Opinion > Columns
Industry experts and KM leaders share their ideas about the state of Knowledge Management in the world today and where it is going.

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Was the web good for knowledge management?

So, yes, the web enables everyone with an internet connection and the freedom to use it to contribute to our new, global, contentious, and contradictory knowledge space. But I did not foresee the dark side because of an optimism born of privilege.

Should we go back to paper-based KM?

The sheer volume of largely useless data we have accumulated across the years severely limits the ability of AI to work well, and it comes at a heavy environmental and financial cost.

8 billion and counting

The message is clear: No single person or committee or group can weave the best paths through the infinite maze of possible event chains. Only humans and machines working together, side by side, can produce a better result than would ever be possible from either one alone.

When is good enough enough?

Our goal should be to improve the quality of knowledge assets and their accuracy and relevance in use. Much of this will come from human expertise and effort, increasingly combined with the power of AI.

Get your game on: KM skills needed for reliable use of LLMs

There is no questioning that generative AI is here to stay, but its use in mission-critical work has some way to go before it can be trusted and let loose.

Are you data-driven or knowledge-driven?

We no longer need to blindly accept the output of even the most sophisticated AI/ML platforms. In fact, we should not consider any artifact, whether produced by humans or machines, as valid knowledge unless it contains not only supporting data and analyses, including provenance, but also an explanation of the underlying plausibility.

What are your chatbot’s pronouns?

We don't have pronouns by which we can address inanimate objects because we haven't had any occasions to have actual conversations with them.

The ChatGPT ways of knowledge

These two types of knowing—understanding the world and understanding knowledge—are, in some important ways, at odds in AI-based chatbots.

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.

Tags, AI, and dimensions

Tags have become so common that they've faded from consciousness since 2007, although sometimes a clever hashtag pops up.

AI’s new type of knowledge

This way of knowing works pragmatically for some very complex systems of the sort we find in the real world. But, oddly, itseems not to work so well in some artificially simple systems.

Return on … Infrastructure???

As our physical and IT infrastructure continues to grow in size, complexity, and vulnerability, people and the knowledge they possess will play an ever-increasing role.

Three trends in ’23

The combined human and computing clouds will drive our core KM processes of search, collaboration, and discovery to new heights.

Knowledge as I remember it

The web transformed the role of knowledge by making it instantly available but not inherently reliable.

Getting more confused about regulating social media

Out of the mix of commercial greed, politics, and genuine desires to make the world better, we'll try many ways to "fix" social media. But I think it may take a couple of generations, affected by what we do, for us to begin to agree about what's right and wrong.

The undiscovered country

Capturing and sharing what you already know is good; and with today's data and text analytics tools, it has become much easier than when we'd first begun this journey.

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.

Dispatches from the edge

Edge-of-chaos decisioning means being continually informed on the critical elements needed to make better, faster decisions.

AI’s ways of being immoral

The most powerful ML can require the resources of wealthy organizations. Such organizations usually have at best mixed motivations, to be charitable about it.

Fusion, fission, or something else?

When it comes to applying KM, the key is identifying and connecting the dots in meaningful and synergistic ways.