AI works best when humans are in the loop. Knowledge communities can provide a robust flow of information that supports and continuously refreshes the content on which AI relies. When organizational processes are identified and documented, AI can take over routine tasks, leaving the creative and more challenging problem-solving tasks to be handled by humans. Behind the scenes, content and product models need to be developed and aligned with data capture processes to make AI components work, but humans must create the knowledge flow and take charge of the content.
- Knowledge communities contribute to healthy knowledge flows, which in turn become an invaluable source of accurate, up-to-date information for knowledge bases that support organizational processes.
A knowledge community is essentially a community of practice—a place where experts can share approaches, nominate best practices, and submit exemplars of solutions or deliverables. When carefully planned and managed at enterprise scale, knowledge communities will always be a richer, deeper source of wisdom and expertise than a unit-level group. They can draw upon a more diverse set of information and enable more in-depth analyses.
Knowledge communities can help address several inherent challenges around knowledge capture/refactoring, curation, tagging and retrieval. For many organizations with very large volumes of content, purely manual approaches are costly and cannot keep up with the velocity of knowledge creation and downstream application and thus become cost prohibitive. Instead, a combination of AI/ machine learning, and human in the loop approaches is the only cost effective and sustainable option.
This is an excerpt of an article published on KMWorld.com on June 23, 2021. Read the full article here.