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.

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.

Let’s Get Real About the Impact of AI on Jobs

History tells us that industrial revolutions ultimately do create more jobs, but that the transition period is long and highly turbulent. Thus, when we see resistance in the workplace to AI and automation in general, we should acknowledge that the resistance and fear are well-grounded.

The Dethroning of Deduction

AI's favoring of induction over deduction is the root of its power, for it lets it deal with the specifics that bedevil the application of broad major premises.

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 ...

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.

The end of tech glory days

The tech industry's glory days may be fading a little, but this is not a time for despair. It's an opportunity for renewal. By shifting to a needs-driven approach, the industry can ensure its relevance in a rapidly changing landscape.

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.

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.

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.

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.

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.

AI technologies upending traditional KM

If we are not careful and proactive about it, the concept and importance of knowledge itself may soon become blurred or lost.

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.