When IT Meets Knowledge Management: The Six Faces of GenAI Adoption

When organizations say they’re “implementing Generative AI,” they may be referring to very different things.

From a Knowledge Management perspective, we typically encounter six main categories, and in each, our role as KM professionals changes.

Enhanced Software Tools:

Familiar platforms we already use (document management, search engines, customer service systems, development tools) now come with AI-powered upgrades. Here, IT leads, and KM supports integration and purposeful use.

AI-Specific Tools:

New solutions like ChatGPT, Gemini, Claude, GAMA, Midjourney, or chatbot management systems built on organizational content.

Again, IT leads and KM ensures responsible and relevant application.

Automation Agents (RPA-like):

AI agents that automate repetitive tasks. IT drives efficiency; KM ensures that what’s being automated reflects accurate, current, and meaningful knowledge processes.

These first three categories are technology-driven, typically led by IT teams.

But in the next three, KM steps into the spotlight.

Data-Based AI Agents:

Agents trained on structured and unstructured information repositories to retrieve and provide answers. IT usually leads the technical setup, while KM ensures content quality, metadata accuracy, context alignment, and prompt optimization. The smarter the data, the smarter the agent.

AI Adoption & Learning:

Guiding employees and teams to use AI effectively in their day-to-day knowledge work. KM professionals, with their deep understanding of human, organizational, and learning dynamics, bridge people and machines — ensuring adoption with purpose, trust, and insight.

Knowledge-Based Intelligent Agents – The next level.

These agents don’t just retrieve information — they think with it. Built from expert insights and organizational logic, they represent how professionals analyze, reason, and decide. Developing them requires capturing true knowledge, structuring tacit expertise, and translating it into machine-understandable reasoning. This is where KM’s distinctive value becomes strategic.

As Knowledge Managers, we are not just supporting AI adoption, we are shaping it.

By structuring knowledge, connecting people and technology, and embedding intelligence into processes, we turn AI from a tool into a trusted partner in organizational knowledge.

P.S. And if you’d like to see how it actually works in practice, feel free to reach out.