4 Nov 2023 – New field study finds AI will not put people out of work The technology will transform the employment sector but will likely leave few workers behind.

Published by rudy Date posted on November 9, 2023

New field study finds AI will not put people out of work
The technology will transform the employment sector but will likely leave few workers behind.

Loukia Papadopoulos
Published: Nov 04, 2023 02:11 PM EST

AI-powered automation has the ability to take over routine, repetitive, and manual processes. Consequently, employment in sectors such as manufacturing, data entry and customer service may be vulnerable with workers in these industries losing their jobs.

On the other hand, AI can also create jobs. The need for qualified experts with the ability to create, manage, and enhance AI systems is rising. Jobs related to data science, machine learning, robotics, and AI research are increasingly becoming more popular.

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A crucial question
So will AI be the source of new employment or the end of more traditional roles? A new study seeks to answer this one crucial question. Weiguang Wang of the University of Rochester and leading author of the new work undertook a field study among knowledge workers and found some pretty interesting insights.

“We were surprised by what we found in the study. The different dimensions of work experience have distinct interactions with AI and play unique roles in human-AI teaming,” said Wang.

The research also looked at what level of experience workers had to have in order to be best assisted by AI.

“While one might think that less experienced workers should benefit more from the help of AI, we find the opposite – AI benefits workers with greater task-based experience. At the same time, senior workers, despite their greater experience, gain less from AI than their junior colleagues,” said Guodong (Gordon) Gao of Johns Hopkins Carey Business School, and study co-author.

Increased productivity
The work highlighted the need to use AI productively to benefit the most from its many positive attributes. The researchers found that experienced workers can better utilize AI to increase productivity, but senior workers who take on more responsibility and have a deeper commitment to the company often avoid using AI because they fear the hazards involved in depending on it. Consequently, they are not using AI efficiently.

When implementing AI in the workplace, the researchers strongly advised businesses to take into account the various types and levels of worker experience. When using AI, new employees who have less task experience are at a disadvantage. Senior employees who have more organizational expertise, however, might be worried about the possible threats that AI could bring. Achieving successful human-AI collaboration requires tackling all these particular points of view and finding a middle ground.

The bottom line is that AI is more likely to change the nature of work than to cause job losses in most occupations. Worker attention might be diverted to more intricate and creative facets of their roles by the introduction of automation. In order to adjust to these changes, workers might need to pick up new skills but will most definitely not be left behind.

The study is published in the journal Management Science.

Study abstract:

As artificial intelligence (AI) applications become more pervasive, it is critical to understand how knowledge workers with different levels and types of experience can team with AI for productivity gains. We focus on the influence of two major types of human work experience (narrow experience based on the specific task volume and broad experience based on seniority) on the human-AI team dynamics. We developed an AI solution for medical chart coding in a publicly traded company and conducted a field study among the knowledge workers. Based on a detailed analysis performed at the medical chart level, we find evidence that AI benefits workers with greater task-based experience, but senior workers gain less from AI than their junior colleagues. Further investigation reveals that the relatively lower productivity lift from AI is not a result of seniority per se but lower trust in AI, likely triggered by the senior workers’ broader job responsibilities. This study provides new empirical insights into the differential roles of worker experience in the collaborative dynamics between AI and knowledge workers, which have important societal and business implications.

 

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