Bridging the Digital–Energy Divide: Artificial Intelligence, Internet Connectivity, and Knowledge Management

Aim/Purpose: The rise of modern artificial intelligence (AI), in particular, machine learning (ML), has provided new opportunities and directions for knowledge management (KM). A central question for the future of KM is whether it will be dominated by an automation strategy that replaces knowledge work or whether it will support a knowledge-enablement strategy that enhances knowledge work and uplifts knowledge workers. This paper addresses this question by re-examining and updating a critical argument against KM by the sociologist of science Steve Fuller (2002), who held that KM was extractive and exploitative from its origins. Background: This paper re-examines Fuller’s argument in light of current developments in artificial intelligence and knowledge management technologies. It reviews Fuller’s arguments in its original context wherein expert systems and knowledge engineering were influential paradigms in KM, and it then considers how the arguments put forward are given new life in light of current developments in AI and efforts to incorporate AI in the KM technical stack. The paper shows that conceptions of tacit knowledge play a key role in answering the question of whether an automating or enabling strategy will dominate. It shows that a better understanding of tacit knowledge, as reflected in more recent literature, supports an enabling vision. Methodology: The paper uses a conceptual analysis methodology grounded in epistemology and knowledge studies. It reviews a set of historically important works in the field of knowledge management and identifies and analyzes their core concepts and conceptual structure. Contribution: The paper shows that KM has had a faulty conception of tacit knowledge from its origins and that this conception lends credibility to an extractive vision supportive of replacement automation strategies. The paper then shows that recent scholarship on tacit knowledge and related forms of reasoning, in particular, abduction, provide a more theoretically robust conception of tacit knowledge that supports the centrality of human knowledge and knowledge workers against replacement automation strategies. The paper provides new insights into tacit knowledge and human reasoning vis-à-vis knowledge work. It lays the foundation for KM as a field with an independent, ethically defensible approach to technology-based business strategies that can leverage AI without becoming a merely supporting field for AI. Findings: Fuller’s argument is forceful when updated with examples from current AI technologies such as deep learning (DL) (e.g., image recognition algorithms) and large language models (LLMs) such as ChatGPT. Fuller’s view that KM presupposed a specific epistemology in which knowledge can be extracted into embodied (computerized) but disembedded (decontextualized) information applies to current forms of AI, such as machine learning, as much as it does to expert systems. Fuller’s concept of expertise is narrower than necessary for the context of KM but can be expanded to other forms of knowledge work. His account of the social dynamics of expertise as professionalism can be expanded as well and fits more plausibly in corporate contexts. The concept of tacit knowledge that has dominated the KM literature from its origins is overly simplistic and outdated. As such, it supports an extractive view of KM. More recent scholarship on tacit knowledge shows it is a complex and variegated concept. In particular, current work on tacit knowledge is developing a more theoretically robust and detailed conception of human knowledge that shows its centrality in organizations as a driver of innovation and higher-order thinking. These new understandings of tacit knowledge support a non-extractive, human enabling view of KM in relation to AI. Recommendations for Practitioners: Practitioners can use the findings of the paper to consider ways to implement KM technologies in ways that do not neglect the importance of tacit knowledge in automation projects (which neglect often leads to failure). They should also consider how to enhance and fully leverage tacit knowledge through AI technologies and augment human knowledge. Recommendation for Researchers: Researchers can use these findings as a conceptual framework in research concerning the impact of AI on knowledge work. In particular, the distinction between replacement and enabling technologies, and the analysis of tacit knowledge as a structural concept, can be used to categorize and analyze AI technologies relative to KM research objectives. Impact on Society: The potential of AI on employment in the knowledge economy is a major issue in the ethics of AI literature and is widely recognized in the popular press as one of the pressing societal risks created by AI and specific types such as generative AI. This paper shows that KM, as a field of research and practice, does not need to and should not add to the risks created by automation-replacement strategies. Rather, KM has the conceptual resources to pursue a (human) knowledge enablement approach that can stand as a viable alternative to the automation-replacement vision. Future Research: The findings of the paper suggest a number of research trajectories. They include: Further study of tacit knowledge and its underlying cognitive mechanisms and structures in relation to knowledge work and KM objectives. Research into different types of knowledge work and knowledge processes and the role that tacit and explicit knowledge play. Research into the relation between KM and automation in terms of KM’s history and current technical developments. Research into how AI arguments knowledge works and how KM can provide an enabling framework.

Knowledge is recognized as a central component for an organisation to remain competitive. In this current phase, the blend of artificial intelligence (AI) and knowledge management (KM) frameworks can upgrade and improve organizational success. The role of knowledge management (KM) frameworks has evolved because of fast changing environment technology and continuously upgrading technology. AI combining KM can become that thriving component for organizations to be more progressive. Application of AI can be used in different forms of KM, like creation, storage, share and usage of knowledge. Different forms of AI technologies such as expert systems, intelligent search, chatbots, Robotics process automation, machine learning (ML) and natural language processing (NLP), can help to process large data and extract valuable information. AI-enabled KM systems in organisations can bring transformation through improved organisation learning, better decision-making based on data analysis, upgraded technology and innovation, and can provide aid to be more informed, intelligent, and adaptable to respond quickly with unpredictable corporate settings. If a business were to leverage the power of AI in organisation systems in India, they can gain competitive advantage, improve their innovation and agility in today's fast changing scenario. However, there are challenges in integrating AI and KM frameworks, such as data privacy and ethical concerns. This study explores the integration of KM frameworks and AI to increase organisation effectiveness also it examines how technologies of AI can help with creation of knowledge, its storage, knowledge sharing and application of knowledge. Though, it is also significant to consider challenges of using AI in KM frameworks, like data privacy and ethical concerns are particularly important, so that organization's sensitive information should not be compromised and can use AI technologies in transparent, accountable, and ethical manner.

PurposeIn this research, we seek to understand the effects of artificial intelligence (AI) and knowledge management (KM) processes in enhancing proactive green innovation (PGI) within oil and gas organizations. It also aims to investigate the moderator role of trust and sustainability in these relationships.Design/methodology/approachThis paper employs a quantitative analysis. Surveys have been gathered from the middle-line managers of twenty-four oil and gas government organizations to evaluate the perceptions of the managers towards AI, KM processes, trust, sustainability measures and proactive measures toward green innovation. Analytical and statistical tools that were employed in this study, including structural equation modeling with SmartPLSv3.9, have been used to analyze the data and to examine the measurement and structural models of this study.FindingsThe study results reveal a significant and positive impact of AI utilization, KM processes and PGI within oil and gas organizations. Furthermore, trust and sustainability turn out to be viable moderators affecting, and influencing the strength and direction of AI, KM and PGI relationships. In particular, higher levels of trust and more substantial sustainability commitments enhance the positive impact of AI and KM on green innovation outcomes.Practical implicationsUnderstanding the impact of AI, KM, trust and sustainability offers valuable insights for organizational leaders and policymakers seeking to promote proactive green innovation within the oil and gas industry. Thus, organizations can increase the efficiency of sustainable product development, process improvement and environmental management by using robust AI technologies and effective KM systems. Furthermore, fostering trust among stakeholders and embedding sustainability principles into organizational culture can amplify the effectiveness of AI and KM initiatives in driving green innovation outcomes.Originality/valueThis study extends the current knowledge by assessing the effect of AI and KM on proactive green innovation while accounting for trust and sustainability as moderators. Utilizing quantitative methods offers a nuanced understanding of the complex interactions between these variables, thereby advancing theoretical knowledge in the fields of innovation management, sustainability and organizational behavior. Additionally, the identification of specific mechanisms and contextual factors enriches practical insights for organizational practitioners striving for a practical understanding of the dynamics of the complexities of sustainable innovation in an AI-driven era.

The relationship between Artificial Intelligence (AI) and Knowledge Management (KM) is essential for modern organizations. Recent advances in AI offer opportunities to transform KM by automating tasks such as data analysis and categorization, but they also pose challenges. This study addresses the role of AI in KM of modern organizations, the most commonly used AI techniques to generate knowledge, and the period of greatest research on the impact of AI on KM. Topics such as the role of technology in KM, the transition from organizational learning to KM, and the relationship between KM processes and AI are analyzed. Bibliometric analysis of 23 articles highlights significant growth in information technologies and AI, which are redefining industries and unleashing great creative potential. Generative AI is leading this revolution and promises a $4.31 trillion market by 2030. Although promising, an ethical dilemma arises over replacing human talent in some organizations due to the speed of AI in creating, modeling and transferring knowledge. . . Despite the challenges, the future of AI promises to improve economic outcomes and positively impact global GDP, transforming industries and opening new frontiers. The debate on the balance between AI and human talent is raised as a key question for future research.

Knowledge management (KM) is crucial component for business development in modern enterprises and this type of management is facilitated through technology. Nevertheless, conventional knowledge management systems (KMS) face problems concerning, but not limited to, information silos, difficulty in accessing data, and the complexity in managing unstructured data. As new advancements are made towards Natural Language Processing (NLP), Artificial Intelligence (AI) technologies that allow for contextual knowledge discovery, intelligent search, automated summarization, and real time content classification become readily available. This research analyzes the application of NLP systems concerning their integration with knowledge systems in business, information retrieval, enterprise search, and knowledge recommendation systems. For these integrations to be successful, Name Entity Recognition (NER), semantic search, Retrieval-Augmented Generation (RAG), Optical Character Reader (OCR), and Explainable AI (XAI) technologies need to be utilized. This will assure that decision-making processes are secure and ethical. This paper also presents an NLP-Driven Knowledge Management Framework (NLP-KMF), which is a novel framework that helps manage knowledge. The paper discusses the real-world usage of NLP-powered knowledge management in corporate learning, customer service, and compliance with Google, Accenture, IBM, and JPMorgan Chase serving as the centers of case studies. Strategies to counter issues such as AI bias and misinformation alongside privacy threats are discussed as well. The last section of the paper analyzes the forthcoming research areas that could include topics such as multimodal AI for knowledge management, AI repositories that continuously learn, and decision intelligence driven by AI. This serves as a constructive and precise plan for organizations that wish to evolve from static knowledge databases to dynamic self-adapting AI systems.

Purpose The interactive green innovation (IGI) highlights cross-organisational collaboration, stakeholder engagement and shared knowledge flows, while traditional/internal innovation centres on firm-level processes. Thus, the purpose of this study is to investigate how the integration of artificial intelligence (AI) and knowledge management (KM) processes drives IGI within the Iraqi oil industry. This study further explores the moderating roles of trust in technology and sustainability initiatives in this relationship. Design/methodology/approach Using a cross-sectional survey of 612 middle management executives in Iraqi oil companies, this study uses structural equation modelling (SmartPLS v3.9) and statistical analyses (SPSS v26) to validate the conceptual framework and examine the hypothesised relationships. Findings The findings of this study reveal that AI significantly enhances KM processes and directly fosters IGI (β = 0.448, f2 = 0.273 and p < 0.001). KM processes mediate the relationship between AI and IGI (β = 0.251, f2 = 0.153 and p < 0.001) and serve as a pivotal enabler of sustainable practices. Although trust and sustainability exert moderating effects, their influence is relatively modest. These results underscore the transformative potential of AI and KM integration in advancing IGI in resource-intensive industries. Practical implications This research offers actionable insights for oil sector leaders aiming to embed environmental sustainability into core business practices. By harnessing AI and KM, firms can optimise resource use, streamline operational processes and cultivate a culture of innovation. Moreover, investing in trust-building and sustainability-focused strategies can enhance organisational resilience and competitive advantage in an increasingly eco-conscious global market. Originality/value This study bridges a critical gap by elucidating the combined impact of AI and KM on IGI in the context of the Iraqi oil industry – a sector facing profound environmental and geopolitical challenges. By incorporating trust and sustainability as moderating factors, it deepens understanding of the drivers of innovation adoption in traditional industries and highlights pathways for sustainable transformation.

G20 roadmap for carbon neutrality: The role of Paris agreement, artificial intelligence, and energy transition in changing geopolitical landscape

AI and Knowledge Management Evolution

How was the orchestration AI and Knowledge Management Implementation in Today's Business? Objective: Examined the orchestration AI and Knowledge Management in Today's Business. Method: The author used a theory review method that relates to AI and Knowledge Management. Journal review and point of view on how it has been implemented in today's business. Findings: The integration of AI and KM was crucial for optimizing operations and competitiveness in modern businesses It transcends geographical boundaries and reshapes global business practices. Challenges include integration complexities and data privacy concerns, but the benefits include unlocking insights and fostering innovation. Looking ahead, AI-KM synergy promises transformative change and innovation. Conclusion: integration of Artificial Intelligence (AI) and Knowledge Management (KM) emerges as a vital strategy for modern businesses, offering transformative benefits despite challenges. By optimizing operations, fostering innovation, and transcending geographical boundaries, AI-KM synergy reshapes global business practices and drives organizational agility. As emerging trends like augmented intelligence and human-machine collaboration redefine the future of work, strategic orchestration of AI and KM becomes essential for navigating complexity, capitalizing on opportunities, and thriving in the digital era.

PurposeThis paper draws on the dynamic capabilities (DC) view to develop a conceptual framework that explicates the mechanisms through which human intelligence (HI) and artificial intelligence (AI) substitute and complement each other for organizational knowledge management (KM) while considering the role of ethics.Design/methodology/approachThis is a conceptual paper that draws on DC theory and integrates insights from the burgeoning literature on organizational AI adoption and application to develop a conceptual framework that explains the mechanisms through which HI and AI may substitute and complement each other for organizational KM to develop DC.FindingsThe conceptual framework demonstrates that substituting HI with AI is suitable for external environmental scanning to identify opportunities, while AI substitution for HI is ideal for internal scanning through data analytics. Additionally, HI complementing AI is effective for seizing opportunities by aligning internal competencies with external opportunities, whereas AI complementing HI is beneficial for reconfiguring assets by transforming tacit knowledge into explicit knowledge. This substitution and complementarity between HI and AI shape KM processes—acquisition, conversion, application, and retention—that influence organizational performance, depending on how internal and external ethical standards govern organizational AI use.Research limitations/implicationsThe paper presents key insights into how AI may substitute for HI for internal data analytics in KM but may be ineffective for external environmental scanning to sense opportunities. It further reveals that using AI to capture and convert tacit knowledge (HI) to explicit knowledge requires ethical considerations at the organizational level, but ethical considerations are necessary at the employee/manager level when HI relies on AI-generated insights for strategic decisions.Practical implicationsThe study implies that in environments with defined regulations for AI and KM (e.g. privacy protection), responsibility for the consequences of AI-HI substitution and complementarity in developing DC can be assigned to specific steps in the KM process. However, in environments with undefined regulations, responsibility must be assigned to people, units or departments who manage the entire KM process to ensure accountability for ethical breaches.Originality/valueThis study proposes AI-HI substitution and complementarity in organizations to extend the understanding of the relationship between AI and HI to DC development.

Matias Alvarado is currently a Research Scientist at the Centre of Research and Advanced Studies (CINVESTAV-IPN, Mexico). He got a Ph.D. degree in computer science from the Technical University of Catalonia, with a major in artificial intelligence. He received the B.Sc. degree in mathematics from the National Autonomous University of Mexico. His interests in research and technological applications include knowledge management and decision making; autonomous agents and multiagent systems for supply chain disruption management; concurrency control, pattern recognition and computational logic. He is the author of about 50 scientific papers, a Journal Special Issues Guest Editor on topics of artificial intelligence and knowledge management for the oil industry; an academic, invited to the National University of Singapore, Technical University of Catalonia, University of Oxford, University of Utrecht, and Benemerita Universidad Autonoma de Puebla. Leonid Sheremetov received the Ph.D. degree in computer science in 1990 from St. Petersburg Institute for Informatics and Automation of the Russian Academy of Sciences, where he has worked as a Research Fellow and a Senior Research Fellow from 1982. Now he is a Principal Investigator of the Research Program on Applied Mathematics and Computing of the Mexican Petroleum Institute, where he leads the Distributed Intelligent Systems Group, and a part-time professor of the Artificial Intelligence Laboratory of the Centre for Computing Research of the National Polytechnic Institute (CIC-IPN), Mexico. His current research interests include multiagent systems, semantic WEB, decision support systems, and enterprise information integration. His group developed CAPNET agent platform and has been involved in several projects for the energy industry ranging from petroleum exploration and production to knowledge management with special focus on industrial exploitation of agent technology. He is also a member of the Editorial Boards of several journals. Rene Banares-Alcantara has worked in the University of Oxford from October 2003 and is now a Reader in engineering science at the Department of Engineering Science and a Fellow in engineering at New College. He previously held a readership at the University of Edinburgh and lectureships in Spain and at the Universidad Nacional Autonoma de Mexico (UNAM). He obtained his undergraduate degree from UNAM and the M.S. and Ph.D. degrees from Carnegie Mellon University (CMU). Starting with his work at CMU, his research interests have been in the area of process systems engineering, in particular chemical process design and synthesis. He has developed a strong relationship with computer science/artificial intelligence research groups in different universities and research institutes, with current research also linking to social and biological modeling. He has (co)authored more than 100 refereed publications and has been a Principal Investigator and a Researcher in several EPSRC and European Union projects. Francisco Cantu-Ortiz obtained the Ph.D. degree in artificial intelligence from the University of Edinburgh, United Kingdom and the Bachelor's degree in computer systems engineering from the Tecnologico de Monterrey (ITESM), Mexico. He is a Full Professor of artificial intelligence at Tecnologico de Monterey and is also the Dean of research and graduate Studies. He has been the Head of the Center for Artificial Intelligence and of the Informatics Research Center. Dr. Cantu-Ortiz has been the General Chair of about 15 international conferences in artificial intelligence and expert system and was a Local Chair of the International Joint Conference on Artificial Intelligence in 2003. His research interests include knowledge based systems and inference, machine learning, and data mining using Bayesian and statistical techniques for business intelligence, technology management, and entrepreneurial science. More recently, his interests have extended to epistemology and philosophy of science. He was the President of the Mexican Society for Artificial Intelligence and is a member of the IEEE Computer Society and the ACM.

The integration of Artificial Intelligence (AI) and Knowledge Management (KM) structures have emerged as a powerful strategy to streamline business processes and gain a competitive edge. The primary objective is to examine how the integration of AI and KM improves business efficiency, fosters innovation, and enhances competitive advantage across various industries, providing insights into the measurable benefits. Key variables include the level of AI integration, knowledge management (KM) effectiveness, business process efficiency, competitive advantage, and employee satisfaction. These factors were measured using standardized scales to determine their interrelations and impact on business performance. Data was collected using surveys from 750 employees across 10 companies, alongside 15 interviews with senior managers. SPSS was used to analyze quantitative data. SPSS was also used for correlation, regression, and descriptive statistics of key variables. There are strong positive correlations between business process efficiency and AI integration level (0.64) as well as between competitive advantage and KM effectiveness (0.67), and the results show that all variables have high mean scores, with business process efficiency having the highest mean (4.22) and employee satisfaction having the lowest (3.98). The investigation concludes that integrating AI technologies with KM systems significantly improves business process efficiency and provides a competitive edge. Organizations should prioritize these integrations to stay competitive, though challenges such as resistance to change must be managed.

The process of generating, disseminating, using, and managing an organization’s information and knowledge is known as knowledge management (KM). Conventional KM has undergone modifications throughout the years, but documentation has always been its foundation. However, the significant move to remote and hybrid working has highlighted the shortcomings in current procedures. These gaps will be filled by artificial intelligence (AI), which will also alter how KM is transformed and knowledge is handled. This article analyzes studies from 2012 to 2022 that examined AI and KM, with a particular emphasis on how AI may support businesses in their attempts to successfully manage knowledge and information. This critical review examines the current approaches in light of the literature that is currently accessible on AI and KM, focusing on articles that address practical applications and the research background. Furthermore, this review provides insight into potential future study directions and improvements by presenting a critical evaluation.

Objective. This study examined the scientific literature addressing the relationship between artificial intelligence (AI) and knowledge management (KM) to identify the main issues around this binomial. Design/Methodology/Approach. We used co-word analysis as our bibliometric technique. We only worked with each article's keyword and keyword plus variable. Each cluster within the map was assigned a generic name according to the theme it represented. We also conducted some analysis based on the degree of centrality of keywords per cluster. We also performed qualitative analyses of each cluster's terms and word relationships. Results/Discussion. The co-occurrence map of terms revealed nine clusters related to the relationship between KM and AI: (1) main and central themes, (2) innovation and system design, (3) knowledge representation and learning, (4) theoretical models and information management, (5) collaborative networks and dynamics, (6) natural language processing, (7) ethics and governance, (8) visualization and knowledge representation, and (9) emerging and specialized areas. Conclusions. This study contributes to closing a gap in the literature by demonstrating that integrating AI and KM is a key alliance to meet the challenges of the knowledge society. AI strengthens conventional KM processes and opens new opportunities to create organizational and societal value. However, implementing AI requires a balanced approach that combines technological innovation with ethical and human considerations.

Emerging artificial intelligence (AI) capabilities will likely pervade nearly all organizational contours and activities, including knowledge management (KM). This article aims to uncover opportunities associated with the implementation of emerging systems empowered by AI for KM. In doing so, we explicate the potential role of AI in supporting fundamental dimensions of KM: creation, storage and retrieval, sharing, and application of knowledge. We then propose practical ways to build the partnership between humans and AI in supporting organizational KM activities and provide several implications for the development and management of AI systems based on the components of people, infrastructures, and processes.