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.

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.

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.

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.

Trees, chains, and brains

Today's AI has many different flavors and architectures, along with massive amounts of memory and processing capacity. We could probably make better use of this computational power by looking at how we can improve the quality of our queries and, as a result, make better quality decisions.

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.

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 …

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.

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.

Dispatches from the edge

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

Data is never just data

As with all tools, data has uses because of complex contexts that include other objects, physics, social norms, social institutions, and human intentions.

A limit to business intelligence?

The extent to which businesses protect their data assets is the extent to which business is limiting its own intelligence.