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.

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.

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.

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.

The coming blue wave

It should come as no surprise that topping the list of requirements to create and sustain a vibrant blue economy are innovation, learning, and collaboration.

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.

The future of food: a fresh look

There's a growing demand for the ability to facilitate the integration of knowledge generated by widely diverse communities from multiple disciplines.

Usability testing for effective interactivity

Connecting the seeker to the information she seeks is not a new problem. Interaction design has been a stumbling block since the age of the card catalog.