Let’s Get Real About the Impact of AI on Jobs

History tells us that industrial revolutions ultimately do create more jobs, but that the transition period is long and highly turbulent. Thus, when we see resistance in the workplace to AI and automation in general, we should acknowledge that the resistance and fear are well-grounded.

Pricing Models on the Horizon

What is inarguable is the fact that enterprise SaaS is incredibly profitable and, by default, incredibly expensive to use over time.

The Long- and Short-Term Impacts of AI Technologies

A much less-known but arguably more critical tech law is Amara's Law, which states that we tend to overestimate the short-term impact of new technology while underestimating its long-term effects.

Agentic AI—So hot right now!

We are in the earliest stages of Agentic AI, and, much like the early days of RPA and GenAI, there's a lot of excitement but also a lot of uncertainty. While the potential benefits are enormous— streamlined operations, lower costs, fewer human errors—there are equally important concerns about job displacement, bias in AI decision making, and a lack of transparency in how these systems operate.

The rise and potential fall of the citizen developer

The citizen developer movement was heralded as a revolution. Like most revolutions, things have sometimes gone differently than planned. The logic is sound, empowering those who know the business best to build the tools and systems needed to do their job. Ah, if only things were that simple …

The end of tech glory days

The tech industry's glory days may be fading a little, but this is not a time for despair. It's an opportunity for renewal. By shifting to a needs-driven approach, the industry can ensure its relevance in a rapidly changing landscape.

The third place of knowledge management

The third place I alluded to goes far beyond mechanistic KM or curated knowledge and takes us into the actual world of tacit knowledge. Here, knowledge comes from and often remains as personal experience, impressions, and intuition; it's undocumented and often hidden and elusive.

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.

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.

The evolution of the KM technology stack

Historically, KM managers have tried to centralize knowledge assets into a single KM platform and curate within it. But outside of a few niche use cases, this has not been feasible for many years. Combining few KM human resources and an increasing data deluge makes it impractical. That is not to say we don't have the tools and resources to manage knowledge assets effectively; rather, we need to recognize corporate realities, be open to innovation, and embrace radical change.

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 effect of ChatGPT on KM

At this peak of ChatGPT hype, we have to ask what value it may bring.

Introducing work intelligence systems

Technological advances are significant and can bring huge benefits, but only as long as you understand that they can advise, augment, and support, but not replace, you.

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.

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.

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.

The Law and AI

AI is very good, and light years ahead of where it was just a decade ago, but it is far from "intelligent." Indeed, it is only as good as the data it is provided and needs close human supervision.

Knowledge unchained

Blockchains eliminate the need to trust other people. That's it; that is all there is to it. Trust is deferred to the system itself.