Generative AI: Redefining How We Manage What We Know

Generative AI is stepping into the spotlight, promising to reshape how we manage knowledge. It's not just a buzzword anymore. This technology has begun addressing the shortcomings of traditional knowledge management systems and is already showing results in the real world. But the real question is, are we ready to fully embrace its potential?

Beyond Explicit Knowledge

Most of the conversation around generative AI and knowledge management focuses on explicit knowledge. That's the stuff we can write down or codify easily. But what about tacit knowledge? You know, the kind of knowledge that's harder to articulate, like riding a bike or knowing just the right way to handle a tricky situation at work. Generative AI is making strides here too, though the efforts have been sporadic.

Enter the "GenAI SECI" model, a fresh take on a classic framework. This model aims to integrate both explicit and tacit knowledge using AI. It introduces the intriguing concept of "Digital Fragmented Knowledge," a blend of these knowledge types, all living in digital spaces. This isn't just a tweak, it's a transformation.

The New Digital Knowledge Landscape

So, what's actually new here? The model outlines a system architecture that offers a structured approach to handling knowledge. It's not just theory anymore. It's a practical framework that can be implemented and tested against the backdrop of how we currently handle information.

But who benefits? And is this shift just about technology, or is it really about power dynamics? As we integrate AI into knowledge management, the benchmark doesn't capture what matters most. We need to ask whose data is being used, whose labor is involved in training these models, and ultimately, who stands to gain the most.

The Road Ahead

The introduction of this model comes at a key time. Research in this space is growing rapidly. According to recent studies, the integration of AI in knowledge management systems can lead to more efficient and inclusive environments. But let's not kid ourselves, it's not all sunshine and rainbows. There are ethical concerns and potential downstream harms we need to address.

Incorporating generative AI into how we manage knowledge is more than just a technical upgrade. It's an opportunity to rethink what we value in knowledge and who controls it. So, are we going to let AI drive the conversation, or will we steer it toward greater equity and representation?