The human capability to under-or overestimate
Yet maybe the most glaring example of underestimating humans we encounter in our work is in the world of AI. It's partly the term "intelligence" in AI that misleads so many, as AI is not intelligent in the same way that humans are intelligent. Though powerful, AI ultimately matches patterns it has learned, and even the smartest of AI systems is limited in how many patterns it can match and make sense of.
To hyperautomate or not to hyperautomate?
The logic behind hyperautomation is clear: Automate everything that can be automated. The practicalities of that are far less clear.
Finding the weakest link
Though traditional and often reluctant to change, the supply chain sector is now reassessing its lack of embrace of technology and, significantly, rethinking long-established processes.
The Law and AI
AI is very good, and light years ahead of where it was just a decade ago, but it is far from "intelligent." Indeed, it is only as good as the data it is provided and needs close human supervision.
Blockchains eliminate the need to trust other people. That's it; that is all there is to it. Trust is deferred to the system itself.
Getting to the future of KM
AI can and does do a good job of assisting and even augmenting knowledge work, but our "to be" state should not take the human element—however flawed—from the work.
From robots to digital workers
As more firms use the term "digital workers" in place of bots, a spotlight is being shone on the role, importance, and increasing controversy surrounding enterprise automation.
The big opportunity for knowledge management
It may well be stating the obvious but we will not be returning to the old ways of working, even though some of us, myself included (as it turns out, I am in the minority), would like to.
Can AI be ethical?
Without inherent bias in the data, AI would not make decisions. Bizarre though it may seem, AI is dependent on bias being present.
How we innovate matters
Just as nobody was fooled by the arguments used to justify offshoring and outsourcing business processes, they should also not be misled by the furious energy behind automation, be it in the form of RPA or even AI.
Reframing the KM discussion
The tech sector is growing fast, but without thorough business analysis, insight, proper planning, and a focus on challenging the better-quicker-cheaper approach and replacing it with a beneficial-adaptable-affordable commitment, there is a world of trouble ahead.
The rise of machine teaching
In contrast to some jobs that can indeed be automated and removed from the human payroll, KM practitioners have the potential to see their skills in much higher demand and volume in the future.
Decentralized knowledge management
Decentralization, though a boon to technology vendors, poses a unique set of challenges and risks for information and knowledge managers to grapple with.
Thinking about KM differently
Moving to a push rather than a pull mentality simply means that we now have the technology to tag, manage, and interpret information automatically and near instantly—automatically pushing the right information to the right person (or application) at the right time.
Thinking beyond the status quo
The technologies exist today to achieve almost any corporate or departmental goal. What is lacking is the nerve to think big and think beyond the status quo—to break barriers, to collaborate, and to share.
The right time for knowledge management
A new generation is coming in—one that sees order in the chaos, spots previously invisible patterns, and not only embraces technology but grew up with it.