The Internet has fundamentally changed the way knowledge is controlled and shared.
Where once those who held knowledge, either in their own heads or through access to books, acted as gatekeepers, suddenly everyone could share their ideas and thinking; at least in countries that allowed them to.
This explosion of information has brought massive gains and a few key challenges. The gains are obvious. New insights and learning can be shared instantly, across the world. This helps accelerate global learning and social change. People can now build on each other's ideas from across the globe with access to expertise no longer a physical constraint.
The challenges are sometimes a little less obvious. One is differentiating between truth and fiction. Curatorship is also increasingly crowdsourced and with platforms such as Facebook and Google creating digital echo chambers, false narratives are amplified. Plus it can lead to a narrowing, rather than a broadening, of views.
Another challenge we face is the sheer volume of information out there. It's simply overwhelming.
The impact of AI
This complexity means traditional forms of content curation simply won't work anymore. Instead, it's rapidly becoming clear that artificial intelligence (AI) will step into the breach and perform the role of 'librarian' in the field of global knowledge management.
Not only can these technologies 'read' and 'understand' the written word, they can interpret other media formats such as video and sound. And with exponential learning capability, AI can consume enormous volumes of information in very short time frames.
AI's ability to interpret the content it consumes, and to learn, is turning it into a powerful digital expert.
AI does not worry that information is not properly structured and managed. It can also overcome poor syntax and different languages. And it can make sense of enormous volumes of content fairly quickly.
As a result, AI is becoming our global librarian, helping us locate the information we seek more accurately and easily.
But it's far more than that too. AI's ability to interpret the content it consumes, and to learn, is turning it into a powerful digital expert.
In fact, in scenarios where decisions and actions can be repeated based on the lessons learned, and documented, by others, AI beats humans every time. Digital intelligence can learn in seconds, from more sources, than any human could hope to do in a lifetime.
This poses some interesting questions around the future of information-sharing, especially in the corporate space.
Impact on corporate knowledge management
Any business hoping to survive in this new environment must stay on top of any changes in the field of knowledge management.
One of the biggest changes concerns the way knowledge and knowledge management are understood.
Traditionally, companies see 'knowledge' as a synonym for 'decision-making instructions'. The primary purpose of these instructions is to 'code' staff brains to perform prescribed actions in line with clear product, policy, procedure and system rules.
As a result, most corporates operate like dictatorships. Knowledge is carefully curated, and the purpose of knowledge management is to ensure the formula is accurately captured, and that staff have this formula available to them as and when they need it.
The problem with this model comes when the rules change. The learning and knowledge team scrambles to ensure staff members understand the new way of doing things and hope the old rules are deleted from their brains.
These efforts are seldom anywhere near as effective as we'd like them to be, but corporates stick with them, largely because they don't know what else to do.
As a result, staff are seldom equipped to make the right decisions and grow over time.
Fortunately, things are about to change.
Knowledge management 2.0
To liberate knowledge management within the corporate space and to focus efforts on unlocking and amplifying new thinking, rather than simply replicating old thinking, we need to liberate knowledge management from the primary role of logic capture and replication. We need to hand this role over to digital intelligence.
This frees staff from having to make robotic decisions and actions, and instead asks them to focus their efforts on coming up with new thinking and ideas.
To achieve this shift, companies need to take a leaf out of the Uber book, where drivers are first empowered by decision-making navigation (GPS) so they can get better at customer engagement. Over time, as self-driving cars (full automation) become the norm, these drivers will hopefully have learned new skills that allow them to transition to new roles.
Staff augmentation technologies are becoming smarter and easier to use. They allow companies, instead of coding non-networked human brains, to programme a single digital brain that replicates their decision-making formula in a consistent, compliant way. These technologies allow knowledge management teams to transform themselves into knowledge generation teams, with the primary focus of developing a learning organisation, not a decision-making replication engine.
From all the engagements I have with learning and knowledge management teams, this change can't come soon enough.
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