Looking to the Past to Co-Create the Future

As more ancient texts become digitized and translated, let's go all-in by using human-augmented AI, combining ancient knowledge artifacts with our modern body of research. And let's not just be confined to one or two disciplines. Infinitely large numbers of breakthrough innovations even more impactful than the examples shared are possible.

The Deterministic Delusion: Why Agentic AI Fails the Rules-Based Reality of KM

The real error of the expert system era was not determinism itself—it was incomplete rules. Today's risk is the opposite: We have agents that are too flexible, running on too little accountability, deployed into environments where variation is not a feature but a liability.

Why Knowledge Management Needs a Quantum Reboot for the Agentic AI Age

By embracing a quantum approach, we can create an organization that is genuinely adaptive and intelligent. Agents, freed from the shackles of classical KM, can roam our knowledge graphs, identifying emergent patterns and unexpected connections that no human ever could. They can see that the support ticket trend and the new feature request in the sales call are actually the same particle, just observed in different contexts.

A Glorious Victory for KM!

That AI has proven itself to be a revolutionary knowledge tool paints a different picture of the world itself. For millennia, we in the West counted as the highest knowledge the bedrock beliefs that ground the certainty of the layers of lesser knowledge that rest upon them. While the success of our culture proves the value of this approach in some critical areas, the rapid advances in knowledge enabled by machine learning based in multidimensional models that are too complex for us to understand remind us of what we've always already known: Our world overwhelms our smidgeon of consciousness.

Humans in Loops, Flows, and Dialogues

I think we are entering—possibly are already in—the era of humans in the dialogue with AI, discovering our values, getting more specific about them, and altering their applications based on the specifics of our world and situation. If the old KM was about building, organizing, sharing, and leveraging knowledge, the new KM might also be about mastering the dialogue: using AI not just to retrieve our answers, but to help us finally articulate the right questions.

A Call to Arms for Information Professionals

The AI world is advancing at a breathtaking pace with staggering sums of money, but it's built on unstable and illusory foundations. Our role is not to stand on the sidelines shouting warnings. It is to quietly, strategically, and indispensably become the people they cannot do without—the ones who ensure the entire system can actually function.

Forget AI Magic, Embrace the Knowledge Graph

The advances in AI and information management are not our enemies; they are our most powerful allies. When wielded by skilled KM professionals, these technologies work. When deployed without our input, they fail miserably, delivering incorrect, misleading, or plain nonsensical results.

Is Your Agentic AI Built on Sand or Bedrock?

Data and knowledge do not, anymore, exist as separate components. They are rapidly merging into a single architecture. As KM'ers, we can no longer leave data management solely up to the admins. Rather, we need to work closely with them on creating data architectures that are contextually and semantically rich enough to be reliably actionable for use by autonomous and semi-autonomous agents.

The Productivity Paradox: Why Your AI Investment Won’t Pay Off Without KM

There should be one clear group of winners emerging from the coming disillusionment: knowledge and information managers. The AI reckoning will force a long-overdue epiphany upon executive leadership: The value of technology is not inherent; it is contingent on the quality of the information fuel you feed it.

Me and Mr. Tibbs

Enter Mr. Tibbs, the personal AI agent I imagine having in a year or so. If Mr. Tibbs went through that filing cabinet, it would learn plenty. Of course, I'm imagining Mr. Tibbs version 4.0, which is not only smarter, but also magically has the physical mechanisms required to go through a stack of folders.

What Problem Is AI Actually Trying to Solve?

Too often, AI is deployed reactively—thrown at symptoms rather than root causes—leading to wasted resources, disillusionment, and even deeper inefficiencies.

The Long- and Short-Term Impacts of AI Technologies

A much less-known but arguably more critical tech law is Amara's Law, which states that we tend to overestimate the short-term impact of new technology while underestimating its long-term effects.

On Chat AI and BS

So, I'm sticking with hallucinations for all of chat AI's statements, true or false. But that leaves us with a question: Why isn't there a word that perfectly expresses this situation? The answer is easy: LLMs are doing something genuinely new in our history. Our lack of a perfectly apt verb proves it.

Inefficient at the speed of light

While process mining started years ago as a mainly data-driven exercise, its stated goal is to be knowledge-driven. Given KM's multidisciplinary scope, we can play a major role in achieving that goal. Any process, no matter how simple, has the potential to reach across an entire business ecosystem, including all stakeholders. This seems like a perfect match for collaborative workflow, AI/ML, knowledge graphs, human sensemaking, and many of the other arrows in our KM quiver.

Pushing the boundaries of knowledge curation

Knowledge democratization occurs in two directions, seemingly engaged in an endless tug of war: acquisition and dissemination.

The flip side of generative AI: Extractive AI

Extractive AI takes a more comprehensive and transparent approach to machine intelligence.

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.

The five ages of data

Perhaps this latest phase in the history of data will bring us to accept inexplicable complexity as a property of the world. We could view this as pure chaos, but thanks to having lived through the past four ages in rapid succession, we might instead recognize that chaos as being rich with endless mysteries we will never uncover completely.

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.