The evolution of the KM technology stack
Historically, KM managers have tried to centralize knowledge assets into a single KM platform and curate within it. But outside of a few niche use cases, this has not been feasible for many years. Combining few KM human resources and an increasing data deluge makes it impractical. That is not to say we don’t have the tools and resources to manage knowledge assets effectively; rather, we need to recognize corporate realities, be open to innovation, and embrace radical change.
1. Corporate realities It’s improbable that any organization will soon sign off on the budget for, and hire, an army of skilled knowledge managers. Finding such an army would prove challenging even if there were a budget. What’s more, information and knowledge assets (in the form of hard data) live within many applications and silos across organizations. Many of these assets will be in file servers, email platforms, cloud file-sharing and document management systems, business applications—the list goes on. That fragmented and disparate data landscape is also unlikely to change anytime soon.
2. Innovation and radical change Given those corporate realities, we must first accept that human resources for KM-related work will always be at a premium and that assets will live anywhere and everywhere. We must leverage AI but balance it soundly with skilled human input. Frankly, I can’t see any other option. The attractive and exciting thing, in my opinion, is that this is soundly doable. There is undoubtedly some dark magic within today’s AI, but thankfully in the world of KM, AI is more controllable and manageable than many seem to realize. For example, knowledge graphs that visualize and surface connections between data, people, and information elements are the KM professional’s new best friend. Not that the concept of knowledge graphs is new, but their value is next to nothing unless managed by skilled KM professionals.
What about the KM technology stack?
What all this means is that the technology stack for KM can, in theory, be as simple as a dedicated KM or document management system. But, in practical terms, the technology stack for KM today, and also moving into the future, consists of any software application or data silo that holds valued data and information. But realistically, for KM professionals to make sense of and leverage all that data and information, they need help from AI, particularly within larger organizations. AI tools can help you move from simply searching and retrieving data to actually delivering accurate insights, corporate experience, and learnings. These emerging tools can move beyond the tech stack and empower the KM professional to retake control.