational knowledge processes.
A. Antonova, Dilyan Georgiev, Anikó Csepregi· European Conference on Knowl...· 0 citations
Business Intelligence systems powered by Artificial Intelligence (AI) have demonstrated to be an innovative approach towards transforming organizational intelligence into valuable information for strategic decisions, maximum efficiency of operations, and constant innovations within organizations. Conventional Business Intelligence systems heavily utilize descriptive analytics based on historical reporting methods and dashboard-based visualizations, and as such, they feature limited predictive and prescriptive analytics capabilities. In this scenario, the current paper aims at providing the full conceptual framework of AI-based Business Intelligence, encompassing the various elements of the system, namely, data management, AI data analytics, automated insight generation, intelligent decision making, and innovation within the organization.This framework is based on machine learning, deep learning, natural language processing, predictive analytics, and anomaly detection technologies that help process structured and unstructured data from various sources including enterprise systems, customer databases, IoT devices, market information systems, and social media platforms. AI-based analytics can help in identifying trends that manifest themselves in business processes, performance problems, risks, and opportunities as well as in providing recommendations for decision making in real time.The framework promotes and boosts innovations through enhanced strategic planning, a focus on customers’ needs in product development, optimizations of operations including processes, and adaptability of business models. The findings indicate that business intelligence based on AI technology can help improve the quality of decisions in addition to fostering higher operational efficiency, agility of organizations, and securing compliance with the demands of modern competition. Nevertheless, one should keep in mind that challenges related to data quality, systems integration, data security, explainability, governance, and adequate readiness of organizations might hinder the implementation of this framework.The results highlight the need for AI to perform the role of an intelligent decision-making support tool which enhances human expertise without replacing it. Further studies should investigate the use of AI across various industries, use of Explainable Artificial Intelligence (XAI), Generative AI, AI governance frameworks and the validation of ideas proposed through machine learning experiments and organizational case studies. The suggested project gives a framework for companies in their attempts to achieve intelligent, flexible and innovation-oriented digital transformation using the AI-driven systems of Business Intelligence.
Virendra Gomase, Suhas B. Dhande, P. Natu et al.· Journal of Intelligent Decis...· 0 citations
This study presents an automated knowledge extraction and prediction system using the advancements in Artificial Intelligence (AI) tools, referred to as APEX-LLM, which is a scalable, domain-independent system which can be customized and applied to health, financial and business sectors, and education.
This study proposes an LLM-powered knowledge management framework that combines Retrieval-Augmented Generation (RAG), semantic embeddings, enterprise-specific language models, and vector databases to transform enterprise data into actionable knowledge.
Farhan Malik, Zara Ahmed· International Journal of App...· 0 citations
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