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For years, many organizations viewed Knowledge Management as a support function. Important, yes, but often underestimated. KM teams were asked to maintain repositories, improve intranets, organize content, support lessons learned programs, and encourage knowledge sharing across the business. That model is changing fast. The rise of Artificial Intelligence is forcing organizations to rethink how knowledge … Read more
Organizations are moving fast with AI. New tools are being deployed, copilots are integrated into workflows, and leadership expects measurable business impact. Yet in many cases, the results feel underwhelming. Outputs are inconsistent, adoption is uneven, and trust remains fragile. The underlying issue is not the AI itself. It is the absence of a strong … Read more
Most knowledge managers didn’t get into this field to become AI experts. They got into it because they care about how organizations learn, how expertise flows, and how knowledge gets preserved and used rather than lost. Now, barely a conversation about KM strategy goes by without large language models entering the room, sometimes as a … Read more
Many organizations are investing heavily in AI, expecting immediate gains in productivity, decision-making, and automation. Yet the results often fall short. The issue is rarely the algorithm. It is the knowledge behind it. Knowledge management for AI is the real differentiator between systems that deliver value and those that produce unreliable, inconsistent outputs. AI does … Read more
Knowledge capture has been the stubborn problem at the center of knowledge management for decades. Organizations invest heavily in frameworks, workshops, and KM platforms, and then watch critical knowledge disappear when a senior engineer retires, a project team disbands, or an expert moves on. The process of getting knowledge out of people’s heads and into … Read more