The flip side of generative AI: Extractive AI

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

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

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.

The human capability to under-or overestimate

Yet maybe the most glaring example of underestimating humans we encounter in our work is in the world of AI. It's partly the term "intelligence" in AI that misleads so many, as AI is not intelligent in the same way that humans are intelligent. Though powerful, AI ultimately matches patterns it has learned, and even the smartest of AI systems is limited in how many patterns it can match and make sense of.

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.

Dispatches from the edge

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

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.

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.

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.

How we innovate matters

Just as nobody was fooled by the arguments used to justify offshoring and outsourcing business processes, they should also not be misled by the furious energy behind automation, be it in the form of RPA or even AI.

Thinking about KM differently

Moving to a push rather than a pull mentality simply means that we now have the technology to tag, manage, and interpret information automatically and near instantly—automatically pushing the right information to the right person (or application) at the right time.

Building the enterprise of the future: If not now, when ?

It should be plainly clear that we need knowledge management now more than ever. You can be sure that the COVID-19 crisis won't be the last crisis to come our way. And the next one might be even more severe because our supporting systems have taken some serious hits.

Enterprise of the future update: More disruption ahead

The concept of a phyle has experienced a resurgence, driven in part by the frustration people are feeling about being forced into making binary choices regarding the groups with which they want to be identified: public versus private, capitalist versus socialist, and liberal versus conservative.

Deep project management

Given the increased negative media exposure that comes from project failure, organizations need more tightly integrated, intelligent project management systems, in addition to people who have the requisite skills. This need will grow as systems continue to become more complex and timelines more tightly compressed.

A deep future approach to KM

We're familiar with the near-term portion of the time spectrum—from femtosecond lasers used in eye surgery to high-frequency trading in milliseconds on the major securities exchanges. Unfortunately, the extreme opposite end of the time spectrum, the "deep future" receives little if any attention. Decisions in fields such as genetic engineering, nuclear energy, geopolitics and the like can have serious implications for human civilization. But the impact of those decisions might not become apparent for many thousands of years and hundreds of generations.

Critical capacities for navigating in turbulent times

To navigate better in severe turbulence, three critical capacities are needed: foresight, agility and resilience. Those capacities have always been fundamental to sustaining high performance in knowledge-based organizations. But during times of turbulence, they need to be operating at peak levels.

An uncommon view of collaborative competencies

"How do you get the most from working together as a team?"