Will Knowledge Management survive and thrive to see the artificial future it so craves? Read this article to find out why you need to focus on today's challenges instead of waiting for an artificial future.
The world of Knowledge management is awash with hope for an artificial future. However, before you get too carried away by the promises of Artificial Intelligence and Machine Learning, you might want to focus on the challenges of the present.
Many in KM are pointing to AI gains and strains in the field of content writing - for example, see this interesting article from Real KM. However, the discussion tends to focus on the ability to write content, missing the fact that the value of content is twice removed, where value is perceived and transmitted by the knowledge holder and only realised when it is received and moved to meaningful action by the receiver.
The ability of AI to extract, codify and transmit knowledge appeals to KM functions driven by the belief that knowledge is a technology problem in search of a technology solution. However, the world of the all-knowing AI knowledge solution is nowhere near being realised and here is why.
First, knowledge does not exist in a single, accessible knowledge domain. Enterprise knowledge exists in three domains: simple, complicated and complex - explained in this 60-second video.
Traditional technology-led KM programmes tend to focus on points of convergence: agreed-upon facts or 'best' answers found in the simple enterprise knowledge domain. Technology-driven KM solutions, such as Q&A or FAQ portals, perform well in this area where 'facts' are widely known, people can predict outcomes, and moderators can assure quality.
However, technology-driven KM stresses - and fails - when it experiences divergence, a deviation toward better or worse answers in the complicated knowledge domain. Think of it as a branching decision, where the level of complicatedness increases in line with the number of decision points.
Today's technology-led KM programmes more-often-than-not stress and fail in this complicated domain. Why? Because it becomes difficult to understand what brings a given person in a given context to take a given decision. In other words, what is the better decision, and is the meaning and value of what makes it a better decision embedded in the beliefs, attitudes, skills, knowledge, experience and talent of that given individual? Back to the challenge of knowledge, meaning and value being twice removed.
To better understand the challenges of meaning and value, consider these two questions:
What is 100% of what you know, and what beliefs, attitudes, skills, knowledge, experience and talent informed the way you tackled your last messy challenge or opportunity?
How much of what you know is shrouded in automaticity - actions that you are not consciously aware of - linked to the expert blind spot effect?
Artificial Intelligence will continue to evolve. I do not doubt that technology will continue to impact the way we go to work, as it has done throughout history. However, Knowledge Management must not use the dreams of the future to avoid today's reality.
In today's reality, KM is still not doing a good job of demonstrating its ability to move individual, team and organisational knowledge to action - actions that create meaningful value. If you want to endure, to thrive over time, then you must first address the challenge to create meaning and value in the present.
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