Filling The Multi-Domain Knowledge Gap - How To Build The Missing Knowledge Needed By AI Agents and Humans

The Knowledge Management Method™ addresses the Multi-Domain Knowledge Gap

In a previous post, I pointed out that the missing fuel for AI Agents is accurate enterprise knowledge. In order for AI Agents to operate autonomously, they need specific end-to-end knowledge about how the enterprise actually operates. This is detailed knowledge about the interactions and data that continuously flow across platforms, people, organizations and suppliers. 

Unlike the spectacular success experienced by LLMs and other AI tools, Agentic AI will fail badly if AI Agents aren’t fed with the specific kind of enterprise knowledge they need to perform complex, multi-step tasks.

End-to-end enterprise knowledge is not just needed by AI Agents.  According to a McKinsey study (and our decades of experience with multi $B enterprise IT programs), people spend up to 20% of their time chasing the knowledge they need to do their day-to-day jobs. Many stakeholders and experts have clearly pointed out that the ultimate success of AI Agents rests on their ability to perform work in concert with humans. So both humans and AI Agents need this missing enterprise knowledge.  

Adding to the problem is the fact that end-to-end knowledge typically crosses multiple domains and not all such knowledge is needed in order for AI Agents and humans to work together. 

As I mentioned in a previous post, documentation should be able to address this gap. However, in most organizations that we’ve worked with, documentation is traditionally a much lower priority than getting functionality out the door. Where it does exist, documentation tends to be fragmented across organizations and typically focuses on system-level designs and code - not end-to-end business processes and architecture.

So how do you go about filling this multi-domain knowledge gap?

Simply put, what’s needed is a knowledge management methodology - a clearly articulated multi-domain approach and set of best practices to assemble the missing knowledge needed by AI Agents and humans - on a per use case basis. That’s why we at DomainStream developed our proprietary Knowledge Management Method™ (KMM).  

The Domainstream KMM and our multi-domain experts provide a step-by-step approach, with clearly defined techniques, actions, roles and templates. We work closely with all your stakeholders to build an enterprise knowledge base that satisfies both the cross-domain needs of AI Agents and provides a self-serve tool that people can use at any time to get the knowledge they need to do their jobs.

Contact us to find out more.