On Wednesday, May 30, Insurance-Canada.ca hosted its first in-person Technology in Action seminar, focused on Artificial Intelligence. Our theme was “AI: The Foundation of Next Generation Insurance” – but I was reminded to look backward to a phenomenon that is almost 100 years old.
AI is hot, but we have seen this before
The half-day was eclectic, ranging from “Opportunities and Threats” by Benjamin Von Euw from Industrial Alliance to “Leveraging the Power of AI” by Andrew Lo, President & CEO of Kanetix, and closing with “AI in 2030” by Nick Milinkovich from McKinsey.
And that was just the framework. We had additional content from experts in AI, insurance, and related organizations. And that latter group helped myself, and others understand how profound changes would be in the next generation.
I was privileged to moderate a panel that focused on “Where AI and Health Meet Insurance.” This followed a session by Farnoud Kazemzadeh, PhD, called “Precision Medicine: The Impact of AI in Healthcare.”
Farnoud presented on how machine intelligence has been used in the healthcare system for the past decade and how it is being proposed to be used today. He then painted a picture of how AI can be used in the future to allow everyone to be proactive and participate in their own healthcare, while seeking prevention through personalized precision medicine.
For the panel discussion, Dave Lund from AIG and McKinsey’s Milinkovich join Farnoud.
The panelists and attendees quickly engaged in discussions that interleaved modernized medical technologies with pressures coming from insurance programs to keep up with risks. As well for both entities, there are increased requirements to estimate costs and continue development for new protocols.
We’ve met this scheme before
Significantly, practitioners have to keep up with the demands to support new risks. In many cases, the front line workers need to have a close channel to decision makers. This is not new.
In the 1920s, Elton Maya a researcher, analyzed work performance in the Hawthorne Works operation outside Chicago. Mayo found that when lighting was brightened, productivity improved. However, he also found that decreasing light created the same improvement.
Writing in Explorable, Mayo concluded that “employees were pleased to be singled out, and increased productivity as a result.”
This is referred to as the “Hawthorne Effect.”
Back to today …
Fast forward to the Digital Era, and much has changed. But some things have not. The Hawthorne effect is not a single activity. The changes in the environment needed to be changed periodically. Workers were not the experts that are coding AI now, but were critical for the functions required on the floor.
And, most significantly, managers and team leaders in the Digital Environment have the same span of control as the Hawthorne supervisors. Each have to find ways to supportive team in innovative methods to maintain commitment and results.
The bigger challenge today is to deal with the AI machines. In one respect, the Hawthorne Effect is very different than AI devices. Turning lights up and down is highly simplistic.
However, at Hawthorne, there had to be a variety that maintained the highest level of interest. AI machines need to be calibrated to adjust to changes in the environment as well.