Estimated reading time: 4 minutes
I recently came across an interesting research paper titled “Technological Disruption in the Labor Market” from the National Bureau of Economic Research based in Cambridge, Massachusetts. The paper was prepared for the 2024 Aspen Economic Study Group annual meeting, and it discusses the relationship between technological disruption and the labor market. It’s an interesting read but I will also forewarn you, it’s a research paper and reads like a research paper.
Obviously, when discussing the concept of technological disruption, the focus was on artificial intelligence (AI). There’s been a lot of talk about AI replacing workers and taking jobs. And that’s what attracted me to this paper. I wanted to see some research about the subject. Not just a “I think, or I feel that AI is gonna do this …”
The section of the paper that I found particularly interesting was the discussion around what it takes for a technology to be classified as disruptive. The paper says that disruptive technologies need to be what are called GPTs (general purpose technologies). A GPT is defined as a technology that “can affect an entire economy whether that’s a national or global level”. Examples of GPTs include electricity, automobiles, airplanes, computers, and the internet. So GPTs can be products, processes, or a system.
In the paper, the authors talk about the criteria for being identified a GPT and how that relates to the labor market.
Predictions and decision making. Many people think of AI as being predictive. I mentioned this in a recent article about using AI to predict what would be “in and out” in business and pop culture. Since most jobs include some level of predictions and decision making, is it possible that AI will have a role to play in most of our work?
Productivity. The answer to the question about predictions and decision making might rest in whether the results of those predictions and decisions increase productivity. We saw this in my little AI what’s in and out experiment. In some places, AI was spot on and in other places … way off the mark. So, did the accurate predictions benefit the organization? Or did the poor predictions hurt the company?
Demand for products and services. While productivity is a good thing, increased productivity needs to translate to the bottom line. The example used in the paper is agriculture. Farmers became productive with the adoption of agricultural tools (also known as GPTs). They produced tons of food. But people only need so much food … so did it translate to increased profits or excess waste?
The purpose of this discussion isn’t to tell organizations whether their artificial intelligence adoption strategy is good or bad. But I think it’s worth an internal conversation about what’s the business goal. Maybe a better way of saying it is what are the first level, second level, and trierarchy level goals? Today, organizations might say that AI helps them to make better decisions but ultimately, it will help us deliver a better bottom line.
What does this have to do with the labor market? Everything. The organization’s AI goals are going to drive what their workforce plan looks like. It’s going to identify the knowledge, skills, and abilities (KSAs) that the organization needs to be successful. It will show the organization where their skills gaps are so they can put plans in place to upskill and reskill employees.
Yes, it’s important to have an artificial intelligence policy. It’s equally important to have an artificial intelligence strategy including measurable goals.
Image captured by Sharlyn Lauby while exploring the streets of Denver, CO
The post Organizations Should Identify Their Artificial Intelligence Goals appeared first on hr bartender.
