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.

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.

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.

A System of Systems … With a Twist

Many long-standing technologies such as swarm intelligence, biomimicry, neural networks, and the like are now being stitched together. Think of what could happen if each of those technologies interacted not only with each other but also with the environment at large, its living and artificial elements, as an integrated whole.

Will AI Ever Play in Peoria? The Enterprise Reality Check

The tech industry has a long history of overpromising and underdelivering, but AI has taken this to new heights. We're bombarded daily with headlines about AI writing novels, diagnosing diseases, and even replacing entire job functions. Yet, when you peel back the layers, you find a landscape littered with half-baked implementations, inflated claims, and solutions that work only in the most controlled environments.

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.

252 Million Walas

There are 195 countries in the world. How many more entrepreneurial innovation hotspots are out there, waiting to be tapped and awakened? In our high-tech, virtual world, all of the steps Pakistan has taken can be replicated virtually anywhere, regardless of your country's size, GDP, or location. Imagine the possibilities ...

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.

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.

The rise and potential fall of the citizen developer

The citizen developer movement was heralded as a revolution. Like most revolutions, things have sometimes gone differently than planned. The logic is sound, empowering those who know the business best to build the tools and systems needed to do their job. Ah, if only things were that simple …

What is Bharat and why should you care?

Knowledge should always be considered as accretive, not something that's "here today, gone tomorrow."

Pushing the boundaries of knowledge curation

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

The third place of knowledge management

The third place I alluded to goes far beyond mechanistic KM or curated knowledge and takes us into the actual world of tacit knowledge. Here, knowledge comes from and often remains as personal experience, impressions, and intuition; it's undocumented and often hidden and elusive.

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 flip side of generative AI: Extractive AI

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

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.

The trust problem with GenAI

2023 has been the year of ultra-hyping GenAI, and who is paying for this deluge of marketing? Technology vendors that want us to buy it. Again, it's impressive stuff, but when we shift from selling to buying and ultimately using it, many tough questions need to be asked.

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.