Artificial intelligence
This long-running series of over 200 articles across ten years looks at artificial intelligence (AI) in relation to knowledge management (KM), and KM in relation to AI. A wide diversity of perspectives is presented, including opposing perspectives. See also the advances & challenges in foundation agents series.
1. Notable articles
The full series can be found further below. Notable articles from it include (most recent first):
Ethical and responsible AI in KM
- Why students struggle to use AI responsibly
- AI is changing the Dunning-Kruger Effect, with higher AI literacy correlating with overestimation of competence
- AI resources update: 1. PwC 2025 Responsible AI survey | 2. ISO/IEC 42001 guide for business | 3. Architectures of Global AI Governance
- Case studies in the unethical and irresponsible use of AI
- AI resources update: 1. AI Incident Database | 2. The Geopolitics of AI
- AI ‘workslop’ is creating unnecessary extra work. Here’s how we can stop it
- AI resources update: 1. AI Ethics and Governance in Practice Program | 2. Global AI Ethics and Governance Observatory
- Four dimensions and five building blocks for responsible AI in knowledge management
- Strategies and recommendations for managing ethical considerations in the use of AI in KM
- 95% of organizations are getting zero return on AI, while shadow AI thrives. But at what knowledge risk?
- Ignoring harm, saving face: strategies of non-knowledge and knowledge avoidance, and what they mean for KM
- Understanding the ‘Slopocene’: how the failures of AI can reveal its inner workings
- Is AI a con? A new book punctures the hype and proposes some ways to resist
- Are you really doing enough to detect “botshit” in your AI-generated content?
- Will an AI guardrails approach really work?
- Troubling case highlights that the KM community lacks key capabilities needed for safe AI use
- AI doesn’t really ‘learn’ – and knowing why will help you use it more responsibly
- The AI Risk Repository: A comprehensive database of risks from AI systems
- Two horror cases highlight the dangers of blind faith in what AI generates
- The risks associated with AI-generated content
Implementing AI in KM, and KM in AI
- The ‘doorman fallacy’: why careless adoption of AI backfires so easily
- The illusion of thinking: large reasoning models (LRMs) collapse in the face of complicated tasks
- What are the key challenges in implementing AI in KM, and how can they be addressed?
- The GRAI framework – extending the SECI model to reflect generative AI
- Digital humans as knowledge carriers: How AI avatars transform knowledge storage and transfer
- Integrating LLMs with local knowledge bases assists sustainability, but neglects tacit knowledge and complexity
- Knowledge managers bring collectivity, nostalgia, and selectivity to AI
- Fallacies and tyrannies to avoid in Gen-AI implementation
- AI-empowered knowledge management (KM) processes: what effects on decision-making?
- Integrating Global Standards for Human Knowledge and AI [MBRF Knowledge Summit Special Issue]
- The potential benefits of implementing the knowledge management and artificial intelligence ISO standards together
- Evolution of my concept mapping practice – what if it’s a building block in the AI revolution?
- Tom Davenport sees significant potential for AI in BPM. What does this mean for knowledge management?
- Using artificial intelligence (AI) algorithms for expert identification
- How artificial intelligence can support knowledge management in organizations
- Will AI make managing knowledge more or less important?
- Interested in AI to improve your manufacturers’ performance and resilience? KM processes are what you need!
- Artificial intelligence (AI) and knowledge management (KM): two sides of the same coin?
- How might generative artificial intelligence affect knowledge management theories?
- 70 use cases for AI today (2016)
2. Full series
Header image source: Gerd Altmann on Pixabay, Public Domain.
