The flip side of generative AI: Extractive AI

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

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 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.

Look to the skies for KM opportunities

Then there's the inevitable demand for more automation, from the flight planning and clearance process to the operation of the air vehicles themselves. No human or group of humans could possibly keep track of so many constantly changing variables

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.

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.

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.

To hyperautomate or not to hyperautomate?

The logic behind hyperautomation is clear: Automate everything that can be automated. The practicalities of that are far less clear.

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.

Dispatches from the edge

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

Finding the weakest link

Though traditional and often reluctant to change, the supply chain sector is now reassessing its lack of embrace of technology and, significantly, rethinking long-established processes.

Fusion, fission, or something else?

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

Getting to the future of KM

AI can and does do a good job of assisting and even augmenting knowledge work, but our "to be" state should not take the human element—however flawed—from the work.

Thinking fast—and faster

If you're going to achieve consistent, effective high-speed decision making, it can't involve a protracted review by upper management.

From robots to digital workers

As more firms use the term "digital workers" in place of bots, a spotlight is being shone on the role, importance, and increasing controversy surrounding enterprise automation.

The way of the scenario

The Delphi technique has become less effective in recent years, especially in crisis situations in which conditions, assumptions, and other variables are changing faster than the group is able to respond.

Making the jump to hyperdrive

The new, all-digital workforce will be made from a combination of AI, machine learning, computer vision, naturallanguage understanding, robotics, and more.

The big opportunity for knowledge management

It may well be stating the obvious but we will not be returning to the old ways of working, even though some of us, myself included (as it turns out, I am in the minority), would like to.