Get your game on: KM skills needed for reliable use of LLMs
There is no questioning that generative AI is here to stay, but its use in mission-critical work has some way to go before it can be trusted and let loose.
Reframing the KM discussion
The tech sector is growing fast, but without thorough business analysis, insight, proper planning, and a focus on challenging the better-quicker-cheaper approach and replacing it with a beneficial-adaptable-affordable commitment, there is a world of trouble ahead.
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.
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.
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.
Flipping data science
No matter how much "intelligence" is programmed into a computer, it will very likely never understand the results it produces. Doing so takes human cognition, intuition, judgment, and other ways we humans make sense out of data.
Rebooting the information refinery
In the field of knowledge management, of course, the idea of turning data into information into knowledge has been a foundation concept for knowledge managers. But frankly, the ability to achieve this alchemy of data to knowledge has not been broadly demonstrated in practice. A next generation information refinery is required to make something meaningful and valuable out of the raw data flying around the firm and throughout the internet economy.
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.
If you use the word "content" to talk about stuff on the Web, my friend Doc Searls is likely to give you a stiff talking-to. People don't write content. They write articles, poems, songs, etc.
Insights from AIIM
Making a case for context