The appearance of AI and its subsections, ML and DL, is transmuting how organizations achieve and influence knowledge. These technologies are not only redesigning the way data is analyzed but also transforming how knowledge is obtainable, discovered, and communal within companies. The combination of AI and ML into KM systems signs the dawn of a new-fangled era where the mechanization of information dispensation is key to optimizing decision-making and predicting future organizational outcomes. 

By automating the analysis of massive datasets, increasing the accessibility of knowledge, and strengthening the distribution and discovery processes generally, ML is essential in improving organizational KM. A company's ability to make decisions and prepare for future trends and problems can be enhanced with the help of ML algorithms, which can reveal previously unseen patterns in data. Knowledge management systems might be made smarter, faster, and more responsive with this ability. The growing importance of BD has further strengthened ML's function in KM. When it comes to managing an organization's knowledge, Big Data provides the raw materials for ML algorithms to construct logical models. Organizations may make informed decisions based on real-time info and forecast insights thanks to BD's data processing capabilities, which power the automation of KMS. Businesses who want to keep up with the ever-changing market must integrate ML with Big Data. This study delves into the connection between Big Data, Machine Learning, and Knowledge Management by conducting a comprehensive literature review and thematic analysis of recent research. The purpose of this research is to examine ML in KM and identify its advantages, disadvantages, and possible future uses. But there's a huge hole in our understanding because the combination of ML and KM in management and commercial settings has only been the subject of 10% of published studies.  This gap highlights the need for more comprehensive studies on how ML can be effectively integrated into KM to enhance business operations and outcomes.

Companies face the challenge of staff turnover, often due to retirement or personal reasons, which results in employees taking valuable knowledge and experience with them. To retain this critical knowledge, firms must implement effective knowledge management (KM) procedures. KM enhances decision-making and learning by converting tacit knowledge into explicit knowledge, making it easier to retain and share. The emergence of artificial intelligence (AI), particularly machine learning (ML), allows organizations to further optimize KM through automation and improved decision-making (Retno, Wulansari, Sri, & Prabandari, 2024).Many companies, however, have yet to establish proper knowledge management policies. Since most employees do not regularly deal with data flow, they may only recognize a fraction of the problems affecting their company. This lack of understanding can reduce the efficiency and effectiveness of knowledge management processes. By recognizing the strategic value of KM, companies can address seemingly minor issues that can have significant operational impacts. Knowledge management enables businesses to keep pace with market demands and invest in innovative products, services, and solutions by retaining and expanding employees' skills (Bures & Otcenaskova, 2021).To improve decision-making, businesses increasingly turn to knowledge management systems (KMS) that store, analyze, retrieve, and share data. Effective KM enhances information sharing and collaboration, helping organizations optimize processes, reduce errors, and increase productivity (Gupta, Iyer, & Aronson, 2000). Ineffective information-sharing methods come with significant costs, further highlighting the importance of KM in boosting organizational efficiency (Lyu, 2016).Despite its advantages, many businesses struggle to implement KM because employees are unfamiliar with the concept. Even organizations that acknowledge its importance find successful implementation challenging. However, rapid advancements in AI and machine learning offer businesses new opportunities to improve their KM systems. With ML integration, companies can automate information retrieval and sharing, making it easier to address critical knowledge-related challenges and improve overall performance (Nyame, Qin, Obour Agyekum, & Sifah, 2020).In this fast evolving digital landscape, organizations that efficiently achieve and influence their most valued asset—information—can gain a modest advantage through the integration of ML  and AI enabled management.

Literature on Knowledge Management

[1] The study emphasizes the capacity of ML to enhance efficiency in process redesign and resource allocation, two important ML applications across the BPM lifecycle phases. With a systematic approach to coding and literature analysis, the researcher fully understood the importance of ML in BPM. The capacity of ML to improve resource allocation, build process models, and assist decision-making are among its most important findings. [2] the study utilizes bibliometric analysis to uncover significant trends and contributors. It ranks the Journal of Knowledge Management as the top publication and names Bontis N. of Canada as the top author. Big data, artificial intelligence, and knowledge concealment are three important new challenges. A substantial increase in KM literature has been observed in the review. 

[3] Knowledge Management (KM) is essential for firms to respond to fast-paced changes and maintain competitiveness that works helps people make better decisions, solve problems, and perform better, which in turn increases efficiency. By making greater use of intellectual resources, it also propels innovation and economic progress. A company's ability to acquire and keep a competitive edge depends on how well it handles its explicit and tacit information. [4] in this study it provides an overview of the main points, assesses the methods used, and points out where more research is needed. Researchers can save time, get a better grasp of the subject at hand, and contribute to the area by reviewing relevant literature. Researchers need to seek for, assess, and synthesise pertinent literature in order to construct a coherent narrative.[5] knowledge is an organization's most important asset because of its versatility and value. Data organization, storage, and retrieval are all aspects of knowledge resource management that the study acknowledges as being in high demand because of the exponential growth of these assets. Knowledge management, according to Chatterjee, is both a method and an approach to managing an organization's people resources and its body of knowledge. This study sheds light on how knowledge management boosts growth and productivity by investigating knowledge management technologies and tools, drawing connections between their real-world uses and organizational performance.

[6] employs a mixed-methods strategy to investigate how KM may be incorporated into STEM, driven by the common ground between the two fields in terms of emphasis on collaboration, problem-solving, and information sharing. This research analyses different KM process models and suggests frameworks to integrate KM into STEM curriculum.

[7] in this study, to boost corporate competitiveness and accomplish sustainable development goals (SDGs), records management (RM) and knowledge management (KM) are crucial. The research shows that RM and KM are interdependent, and that a competitive advantage and risk reduction may be achieved by good management of both. Standardized RM and KM approaches, technology leverage, and change management focus are the ways to overcome these challenges.

[8] the research shows that in highly mobile workplaces, knowledge sharing boosts employee synergy and helps individuals adapt to new responsibilities. Researcher uses a qualitative case study approach focused on the Baubau City Regional Secretariat to investigate the application of knowledge management strategies. The study emphasizes the importance of efficient resource management in sustaining organizational progress, improving service delivery, and achieving strategic objectives, particularly in the context of bureaucratic reform.

Literature on Machine Learning

[9] emphasizes machine learning's rapid expansion, focusing on the evolution of algorithms that allow computers to learn from experience, identify patterns, and make predictions. The study highlights the critical role of datasets, noting that the performance of machine learning models heavily relies on dataset diversity and quality. To support fast and scalable cross-architecture development, Akshay introduces oneAPI, a unified programming model compatible with various architectures. The article outlines a systematic approach to learning machine learning, aligning with the literature's focus on guided learning. 

[10] examines automating accounting procedures with machine learning improves decision-making efficiency and reduces errors. In order to apply machine learning responsibly, the study tackles important ethical concerns, with a focus on data protection and algorithmic openness. To make sure machine learning is used effectively, ethical management is crucial, who notes that it is already changing accounting norms.

[11] the study investigates into fundamental concepts including supervised and unsupervised learning, feature engineering, model evaluation, and complex algorithms like neural networks and decision trees. Using examples from fields as diverse as finance and healthcare, Pinki delves into current concepts such as deep learning (DL) and reinforcement learning (RL). To keep up with the fast-paced developments in machine learning, the study predicts its future and discusses ethical concerns such as privacy and prejudice.

[12] examined Linear algebra, probability, and statistics are the mathematical building blocks of machine learning algorithms and it is essential for tasks like regression and classification in image and sequence processing, the research investigates Neural Networks (RNNs) and supervised and unsupervised learning models. Beyond that, the research investigate into Support Vector Machines, Bayesian Learning, and their real-world uses in domains such as natural language processing and computer vision. Alignment and mesa optimization are two of the most critical issues that he fixes to ensure that machine learning systems perform as intended.

Literature on Big data and Machine Learning

[13] explains that traditional DBMS face challenges in efficiently handling large, diverse datasets, leading to the enhancement of DBMS capabilities through Machine Learning (ML) integration. Rosa emphasizes the importance of combining IoT and Big Data Analytics (BDA) with DBMS to improve data management and decision-making. The paper also explores current theories and methods for integrating ML into DBMS, highlighting the state of the field and proposing future advancements like ML-IDMS to address ongoing challenges.

[14] lays a solid theoretical foundation by addressing key concepts and approaches in knowledge management frameworks that incorporate big data analytics within higher education. He identifies a significant gap in the current literature, noting the lack of research exploring the interaction between big data and knowledge management. Although various methodological approaches have been used, there is a noticeable absence of multi-case studies spanning across institutions. Shakhzod emphasizes the importance of knowledge management in enhancing education quality and administrative efficiency, highlighting how big data can optimize these systems. The paper concludes by proposing a multi-case study research strategy to explore the use of big data in academic knowledge management.

[15] studied the internet and new technology are changing the dynamics of society and business, and argues that this is posing a threat to established ways of doing business. The research highlights the significance of AI and big data in discovering previously unseen patterns and developing predictive models to boost productivity and creativity. Through the lens of systems thinking, it investigates the nature of human-machine interaction with an emphasis on the revolutionary power of collective intelligence in addressing problems. Chinese researchers used AI and big data to successfully handle the COVID-19 pandemic. To help with decision-making in unpredictable situations, research suggests new frameworks for knowledge management that take into account the pros and cons of these fast shifts and incorporate smart technologies.

[16] highlights the importance of finding methodical ways to evaluate and choose ML models according to criteria that the user specifies. Data management, model development, lifecycle management, and two other primary functionalities are classified into five groups in the article. It suggests Gypscie as a solution to a problem it finds in existing systems—model selection for complicated data domains in particular. Mlflow, ModelDB, and Mistique are some of the existing tools that work well with Gypscie's method. The article also briefly discusses how AutoML might improve model accuracy and decrease the need for human intervention in hyperparameter tuning. 

[17] investigates into the intricate relationship between retail and banking consumer behavior, big data, and knowledge management. Despite studies generally looking at big data and knowledge management independently, it was discovered that they have a substantial impact on customer interactions. More research on their combined impacts is needed, and the paper offers useful insights for future studies by highlighting this need.

Research Gap

Knowledge management (KM) systems offer significant potential to enhance organisational efficiency, decision-making, and knowledge retention, but expertise on fully integrating machine learning (ML) and artificial intelligence (AI) remains limited. Despite the growing popularity of ML and AI across industries, few studies explore their connection to KM in managerial and corporate settings. This view highlights the necessity for further studies into the possibility of ML to enhance KM procedures. Despite the importance of AI and ML for knowledge management, there is a dearth of research that addresses the concerns that companies have regarding data security, implementation costs, and workforce preparation.  There is also a need for more focused studies on ethical considerations and organisational readiness for technology integration. Future research is crucial to bridge these gaps and provide businesses with actionable frameworks for applying AI and ML in KM effectively.

Research Question

How can the incorporation of ML and artificial intelligence AI into KM systems improve knowledge retention, decision-making, and organisational efficiency, particularly in addressing the challenges of knowledge loss caused by employee turnover and improving data-driven insights for strategic advantage?

Research Objective 

To analyse how KM systems can successfully incorporate ML and artificial intelligence AI to improve organisational competence, executive, and knowledge retention. It also seeks to discover ways to overcome problems related to data security, application costs, and workforce willingness in the context of integrating AI and ML into KM systems.

This study employs a SLR to explore KM, ML, AI and ML BD. The method involves classification, assessing, and synthesizing present academic literature to found a hard theoretic basis. The initial search focused on English-language articles published between 1990 and 2024. Databases like Google Scholar, SpringerLink, IEEE Xplore, Scopus, PubMed, MDPI, DOAJ, and Web of Science were used, with keywords such as "Knowledge Management," "Machine Learning," "Artificial Intelligence," and "Big Data," identifying around 1,120 relevant publications. After applying the English-language filter and removing duplicate entries, 340 articles remained. Further criteria narrowed the search to peer-reviewed journals and conference papers specifically addressing KM, ML, and AI, leaving 130 articles. The abstracts of these articles were reviewed to ensure comprehensive coverage of the research landscape. The final selection included 100 articles, which underwent detailed analysis and comparison. This research provides visions into the chances and challenges at the connection of KM, ML, AI, and Big Data, recognizing gaps and areas for future research.

Table 1 AI, Knowledge Management, and Machine Learning since the early 2000s and will be doing so until 2024

Table 1 below provides Researchers have been delving into many facets of AI, information management, and machine learning since the early 2000s and will be doing so until 2024. The research presented here demonstrate the revolutionary nature of AI and ML in fields as diverse as medicine, finance, academia, and transportation. There is an urgent need to incorporate big data into decision-making procedures due to its exponential expansion, which is being propelled by the extensive usage of mobile devices connected to the internet. Support vector machines, Bayesian networks, and other machine learning methods allow for more precise automation and forecasting. More and more industries are relying on data-driven, efficient decision-making processes brought about by advancements in machine learning.

Figure 1 Bibliographic analysis on ML and KM

Figure 1's bibliographic analysis summarises research on the interaction between machine learning and knowledge management. The findings reveal five separate clusters of topics, denoted by a distinctive hue. Among the topics in the red cluster are decision-making, big data, behavioural research, machine learning, and natural language processing. Knowledge management systems can greatly benefit from machine learning in several areas, especially for data analysis and decision-making. Algorithms, supervised learning, support vector machines, and feature extraction are all part of the green cluster. Here we will go over the specifics of using machine learning for knowledge management, with an emphasis on categorization and predictive analytics. Advanced ML methods like convolutional neural networks, forecasting, deep learning, and neural networks are the main emphasis of the blue cluster. To improve predictive analytics and knowledge discovery, as well as to tailor machine learning models to specific domain requirements, these methods are crucial. Focusing on e-learning, student engagement, and reinforcement learning, the yellow cluster investigates ML’s educational applications. This cluster exemplifies the ways in which data-driven, personalized learning can be improved through the use of machine learning in adaptive learning systems. The necessity to enhance user involvement with machine learning systems in knowledge management is brought to light by the purple cluster, which discusses optimization strategies and human-computer interaction. The importance of improving the competence and usability of AI-based systems is reflected in this cluster. There is a lot of unrealized potential for the commercial and organizational applications of machine learning and knowledge management, even if most of the research in these areas is focused on technical domains like computer science and engineering. Improving decision-making, operational efficiency, and BI could be achieved by better integration of machine learning into organizational knowledge management systems. There is much of space for further investigation in this area.

Figure 2 Research on ML and KM

Research on "machine learning" and "knowledge management" steadily increased over the years, as shown in Figure 2. In 1989, the initial research in this area was published. Around 2010 and forward, the number of publications rushed. During that time, groundbreaking innovations such as artificial intelligence (AI), the internet of things (IoT), and BD gathered steam, which is likely what caused this spike. Research was further strengthened by faster internet technologies like 4G, which hastened the usage and development of information management and machine learning.

A plethora of studies investigating the potential advantages of AI and ML for knowledge management (KM) detonated throughout the second half of the twentieth century. Figure 2 shows the dramatic increase in literature covering the area of ML/KM interaction from 1989 to 2010. Innovations in BD, the (IoT), and (AI) have changed the way businesses handle information and make decisions, which has led to this growth. To combat issues like knowledge loss and staff churn, machine learning is revolutionizing how businesses store and find new information. As ML automates the study of large datasets, tacit knowledge is transformed into explicit knowledge, which can then be more easily utilized by organizations. With this change, businesses may save important information that would be lost if workers left. Knowledge management systems are crucial to the success and sustainability of any business, and machine learning and AI are key components in this puzzle. The significance of big data has increased, which in turn has enhanced the function of ML in knowledge management. The development of analytical models that sift through massive datasets in search of hidden patterns and trends all made possible by big data lays the groundwork for machine learning algorithms, which in turn improve decision-making. Companies who want to remain ahead in a cutthroat industry by seeing possibilities and threats ahead of time will find this very useful. By utilizing real-time data and predictive insights, businesses that use ML into their KM systems improve decision-making and operational efficiency. But there are still obstacles to overcome when integrating ML with KM systems. Lack of knowledge or insufficient knowledge-sharing frameworks make it difficult for many firms to execute successful KM efforts. Machine learning's potential to boost operational efficiency could be hindered if employees aren't familiar with knowledge management systems; otherwise, they could not get the big picture of the problems impacting their company.  Problems with data quality, ethics, and the preparedness of organizations to implement ML continue to be major obstacles. To overcome these obstacles, we need to invest in technology, but we also need to change our culture so that we see information as an asset. Machine learning in knowledge management has recently gained traction in a number of industries, such as healthcare, education, and business intelligence.

The possibilities of ML to enhance predictive analytics, customer service, and decision-making have been the subject of a great deal of research. Adaptive learning systems have been developed in education, and ML has improved disease identification and individualized therapy in healthcare. The results demonstrate that ML has the ability to revolutionize knowledge management systems by making them smarter and more adaptive. Very little is known about how to effectively combine ML and KM in business and management contexts. This junction is hardly touched upon in a small percentage of the literature.  To close this knowledge gap, firms should invest in more focused studies that will teach them how to use ML to its full potential in enhancing KM systems, increasing operational efficiency, and preserving important information. By bringing together knowledge management and machine learning, there is enormous opportunity to boost organizational performance. An unparalleled upsurge in research and real-world applications has been spurred by the exponential rise of AI, big data, and ML technologies since 2010. To get the most out of these tools, though, companies have to conquer obstacles like implementing KM systems and getting employees involved. Organizations can use ML to improve decision-making, knowledge retention, and competitive advantage in a complex and competitive market by fixing these difficulties. Machine learning enhances information exchange, automates repetitive tasks, and promotes data-driven decision-making.  It offers scalability and supports large-scale data analysis. However, challenges emerge with data security, high implementation costs, and the need for trained staff. Incorporating machine learning into existing systems also presents difficulties. Expanding machine learning into new industries can lead to better service personalization, predictive maintenance, and innovation in knowledge management systems. Despite these opportunities, legislative obstacles, privacy and ethical concerns, rapid technological change, and the risk of over-reliance on algorithms could hinder its effective implementation in knowledge management.

Figure 3 SWOT analysis for Machine Learning and Knowledge Management

Figure 4 Value chains from big data to decision intelligence

Businesses collect massive volumes of data from consumer interactions, IoT devices, monetary transactions, and social media. Forecasts suggest the world's data sphere will grow exponentially, reaching 175 zettabytes by 2025. About 80-90% of this data, such as images, videos, and social media posts, remains unorganized. As more businesses implement real-time data pipelines for instant analysis, data ingestion rates are surging. Companies using big data analytics often see a 10% rise in profitability and productivity. Data cleaning and preprocessing take up 60-80% of the time in machine learning projects, ensuring quality for model training. Training time for sophisticated models can vary from hours to weeks, depending on the dataset and model complexity. Effective feature selection improves model accuracy by up to 20%, reducing costs and increasing efficiency. In industries like retail, healthcare, and finance, predictive analytics powered by machine learning often reach an accuracy level of 95%. Machine learning algorithms detect patterns and outliers, providing practical insights and recommendations. Organizations reduce their knowledge acquisition time by 50% with AI-driven knowledge discovery tools, quickly uncovering hidden insights. Companies that prioritize knowledge sharing increase innovation and performance by 20%, while knowledge management systems boost productivity by 25-30%. Data-driven decision-support systems cut decision time in half. Continuous learning within knowledge management systems keeps organizational knowledge up-to-date with real-time updates. Automated decision-making tools speed up operational workflows by 30-50%. Data-driven strategic planning, used by 90% of top organizations, improves real-time decision-making. By integrating decision intelligence with continuous improvement frameworks, organizations can increase efficiency by 15-25% annually. Real-time analytics allow organizations to quickly respond to market changes, boosting revenue by 8-10%. With 85% of businesses using these technologies to optimize strategy and resource allocation, informed decision-making becomes a key competitive advantage. The combination of decision intelligence, machine learning, and big data enhances economic value creation by 10-15% for many organizations. Data-driven decision intelligence boosts revenue by 30% compared to competitors not using advanced analytics. Streamlined decision-making processes reduce operational costs by 25%, and predictive analytics improve customer retention by 20% by enhancing the customer experience. Knowledge and decision intelligence frameworks increase innovation by 15%, shortening the time to develop and bring new products and services to market.

Knowledge management (KM) has been revolutionised by the advent of AI, ML, and big data. Automation of decision-making, management of massive datasets, discovery of new insights, and improvement of data-driven decision-making have all been achieved since these technologies proliferated in 2010. Although ML and AI play a vital role in developing more flexible KM systems, there are still certain obstacles that must be overcome before their complete potential can be realised. Data security, hefty implementation costs, and a qualified workforce requirement are some of these issues. In addition, sectors such as healthcare, education, and business intelligence are already feeling the effects of these technologies on innovation, operational efficiency, and customised offerings.  In order to make the most of machine learning and artificial intelligence, businesses need to get beyond organisational barriers including a lack of knowledge about knowledge management systems and ethical considerations, particularly when this area is constantly changing. There is still a need for additional research on the application of ML in managerial and commercial settings. Organisations who masterfully tackle these difficulties and utilise AI-enabled knowledge management will ultimately emerge victorious in a data-driven market that is getting more and more complicated.

The authors declare that they have no conflict of interest

No funding sources

The study was approved by the King Abdul Aziz University – Jeddah

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