AI initiatives face challenges linked to the five V’s of big data- volume, variety, velocity, veracity, and value. As highlighted by Dr. Moria Levy, knowledge management mitigates these risks by adding context, governance, sensemaking, and alignment with business strategy- ensuring AI outputs are meaningful, trustworthy, and actionable. KMGN KM-AI course, session 16. Dr. Art Murray leads us through an enlightening session, focusing on AI challenges and risks and suggesting as

AI improves organizational knowledge when data, processes, and expertise are aligned for insight generation and decision-making. As emphasized by Dr. Moria Levy, knowledge managers add value through data cleansing, bias awareness, and business understanding- helping transform raw information into reliable, actionable knowledge that strengthens performance and strategic outcomes. KMGN- KM AI course, session 15. Today we met Dr. Tony Rhem, a thought leader in both KM and AI dis

Running a first AI pilot requires careful use-case selection, realistic expectations, and measurable success criteria. As emphasized by Dr. Moria Levy, successful pilots focus on strong data availability, management support, and partial-win value- reducing risk while enabling learning, iteration, and scalable adoption. KM AI course. June 15th. Session 11. Today we meet Nicole Talbot from Levatas and learn about running the first AI pilot project in an organization. What is so

The World Bank case study shows how combining AI and knowledge management enables rapid reuse of project insights. By integrating internal and external data with machine learning and expert curation, organizations can deliver relevant knowledge in minutes, embed learning in workflows, and significantly improve decision-making efficiency. May 18th, Session 7. Today we are hosting Margot Brow, Senior Advisor Knowledge Management, Office of the Chief Operating Officer, Internati

AI technologies include natural language processing, speech recognition, computer vision, analytics, and robotics that learn from data rather than explicit programming. By combining cognitive, sensory, and decision systems, organizations can automate complex tasks, enhance objectivity, and expand human capabilities- while requiring strong governance to ensure ethical, responsible use. April 27. 4th session of KM AI course. In many AI advanced solutions, intelligence is based

Explainable AI (XAI) refers to methods that make artificial intelligence decisions transparent and understandable to humans. By revealing how models use data and logic to generate outcomes, XAI builds trust, supports accountability, detects bias, and enables responsible use of AI in high-impact areas such as healthcare, law, and finance. Explainable Artificial Intelligence is a term that refers to AI technology implementation techniques and methods, that can be explained to h

The book aims to provide an overview of AI capabilities while inspiring individuals to incorporate AI into their organizations and advance t

Decision Intelligence combines data science, AI, and decision theory to transform raw data into automated, informed actions. By learning from large datasets and improving through iteration, it enables systems to identify patterns, predict outcomes, and support or automate complex decisions- enhancing operational efficiency, accuracy, and strategic agility across industries. The convergence of Data Science and the field of decision making led to the birth of a new approach to

Artificial Intelligence explains how organizations can apply AI responsibly to improve decision-making, automation, and innovation. It offers practical guidance on data, governance, and human-machine collaboration, helping leaders manage risks, address bias, and integrate AI into business processes for sustainable competitive advantage. The book " Artificial Intelligence " is a compilation of essays from the Harvard Business Review series, initially published in 2019. It enco

Chatbots are digital service agents that simulate human conversation through text or voice to handle customer inquiries. Service improvement relies on three modules: rule-based bots for structured FAQs, AI-driven bots using machine learning and NLP for adaptive responses, and hybrid models combining automation with human escalation- optimizing availability, personalization, and operational efficiency. Nowadays, many businesses tend to incorporate Chatbot components into their

The semantic network enables computers to interpret information through context, relationships, and ontologies, allowing machines to understand meaning rather than keywords. By linking data, concepts, and users, organizations can improve search, collaboration, learning, and decision-making- transforming scattered content into connected, actionable knowledge that supports smarter digital services and workflows. In 2001, Sir Tim Berners-lee (inventor of the World Wide Web) pres