Many businesses are focused on whatever the latest technology [e.g. LLM, GPT] is going to bring. But too many workplaces don’t even have the basics in place. There are large organizations with no knowledge management function. How do they share knowledge across a global enterprise? They don’t. Many companies have not mastered the basics of conducting meetings, something for which there are many good practices. There are ‘learning’ departments that only focus on courses and formal instruction, ignoring performance improvement, collaboration, and social learning.
Work
business & working
asleep at the wheel
A hybrid model — working a combination of on-site and from anywhere — creates different classes of workers and maintains the wall between management and workers. Hybrid workplaces are an effort by management to hang on to power and privilege for as long as possible, I concluded in the worst of both worlds in May 2021. The next month, in not remotely working, I suggested that as — or if — this pandemic winds down, it may get difficult to attract and retain talented people unless they have a degree of control over where they spend their time, especially if competitors offer work from anywhere. This year, in 2023, I note in work is human that the taste of working from anywhere has accelerated the departure of older workers — “Dennis C. would rather retire than return to the office full time — and that’s exactly what he did.”
“it was 20 years ago today”
Today marks the 20th anniversary of Jarche Consulting. There were several times when I thought that I would not make it this far. The roller coaster ride continues, having weathered the great recession and a pandemic. Who knows what the future will bring? For now, I am grateful to my friends, colleagues, and especially my repeat clients who continue to have confidence in my work. It has been a great pleasure.
Writing on this blog, after +19 years, continues to be a primary way that I make sense and connect with people around the globe. Here are some thoughts I shared along the way as I marked other anniversaries.
work is human
Is there a talent shortage today, or merely ineffective hiring practices?
While employers lament a talent shortage, they are scrambling to increase talent attraction and retention. They offer new incentives and promises of increased flexibility and an inclusive company culture. However, most of these efforts ignore one key factor that could make a significant difference–including talent across the age spectrum.
Instead, talent management processes such as recruiting, hiring, promoting and retaining tend to exclude individuals under 24 or over 40. The result is a 16-year criterion for talent. —Forbes 2023-03-12
Is ageism a primary factor influencing the retention of skilled and experienced workers?
the act of creation is human
In 2005 I wrote a business plan for a client that was based on an operational model of employing ‘knowledge artisans‘.
Next-gen knowledge artisans are amplified versions of their pre-industrial counterparts. Equipped with and augmented by technology, they rely on their human capital and skill to solve complex problems and develop new ideas, products and services. Highly productive, knowledge artisans are capable individually and in small groups of producing goods and services that used to take substantially larger teams and resources. In addition to redefining how work is done, knowledge artisans are creating new organizational structures and business models.
I later followed this up by discussing how knowledge artisans choose their tools.
automation vs augmentation
Understanding machine learning (ML), generative pre-trained transformers (GPT), and large language models (LLM) has become a part-time job for me. Not only is there a lot of information and discussion, but a wide range of opinions. The topic of ‘AI’ constantly pops up in professional meetings. Researcher danah boyd discusses the difference between the perspectives of automation vs. augmentation as ‘AI’ develops.
“When it comes to AI’s potential future impact on jobs, Camp Automation tends to jump to the conclusion that most jobs will be automated away into oblivion … most in Camp Automation tend to panic and refuse to engage with how their views might intersect with late-stage capitalism, structural inequality, xenophobia, and political polarization … Camp Augmentation is more focused on how things will just change. If we take Camp Augmentation’s stance, the next question is: what changes should we interrogate more deeply?” —Zephoria 2023-04-21
I am mostly in the augmentation camp, though I am concerned that automation + capitalism = a perfect storm. This was the case with the augmented work enabled by the personal computer. Knowledge work improved significantly but wages did not.
step lively
It seems that today everyone is chatting about GPT (generative pre-trained transformers) and what feeds them — large language models (LLM). I am always skeptical when the next techno-hype cycle comes around but this one seems different. The worst case scenario does not look good, especially for knowledge workers.
In a few months, maybe a year, the first wave of AI-driven layoffs slash firings are going to hit the economy. And then? They’ll just keep going. Executives are going to figure out that a whole lot of work — clerical, administrative, accounting, legal, writing, marketing, customer relations, even decision-making and risk analysis and data analysis — can be automated. AI’s going to be like offshoring, but much, much worse. Offshoring wiped out the working class — AI’s going to finish the job of wiping out the middle class. Offshoring eviscerated blue collar jobs — AI’s going to wipe out some pink collar ones, and a whole lot of white collar ones, too. —Umair Haque 2023-04-28
understanding the hype and hope
I have been keeping an eye on the hype & hope around artificial intelligence (AI), especially:
- ML — machine learning
- GPT — generative pre-trained transformers
- GAI — generative artificial intelligence
- LLM — large language models
“I’ve long been a fan and found value in AI / ML and its capabilities. Learning and finding patterns and causal patterns that in time can lead to outcomes that are problematic (a large fleet of vehicles with hundreds of sensors feeding and AI / ML to detect early engine, transmission, or other failure to address before more expensive damage or at a human cost). Generative AI from large language models is missing core pieces still and had knock-on effects that are really problematic with its lack of understanding facts (or multitudes of facts and truths), but more problematic is it blunts human learning and cognition.”
—Thomas Vander Wal 2023-03-18
How Technology Influences Social Networks
Stewardship of global collective behavior —2021-06-21
“Human collective dynamics are critical to the well-being of people and ecosystems in the present and will set the stage for how we face global challenges with impacts that will last centuries. There is no reason to suppose natural selection will have endowed us with dynamics that are intrinsically conducive to human well-being or sustainability. The same is true of communication technology, which has largely been developed to solve the needs of individuals or single organizations. Such technology, combined with human population growth, has created a global social network that is larger, denser, and able to transmit higher-fidelity information at greater speed. With the rise of the digital age, this social network is increasingly coupled to algorithms that create unprecedented feedback effects.”
pilots and copilots
Simon Terry has a short post on Microsoft’s new Copilot and how we should be careful in fully adopting some of these generative AI tools.
LLMs [large language models] are improvements on past tools but are hardly perfect. In a world where the volume of information means many people scan everything, we need to remain alert for the risks of the models false inferences or patterns gone awry.
In the history of aviation, it became apparent that pilot personal relationships are critical to avoiding dangerous incidents. Authoritarian cultures meant senior pilot mistakes went devastatingly unchallenged. —Microsoft Co-pilot
I mentioned pilot training on a post recently — experience cannot be automated. I concluded that automation, in all fields, forces learning and development out of the comfort zone of course development and into the most complex aspects of human learning and performance. On that post is also a quote by Captain Sully Sullenberger, the famed pilot who safely landed a passenger jet on the Hudson River. A movie was made about this, which included the subsequent safety investigation. Tom Hanks plays Sully and in this sequence of videos we see the difference between human cognition of experienced pilots versus the best software/hardware simulation of the day. There is no comparison.
how ideas become ideology
Several times I have referred to this observation about how ideas connect to ideology.
“Ideas lead technology. Technology leads organizations. Organizations lead institutions. Then ideology brings up the rear, lagging all the rest — that’s when things really get set in concrete.”—Charles Green (2009)
Here are some examples of these shifts.
Ideas lead technology
Hedy Lamarr invented spread spectrum technology in 1941 but its value as a technology accelerated half a century later as it would, “galvanize the digital communications boom, forming the technical backbone that makes cellular phones, fax machines and other wireless operations possible.”
Peter Senge’s book, The Fifth Discipline, ushered in the idea of the learning organization but it was only recently that organizations had the Web 2.0 technologies to enable distributed team learning or share systems-thinking across the enterprise.