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AI and Knowledge Management: A Comprehensive Guide
Author: Dr. Evelyn Reed, PhD, Chief Knowledge Officer at InnovateKnowledge Solutions, with 15 years of experience in knowledge management and 5 years specializing in AI applications within KM strategies.
Publisher: Knowledge Management Institute (KMI), a leading research and publishing organization specializing in knowledge management best practices and technological advancements, including AI's impact on the field.
Editor: Sarah Chen, MA in Library and Information Science, with 10 years of experience in editing technical and academic publications related to information technology and knowledge management.
Summary: This guide explores the transformative potential of AI in revolutionizing knowledge management. It outlines best practices for integrating AI tools, addressing common pitfalls, and maximizing the benefits of AI-powered knowledge management systems. Key areas covered include AI-driven knowledge discovery, intelligent search, automated content creation, and ethical considerations. The guide emphasizes the importance of a human-centered approach, ensuring AI augments, not replaces, human expertise in managing organizational knowledge.
Keywords: AI and knowledge management, AI-powered knowledge management, knowledge management systems, AI in KM, intelligent knowledge management, AI knowledge discovery, knowledge automation, AI for knowledge sharing, ethical considerations in AI KM.
1. Introduction: The Rise of AI in Knowledge Management
The convergence of artificial intelligence (AI) and knowledge management (KM) is reshaping how organizations capture, organize, and leverage their intellectual capital. AI offers powerful tools to automate traditionally manual KM processes, improving efficiency, accuracy, and accessibility of organizational knowledge. This guide provides a comprehensive overview of "AI and knowledge management," exploring its benefits, challenges, and best practices. The integration of AI in KM isn't simply about replacing human tasks; it's about augmenting human capabilities to unlock new levels of organizational intelligence.
2. AI-Powered Knowledge Discovery: Unveiling Hidden Insights
One of the most significant contributions of AI to knowledge management is its ability to uncover hidden insights within vast datasets. AI algorithms, particularly machine learning, can analyze unstructured data – such as emails, documents, and social media conversations – to identify patterns, trends, and relationships that might otherwise remain undetected. This "AI and knowledge discovery" process empowers organizations to make data-driven decisions, identify emerging opportunities, and proactively address potential risks.
3. Intelligent Search and Knowledge Retrieval: Finding Information Faster
AI significantly enhances knowledge retrieval by powering intelligent search functionalities. Unlike traditional keyword-based search, AI-powered search engines understand the context and meaning behind queries, delivering more relevant and precise results. Natural language processing (NLP) allows users to search using natural language, eliminating the need for complex search syntax. This improvement in "AI and knowledge retrieval" significantly boosts employee productivity and decision-making speed.
4. Automated Content Creation and Knowledge Synthesis: Streamlining KM Processes
AI can automate the creation and synthesis of knowledge, freeing up human experts to focus on higher-value tasks. AI-powered tools can summarize lengthy documents, generate reports, and even create new content based on existing data. This "AI and content creation" within KM streamlines workflows and accelerates the knowledge creation cycle.
5. AI-Driven Knowledge Sharing and Collaboration: Fostering a Knowledge-Sharing Culture
AI facilitates knowledge sharing and collaboration by connecting individuals with relevant expertise and information. AI-powered platforms can recommend experts, suggest relevant documents, and even facilitate knowledge transfer through personalized learning pathways. This enhanced "AI and knowledge sharing" fosters a more collaborative and knowledge-rich organizational environment.
6. Best Practices for Implementing AI in Knowledge Management
Successfully integrating AI into KM requires a strategic approach. Key best practices include:
Defining clear objectives: Establish specific, measurable, achievable, relevant, and time-bound (SMART) goals for AI implementation.
Data quality and preparation: Ensure the data used to train AI models is accurate, complete, and consistent.
Selecting the right AI tools: Choose tools that align with organizational needs and integrate seamlessly with existing KM systems.
Change management and training: Provide adequate training and support to employees to ensure successful adoption of new AI-powered tools.
Monitoring and evaluation: Continuously monitor the performance of AI systems and make adjustments as needed.
7. Common Pitfalls to Avoid in AI and Knowledge Management
Despite the potential benefits, organizations must be aware of potential pitfalls:
Data bias and fairness: AI models can inherit biases present in the data they are trained on, leading to unfair or discriminatory outcomes.
Lack of transparency and explainability: It can be difficult to understand how some AI models arrive at their conclusions, hindering trust and accountability.
Integration challenges: Integrating AI tools with existing KM systems can be complex and time-consuming.
Security and privacy concerns: AI systems can be vulnerable to security breaches and privacy violations.
Over-reliance on AI: AI should augment, not replace, human expertise in KM.
8. Ethical Considerations in AI and Knowledge Management
The ethical implications of AI in KM must be carefully considered. Organizations should develop ethical guidelines to address issues such as data privacy, algorithmic bias, and transparency. A human-centered approach is crucial, ensuring that AI supports, rather than undermines, human judgment and autonomy.
9. Conclusion
The integration of AI into knowledge management offers tremendous potential to transform how organizations manage and leverage their intellectual assets. By embracing best practices, addressing potential pitfalls, and prioritizing ethical considerations, organizations can harness the power of AI to create more efficient, effective, and insightful knowledge management systems. The future of "AI and knowledge management" is bright, promising a more informed, innovative, and collaborative work environment.
FAQs
1. What are the key benefits of using AI in knowledge management? Improved efficiency, enhanced knowledge discovery, faster knowledge retrieval, automated content creation, and improved knowledge sharing.
2. What types of AI technologies are used in knowledge management? Machine learning, natural language processing (NLP), deep learning, and knowledge graphs.
3. How can organizations ensure data quality for AI-powered KM systems? Through rigorous data cleansing, validation, and ongoing monitoring.
4. What are the ethical considerations of using AI in knowledge management? Data privacy, algorithmic bias, transparency, and accountability.
5. How can organizations mitigate the risk of bias in AI-powered KM systems? By carefully selecting training data, auditing algorithms for bias, and promoting diversity in AI development teams.
6. What are the challenges of integrating AI into existing KM systems? Data integration, system compatibility, and change management.
7. How can organizations measure the success of their AI-powered KM initiatives? Through key performance indicators (KPIs) such as improved knowledge retrieval time, increased employee productivity, and better decision-making.
8. What is the role of human expertise in AI-powered knowledge management? Human expertise remains crucial for overseeing AI systems, interpreting results, and ensuring ethical considerations are addressed.
9. What are the future trends in AI and knowledge management? Increased use of advanced AI techniques, greater personalization of knowledge access, and tighter integration with other enterprise systems.
Related Articles:
1. "AI-Driven Knowledge Graphs for Enhanced Knowledge Management": Explores how knowledge graphs leverage AI to structure and connect organizational knowledge, improving search and discovery.
2. "Building an AI-Powered Knowledge Base: A Step-by-Step Guide": Provides practical guidance on designing and implementing an AI-powered knowledge base within an organization.
3. "The Impact of AI on Knowledge Sharing and Collaboration": Examines how AI fosters a more collaborative and knowledge-rich organizational culture.
4. "Ethical Considerations in AI-Powered Knowledge Management Systems": Deep dives into ethical challenges and best practices related to data privacy, bias, and transparency.
5. "Measuring the ROI of AI in Knowledge Management": Provides strategies and metrics for evaluating the effectiveness of AI investments in KM.
6. "Overcoming the Challenges of Integrating AI into Existing KM Systems": Addresses practical hurdles and solutions related to integrating AI tools into existing infrastructure.
7. "The Future of Work: AI's Role in Transforming Knowledge Management": Explores the long-term impact of AI on the future of work and knowledge management.
8. "Case Studies: Successful AI Implementations in Knowledge Management": Provides real-world examples of organizations that have successfully integrated AI into their KM strategies.
9. "AI and the Democratization of Knowledge: Making Knowledge Accessible to All": Discusses how AI can break down information silos and make organizational knowledge more readily available to everyone.
| ai and knowledge management: Knowledge Management Irma Becerra-Fernandez, Rajiv Sabherwal, Richard Kumi, 2024-02-23 Knowledge Management: Systems and Processes in the AI Era, Third Edition, is aimed at students and managers who seek detailed insights into contemporary knowledge management (KM). It explains the concepts, theories, and technologies that provide the foundation for knowledge management; the systems and structures that constitute KM solutions; and the processes for developing, deploying, and evaluating these KM solutions. This book serves as a complete introduction to the subject of knowledge management, incorporating technical and social aspects, as well as concepts, practical examples, traditional KM approaches, and emerging topics. This third edition has been revised and expanded to include more coverage of emergent trends such as cloud computing, online communities, crowdsourcing, and artificial intelligence. Aimed at advanced undergraduate, postgraduate, and MBA students who are seeking a comprehensive perspective on knowledge management, Knowledge Management is also complemented by online support for lecturers including suggested solutions to the many review questions and application exercises contained within the book. |
| ai and knowledge management: AI-empowered Knowledge Management Soumi Majumder, Nilanjan Dey, 2022-02-23 This book is focused on AI-empowered knowledge management to improve processes, implementation of technology for providing easy access to knowledge and the impact of knowledge management to promote the platform for generation of new knowledge through continuous learning. The book discusses process of knowledge management which includes entirety of the creation, distribution, and maintenance of knowledge to achieve organizational objectives. It also covers knowledge management tools which enable and enhance knowledge creation, codification, and transfer within business firms thereby reducing the burden of work and allowing application of resources and effective usage towards practical tasks. An immense growth of artificial intelligence in business organizations has occurred and AI-empowered knowledge management practice is leading towards growth and development of the organization. |
| ai and knowledge management: Artificial Intelligence for Knowledge Management Eunika Mercier-Laurent, M. Özgür Kayalica, Mieczyslaw Lech Owoc, 2021-07-03 This book features a selection of extended papers presented at the 8th IFIP WG 12.6 International Workshop on Artificial Intelligence for Knowledge Management, AI4KM 2021, held in Yokohama, Japan, in January 2021, in the framework of the International Joint Conference on Artificial Intelligence, IJCAI 2020.* The 14 revised and extended papers presented together with an invited talk were carefully reviewed and selected for inclusion in this volume. They present new research and innovative aspects in the field of knowledge management and discuss methodological, technical and organizational aspects of artificial intelligence used for knowledge management. *The workshop was held virtually. |
| ai and knowledge management: Artificial Intelligence for Knowledge Management Eunika Mercier-Laurent, Danielle Boulanger, 2017-04-10 This book features a selection of papers presented at the Third IFIP WG 12.6 International Workshop on Artificial Intelligence for Knowledge Management, AI4KM 2015, held in Buenos Aires, Argentina, in July 2015, in the framework of the International Joint Conference on Artificial Intelligence, IJCAI 2015. The 9 revised and extended papers were carefully reviewed and selected from 15 submissions. They present new research and innovative aspects in the field of knowledge management such as knowledge models, KM and Web, knowledge capturing and learning, and KM and AI intersections. |
| ai and knowledge management: Artificial Intelligence and Knowledge Management Akira Hanako, 2016-05-24 Artificial intelligence and knowledge management have transformed the process of knowledge circulation and database management in different business enterprises and corporate organizations. Some of the significant topics discussed in the chapters of this book are AI planning strategies and tools, AI tools for information processing, data mining, knowledge-based systems, etc. It explores the innovative concepts and advancements in these emerging fields. The book is an invaluable source of knowledge for students and researchers involved in this field at various levels. |
| ai and knowledge management: Handbook of Research on Artificial Intelligence and Knowledge Management in Asia’s Digital Economy Ordóñez de Pablos, Patricia, Zhang, Xi, Almunawar, Mohammad Nabil, 2022-11-11 Artificial intelligence (AI) and knowledge management can create innovative digital solutions and business opportunities in Asia from circular and green economies to technological disruption, innovation, and smart cities. It is essential to understand the impact and importance of AI and knowledge management within the digital economy for future development and for fostering the best practices within 21st century businesses. The Handbook of Research on Artificial Intelligence and Knowledge Management in Asia’s Digital Economy offers conceptual frameworks, empirical studies, and case studies that help to understand the latest developments in artificial intelligence and knowledge management, as well as its potential for digital transformation and business opportunities in Asia. Covering topics such as augmented reality. Convolutional neural networks, and digital transformation, this major reference work generates enriching debate on the challenges and opportunities for economic growth and inclusion in the region among business executives and leaders, IT managers, policymakers, government officials, students and educators of higher education, researchers, and academicians. |
| ai and knowledge management: Artificial Intelligence for Knowledge Management Eunika Mercier-Laurent, Danielle Boulanger, 2019-09-11 This book features a selection of extended papers presented at the 5th IFIP WG 12.6 International Workshop on Artificial Intelligence for Knowledge Management, AI4KM 2017, held in Melbourne, VIC, Australia, in August 2017, in the framework of the International Joint Conference on Artificial Intelligence, IJCAI 2017. The 11 revised and extended papers were carefully reviewed and selected for inclusion in this volume. They present new research and innovative aspects in the field of knowledge management such as machine learning, knowledge models, KM and Web, knowledge capturing and learning, and KM and AI intersections. |
| ai and knowledge management: Artificial Intelligence for Knowledge Management Mieczysław Lech Owoc, Maciej Pondel, 2021-08-05 This book features a selection of extended papers presented at the 7th IFIP WG 12.6 International Workshop on Artificial Intelligence for Knowledge Management, AI4KM 2019, held in Macao, China, in August 2019, in the framework of the International Joint Conference on Artificial Intelligence, IJCAI 2019. The 8 revised and extended papers were carefully reviewed and selected for inclusion in this volume. They present new research and innovative aspects in the field of knowledge management such as machine learning, knowledge models, KM and Web, knowledge capturing and learning, and KM and AI intersections. |
| ai and knowledge management: Artificial Intelligence for Knowledge Management Eunika Mercier-Laurent, Mieczysław Lech Owoc, Danielle Boulanger, 2016-02-02 This book features a selection of papers presented at the Second IFIP WG 12.6 International Workshop on Artificial Intelligence for Knowledge Management, AI4KM 2014, held in Wroclaw, Poland, in September 2014, in the framework of the Federated Conferences on Computer Science and Information Systems, FedCSIS 2014. The 9 revised and extended papers and one invited paper were carefully reviewed and selected for inclusion in this volume. They present new research and innovative aspects in the field of knowledge management and are organized in the following topical sections: tools and methods for knowledge acquisition; models and functioning of knowledge management; techniques of artificial intelligence supporting knowlege management; and components of knowledge flow. |
| ai and knowledge management: Knowledge Management for Leadership and Communication Jon-Arild Johannessen, 2020-03-11 With the establishment of the innovation economy, the Fourth Industrial Revolution is becoming a reality. As this occurs, new forms of leadership arise, generated by the interaction between leadership functions and neurology. This innovative book asks the question: what are the key value creation processes in the innovation economy? |
| ai and knowledge management: Digital Technology Advancements in Knowledge Management Gyamfi, Albert, Williams, Idongesit, 2021-06-18 Knowledge management has always been about the process of creating, sharing, using, and applying knowledge within and between organizations. Before the advent of information systems, knowledge management processes were manual or offline. However, the emergence and eventual evolution of information systems created the possibility for the gradual but slow automation of knowledge management processes. These digital technologies enable data capture, data storage, data mining, data analytics, and data visualization. The value provided by such technologies is enhanced and distributed to organizations as well as customers using the digital technologies that enable interconnectivity. Today, the fine line between the technologies enabling the technology-driven external pressures and data-driven internal organizational pressures is blurred. Therefore, how technologies are combined to facilitate knowledge management processes is becoming less standardized. This results in the question of how the current advancement in digital technologies affects knowledge management processes both within and outside organizations. Digital Technology Advancements in Knowledge Management addresses how various new and emerging digital technologies can support knowledge management processes within organizations or outside organizations. Case studies and practical tips based on research on the emerging possibilities for knowledge management using these technologies is discussed within the chapters of this book. It both builds on the available literature in the field of knowledge management while providing for further research opportunities in this dynamic field. This book highlights topics such as human-robot interaction, big data analytics, software development, keyword extraction, and artificial intelligence and is ideal for technology developers, academics, researchers, managers, practitioners, stakeholders, and students who are interested in the adoption and implementation of new digital technologies for knowledge creation, sharing, aggregation, and storage. |
| ai and knowledge management: Artificial Intelligence for Knowledge Management Eunika Mercier-Laurent, 2020-07-15 This book features a selection of extended papers presented at the 6th IFIP WG 12.6 International Workshop on Artificial Intelligence for Knowledge Management, AI4KM 2018, held in Stockholm, Sweden, in July 2018, in the framework of the International Joint Conference on Artificial Intelligence, IJCAI 2018. The 11 revised and extended papers were carefully reviewed and selected for inclusion in this volume. They present new research and innovative aspects in the field of knowledge management such as machine learning, knowledge models, KM and Web, knowledge capturing and learning, and KM and AI intersections. |
| ai and knowledge management: Applying Knowledge Management Ian Watson, 2003-01-04 The wholesale capture and distribution of knowledge over the last thirty years has created an unprecedented need for organizations to manage their knowledge assets. Knowledge Management (KM) addresses this need by helping an organization to leverage its information resources and knowledge assets by remembering and applying its experience. KM involves the acquisition, storage, retrieval, application, generation, and review of the knowledge assets of an organization in a controlled way. Today, organizations are applying KM throughout their systems, from information management to marketing to human resources. Applying Knowledge Management: Techniques for Building Corporate Memories examines why case-based reasoning (CBR) is so well suited for KM. CBR can be used to adapt solutions originally designed to solve problems in the past, to address new problems faced by the organization. This book clearly demonstrates how CBR can be successfully applied to KM problems by presenting several in-depth case-studies. Ian Watson, a well-known researcher in case-based reasoning and author of the introductory book, Applying CBR: Techniques for Enterprise Systems has written this book specifically for IT managers and knowledge management system developers.* Provides 7 real-world applications of knowledge management systems that use case-based reasoning techniques.* Presents the technical information needed to implement a knowledge management system.* Offers insights into the development of commercial KM CBR applications* Includes information on CBR software vendors, CBR consultants and value added resellers |
| ai and knowledge management: AI-Empowered Knowledge Management in Education Sayan Chakraborty, |
| ai and knowledge management: Managerial Issues in Digital Transformation of Global Modern Corporations Esakki, Thangasamy, 2021-06-25 Efficient management of managerial tasks by capable managers is essential in order to grow and remain competitive in today’s global business market. On the other hand, digital transformation enables organizations to better compete with their global counterparts. In the process of digital transformation, many firms find it difficult to acquire qualified leadership with adequate knowledge and competence to drive success. Without integrating the dual edges of managerial competence and digital evolution, it is next to impossible for a firm to both survive and grow. Managerial Issues in Digital Transformation of Global Modern Corporations is a critical scholarly publication that examines current challenges in the digital transformation of modern business corporations from a managerial and leadership perspective. Featuring a wide range of topics such as digital transformation, marketing, and global business, this book is ideal for corporate executives, managers, IT specialists, entrepreneurs, business administrators, industry practitioners, academicians, researchers, policymakers, and students from various relevant disciplines that include economics, information and technology, business administration, management science, and commerce. |
| ai and knowledge management: Secure Knowledge Management In The Artificial Intelligence Era Ram Krishnan, H. Raghav Rao, Sanjay K. Sahay, Sagar Samtani, Ziming Zhao, 2022-02-22 This book constitutes the refereed proceedings of the 9th International Conference On Secure Knowledge Management In Artificial Intelligence Era, SKM 2021, held in San Antonio, TX, USA, in 2021. Due to the COVID-19 pandemic the conference was held online. The 11 papers presented were carefully reviewed and selected from 30 submissions. They were organized according to the following topical sections: intrusion and malware detection; secure knowledge management; deep learning for security; web and social network. |
| ai and knowledge management: A Research Agenda for Knowledge Management and Analytics Jay Liebowitz, 2021-01-29 Leveraging the knowledge gained from Knowledge Management and from the growing fields of Analytics and Artificial Intelligence (AI), this Research Agenda highlights the research gaps, issues, applications, challenges and opportunities related to Knowledge Management (KM). Exploring synergies between KM and emerging technologies, leading international scholars and practitioners examine KM from a multidisciplinary perspective, demonstrating the ways in which knowledge sharing worldwide can be enhanced in order to better society and improve organisational performance. |
| ai and knowledge management: Understanding, Implementing, and Evaluating Knowledge Management in Business Settings Merlo, Tereza Raquel, 2022-06-24 Although there are numerous publications in the field of knowledge management (KM), there are still gaps in the literature regarding the aspects of KM that reflect new technology adoption and a deeper analysis discussing the interlinked process between KM and data analytics in business process improvement. It is essential for business leaders to understand the role and responsibilities of leaders for the adoption and consolidation of a KM system that is effective and profitable. Understanding, Implementing, and Evaluating Knowledge Management in Business Settings provides a comprehensive approach to KM concepts and practices in corporations and business organizations. Covering topics such as information overload, knowledge sharing adoption, and collective wisdom, this premier reference source is a comprehensive and essential resource for business executives, managers, IT specialists and consultants, libraries, students, entrepreneurs, researchers, and academicians. |
| ai and knowledge management: Knowledge Management for Leadership and Communication Jon-Arild Johannessen, 2020-03-11 With the establishment of the innovation economy, the Fourth Industrial Revolution is becoming a reality. As this occurs, new forms of leadership arise, generated by the interaction between leadership functions and neurology. This innovative book asks the question: what are the key value creation processes in the innovation economy? |
| ai and knowledge management: Knowledge Management Herwig Rollett, 2012-12-06 A compact guide to knowledge management, this book makes the subject accessible without oversimplifying it. Organizational issues like strategy and culture are discussed in the context of typical knowledge management processes. The focus is always on pointing out all the issues that need to be taken into account in order to make knowledge management a success. The book then goes on to explore the role of information technology as an enabler of knowledge management relating various technologies to the knowledge management processes, showing the reader what can, and what cannot, be achieved through technology. Throughout the book, references to lessons learned from past projects underline the arguments. Managers will find this book a valuable guide for implementing their own initiatives, while researchers and system designers will find plenty of ideas for future work. |
| ai and knowledge management: Knowledge Management, Innovation, and Entrepreneurship in a Changing World Jennex, Murray Eugene, 2020-03-27 In today’s world of business, gaining an advantage of competitors is a focal point for organizations and a driving force in the economy. New practices are being studied and implemented constantly by rivaling companies. Many industries have begun putting emphasis on intensive knowledge practices, with the belief that implementing cutting-edge learning practices will fuel research and innovation within the company. Understanding this dynamic method of management is critical for managers and executives who wish to propel the success of their organizations. Knowledge Management, Innovation, and Entrepreneurship in a Changing World is a collection of pioneering research on the methods of gaining organizational advantages based on knowledge innovation and management. While highlighting topics including human-robot teaming, organizational learning, and e-collaboration, this book will explore the sustainable links between knowledge management influences and organizational capability. This book is ideally designed for managers, strategists, economists, policymakers, entrepreneurs, business professionals, researchers, students, and academics seeking research on recent trends in innovative economics and business technologies. |
| ai and knowledge management: Using AI for Knowledge Management and Business Process Reengineering Rose Gamble, 1998 |
| ai and knowledge management: Artificial Intelligence for Knowledge Management Eunika Mercier-Laurent, Danielle Boulanger, 2019 This book features a selection of extended papers presented at the 5th IFIP WG 12.6 International Workshop on Artificial Intelligence for Knowledge Management, AI4KM 2017, held in Melbourne, VIC, Australia, in August 2017, in the framework of the International Joint Conference on Artificial Intelligence, IJCAI 2017. The 11 revised and extended papers were carefully reviewed and selected for inclusion in this volume. They present new research and innovative aspects in the field of knowledge management such as machine learning, knowledge models, KM and Web, knowledge capturing and learning, and KM and AI intersections. |
| ai and knowledge management: Knowledge Management in Practice Anthony J. Rhem, 2016-08-19 This evidence-based book provides the framework and guidelines that professionals need for working with the contemporary explosion of data that is creating opportunities and challenges to all phases of our society and commerce. –Larry R. Medsker, Research Professor in Physics and Data Science, The George Washington University Knowledge Management in Practice is a resource on how knowledge management (KM) is implemented. It provides specific KM methods, tips, techniques, and best practices to gain competitive advantage and the most from investing in KM. It examines how KM is leveraged by first responders, the military, healthcare providers, insurance and financial services companies, legal firms, human resources departments, merger and acquisition (M&A) firms, and research institutions. Essential KM concepts are explored not only from a foundational perspective but also from a practical application. These concepts include capturing and codifying tacit and explicit knowledge, KM methods, information architecture, search, KM and social media, KM and Big Data, and the adoption of KM. Readers can visit the book’s companion website, KM Mentor (www.KMMentor.com), where they can access: Presentations by industry leaders on a variety of topics KM templates and instruction on executing KM strategy, performing knowledge transfer, and KM assessments and audits KM program and project implementation guidance Insights and reviews on KM tools Guidance on implementing and executing various KM Methods Specialized KM publications A private secure collaboration community for members to discuss ideas and get expert answers and advice |
| ai and knowledge management: Big Data and Knowledge Sharing in Virtual Organizations Gyamfi, Albert, Williams, Idongesit, 2019-01-25 Knowledge in its pure state is tacit in nature—difficult to formalize and communicate—but can be converted into codified form and shared through both social interactions and the use of IT-based applications and systems. Even though there seems to be considerable synergies between the resulting huge data and the convertible knowledge, there is still a debate on how the increasing amount of data captured by corporations could improve decision making and foster innovation through effective knowledge-sharing practices. Big Data and Knowledge Sharing in Virtual Organizations provides innovative insights into the influence of big data analytics and artificial intelligence and the tools, methods, and techniques for knowledge-sharing processes in virtual organizations. The content within this publication examines cloud computing, machine learning, and knowledge sharing. It is designed for government officials and organizations, policymakers, academicians, researchers, technology developers, and students. |
| ai and knowledge management: Knowledge Management and AI in Society 5.0 Manlio Del Giudice, Veronica Scuotto, Armando Papa, 2023 Society 5.0 points toward a human-centred approach by the use of modern, advanced technologies and artificial intelligence. This book explores and offers an overview of knowledge management embraced in the current scenario of Society 5.0, shedding light on its importance in a society that is increasingly digital and interconnected. The book enhances current managerial and economic research by offering the human side of knowledge management (KM) intertwined with the use of artificial intelligences (AIs). Each chapter explores KM from different perspectives, including entrepreneurship, innovation, marketing, and strategy, in a theoretical and practical way. They include insights from both practitioners and scholars, enriched by practical tools that can be used during laboratories, workshops and tutorials. The book presents evidence on how to manage KM and develop new knowledge in different subjects, with the aim of overcoming conventional KM strategy and show how business and society are connected with power of subjective human knowledge creation. Offering both new insights, research and practical guidance, this book will appeal to academics and students of knowledge management as well as digital transformation practitioners looking for ways to transition their organizations from knowledge economy to digital economy-- |
| ai and knowledge management: UML for Developing Knowledge Management Systems Anthony J. Rhem, 2005-11-21 UML for Developing Knowledge Management Systems provides knowledge engineers the framework in which to identify types of knowledge and where this knowledge exists in an organization. It also shows ways in which to use a standard recognized notation to capture, or model, knowledge to be used in a knowledge management system (KMS). This volume |
| ai and knowledge management: Semantic Systems. The Power of AI and Knowledge Graphs Maribel Acosta, Philippe Cudré-Mauroux, Maria Maleshkova, Tassilo Pellegrini, Harald Sack, York Sure-Vetter, 2019-11-04 This open access book constitutes the refereed proceedings of the 15th International Conference on Semantic Systems, SEMANTiCS 2019, held in Karlsruhe, Germany, in September 2019. The 20 full papers and 8 short papers presented in this volume were carefully reviewed and selected from 88 submissions. They cover topics such as: web semantics and linked (open) data; machine learning and deep learning techniques; semantic information management and knowledge integration; terminology, thesaurus and ontology management; data mining and knowledge discovery; semantics in blockchain and distributed ledger technologies. |
| ai and knowledge management: The AI Advantage Thomas H. Davenport, 2019-08-06 Cutting through the hype, a practical guide to using artificial intelligence for business benefits and competitive advantage. In The AI Advantage, Thomas Davenport offers a guide to using artificial intelligence in business. He describes what technologies are available and how companies can use them for business benefits and competitive advantage. He cuts through the hype of the AI craze—remember when it seemed plausible that IBM's Watson could cure cancer?—to explain how businesses can put artificial intelligence to work now, in the real world. His key recommendation: don't go for the “moonshot” (curing cancer, or synthesizing all investment knowledge); look for the “low-hanging fruit” to make your company more efficient. Davenport explains that the business value AI offers is solid rather than sexy or splashy. AI will improve products and processes and make decisions better informed—important but largely invisible tasks. AI technologies won't replace human workers but augment their capabilities, with smart machines to work alongside smart people. AI can automate structured and repetitive work; provide extensive analysis of data through machine learning (“analytics on steroids”), and engage with customers and employees via chatbots and intelligent agents. Companies should experiment with these technologies and develop their own expertise. Davenport describes the major AI technologies and explains how they are being used, reports on the AI work done by large commercial enterprises like Amazon and Google, and outlines strategies and steps to becoming a cognitive corporation. This book provides an invaluable guide to the real-world future of business AI. A book in the Management on the Cutting Edge series, published in cooperation with MIT Sloan Management Review. |
| ai and knowledge management: AI-Empowered Knowledge Management in Education Sayan Chakraborty, Bitan Misra, Nilanjan Dey, 2024-06-22 This book explains basic ideas behind several methods used in artificial intelligence-based knowledge management techniques. It also shows how these techniques are applied in practical contexts in different education sectors. The book discusses AI-based knowledge management applications, AI-empowered knowledge management in primary and higher education, and technical and ethical challenges and opportunities. |
| ai and knowledge management: Knowledge Management and Artificial Intelligence for Growth Isaias Bianchi, |
| ai and knowledge management: Artificial Intelligence in Management Andrzej Wodecki, 2020-11-27 Autonomous systems are on the frontiers of Artificial Intelligence (AI) research, and they are slowly finding their business applications. Driven mostly by Reinforcement Learning (RL) methods (one of the most difficult, but also the most promising modern AI algorithms), autonomous systems help create self-learning and self-optimising systems, ranging from simple game-playing agents to robots able to efficiently act in completely new environments. Based on in-depth study of more than 100 projects, Andrzej Wodecki explores RL as a key component of modern digital technologies, its real-life applications to activities in a value chain and the ways in which it impacts different industries. |
| ai and knowledge management: Knowledge Management and Virtual Organizations Yogesh Malhotra, 2000-01-01 Annotation Twenty essays present current research on knowledge management as related to effective design of new organization forms. The first section of the book covers frameworks, models, analyses, case studies and research on the integration of knowledge management within virtual organizations, virtual teams and virtual communities of practice. Themes covered in this section include business model innovation; design of virtual organization forms; net-based models; techniques for enabling knowledge capture, sharing and transfer; and collaboration and competition at intra- and inter-organizational levels. The focus of the second half is on key success factors that are important for realizing virtual models of business transformation. Topics include the role of organizational control systems, the role of internal and external employees and customers in creation of organizational knowledge, and information quality issues. Annotation c. Book News, Inc., Portland, OR (booknews.com). |
| ai and knowledge management: Artificial Intelligence in Society OECD, 2019-06-11 The artificial intelligence (AI) landscape has evolved significantly from 1950 when Alan Turing first posed the question of whether machines can think. Today, AI is transforming societies and economies. It promises to generate productivity gains, improve well-being and help address global challenges, such as climate change, resource scarcity and health crises. |
| ai and knowledge management: Concepts and Advances in Information Knowledge Management Kelvin Joseph Bwalya, Nathan Mwakoshi Mnjama, Peter Mazebe II Mothataesi Sebina, 2014-04-11 Effective information and knowledge resource management is a driver of competiveness. Many developing countries have put mechanisms in place that seek to match knowledge-based economies, where information has become the fuel for responsiveness, innovation, and competition. Concepts and Advances in Information Knowledge Management brings out emerging and current discussion from the sub-fields of information management in this environment. This title consists of sections on key aspects of information knowledge management and addresses knowledge management, library studies, archives and records management, and information systems. - Presents research aimed at harmonizing theory and practice of general information management paradigms - Gives insight into the place of archives, records management, and information technology impacting socio-economic value chains - Disseminates theoretical and applied models, and information management system architecture emerging from cloud computing and retrieval systems |
| ai and knowledge management: Knowledge Management and AI in Society 5.0 Manlio Del Giudice, Veronica Scuotto, Armando Papa, 2023-03-10 Society 5.0 points toward a human-centred approach by the use of modern, advanced technologies and artificial intelligence. This book explores and offers an overview of knowledge management embraced in the current scenario of Society 5.0, shedding light on its importance in a society that is increasingly digital and interconnected. The book enhances current managerial and economic research by offering the “human” side of knowledge management (KM) intertwined with the use of artificial intelligences (AIs). Each chapter explores KM from different perspectives, including entrepreneurship, innovation, marketing, and strategy, in a theoretical and practical way. They include insights from both practitioners and scholars, enriched by practical tools that can be used during laboratories, workshops and tutorials. The book presents evidence on how to manage KM and develop new knowledge in different subjects, with the aim of overcoming conventional KM strategy and show how business and society are connected with “power of subjective human knowledge creation”. Offering both new insights, research and practical guidance, this book will appeal to academics and students of knowledge management as well as digital transformation practitioners looking for ways to transition their organizations from knowledge economy to digital economy. |
| ai and knowledge management: Knowledge Management Jay Liebowitz, 2001-03-28 Knowledge Management (KM) is strongly rooted in the discipline of Knowledge Engineering (KE), which in turn grew partly out of the artificial intelligence field. Despite their close relationship, however, many KM specialists have failed to fully recognize the synergy or acknowledge the power that KE methodologies, techniques, and tools hold for enh |
| ai and knowledge management: Working Knowledge Thomas H. Davenport, Laurence Prusak, 2000-04-26 This influential book establishes the enduring vocabulary and concepts in the burgeoning field of knowledge management. It serves as the hands-on resource of choice for companies that recognize knowledge as the only sustainable source of competitive advantage going forward. Drawing from their work with more than thirty knowledge-rich firms, Davenport and Prusak--experienced consultants with a track record of success--examine how all types of companies can effectively understand, analyze, measure, and manage their intellectual assets, turning corporate wisdom into market value. They categorize knowledge work into four sequential activities--accessing, generating, embedding, and transferring--and look at the key skills, techniques, and processes of each. While they present a practical approach to cataloging and storing knowledge so that employees can easily leverage it throughout the firm, the authors caution readers on the limits of communications and information technology in managing intellectual capital. |
| ai and knowledge management: Successes and Failures of Knowledge Management Jay Liebowitz, 2016-06-17 Successes and Failures of Knowledge Management highlights examples from across multiple industries, demonstrating where the practice has been implemented well—and not so well—so others can learn from these cases during their knowledge management journey. Knowledge management deals with how best to leverage knowledge both internally and externally in organizations to improve decision-making and facilitate knowledge capture and sharing. It is a critical part of an organization's fabric, and can be used to increase innovation, improve organizational internal and external effectiveness, build the institutional memory, and enhance organizational agility. Starting by establishing KM processes, measures, and metrics, the book highlights ways to be successful in knowledge management institutionalization through learning from sample mistakes and successes. Whether an organization is already implementing KM or has been reluctant to do so, the ideas presented will stimulate the application of knowledge management as part of a human capital strategy in any organization. - Provides keen insights for knowledge management practitioners and educators - Conveys KM lessons learned through both successes and failures - Includes straightforward, jargon-free case studies and research developed by the leading KM researchers and practitioners across industries |
| ai and knowledge management: Management by Design Daniel W. Rasmus, 2010-10-01 A revealing look at work environments that lead to greater loyalty and an increase in productivity Exploring the premise that the best way to attract and retain people, and their knowledge, will come from designing environments that turn today's increasingly virtual workplace into an attractive place for people to spend their time, Management by Design: Applying Design Principles to the Work Experience shows how the principles of design can be successfully applies to the work experience, making it a rewarding and productive. Reveals why the application of design to the workplace experience can improve the employee/employer relationship Why increased morale and employee loyalty start with a great work environment Explains why it is more important than ever to manage work experiences, especially with the projected work shortages in the coming decades Other titles by Rasmus: Listening to the Future: Why It's Everybody's Business This innovative book helps managers and executives connect the dots between employee retention, positive brand expression, and lasting stories that reflect well on an organization. |
OpenAI
May 21, 2025 · ChatGPT for business just got better—with connectors to internal tools, MCP support, record mode & SSO to Team, and flexible pricing for Enterprise. We believe our research will eventually lead to artificial general intelligence, a system that can solve human-level problems. Building safe …
What is AI - DeepAI
What is AI, and how does it enable machines to perform tasks requiring human intelligence, like speech recognition and decision-making? AI learns and adapts through new data, integrating into daily life via virtual assistants, recommendation algorithms, and self-driving cars.
Artificial intelligence - Wikipedia
Artificial intelligence (AI) is the capability of computational systems to perform tasks typically associated with human intelligence, such as learning, reasoning, problem-solving, perception, and decision-making.
ISO - What is artificial intelligence (AI)?
AI spans a wide spectrum of capabilities, but essentially, it falls into two broad categories: weak AI and strong AI. Weak AI, often referred to as artificial narrow intelligence (ANI) or narrow AI, refers to systems designed to excel at specific tasks within well-defined parameters. These systems operate …
Artificial intelligence (AI) | Definition, Examples, Types ...
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OpenAI
May 21, 2025 · ChatGPT for business just got better—with connectors to internal tools, MCP support, record mode & SSO to Team, and flexible pricing for Enterprise. We believe our …
What is AI - DeepAI
What is AI, and how does it enable machines to perform tasks requiring human intelligence, like speech recognition and decision-making? AI learns and adapts through new data, integrating …
Artificial intelligence - Wikipedia
Artificial intelligence (AI) is the capability of computational systems to perform tasks typically associated with human intelligence, such as learning, reasoning, problem-solving, perception, …
ISO - What is artificial intelligence (AI)?
AI spans a wide spectrum of capabilities, but essentially, it falls into two broad categories: weak AI and strong AI. Weak AI, often referred to as artificial narrow intelligence (ANI) or narrow AI, …
Artificial intelligence (AI) | Definition, Examples, Types ...
4 days ago · Artificial intelligence is the ability of a computer or computer-controlled robot to perform tasks that are commonly associated with the intellectual processes characteristic of …
Google AI - How we're making AI helpful for everyone
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Machine learning and generative AI: What are they good for in ...
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