Approximately 28.5% of skills could potentially replaced by Generative AI in the future if businesses change some practices and the tools improve!

?? Among the five possible outcomes (very unlikely, unlikely, possible, likely, very likely), the majority of skills assessed (68.7%) were deemed ‘very unlikely’ or ‘unlikely’ to be replaced by Generative AI.

?? While Generative AI excels at delivering theoretical knowledge, it is less proficient in applying those skills to practical problem-solving.

?? The influence of Generative AI on any given occupation will hinge on the specific skills within that occupation that the technology can or cannot effectively perform, both now and in the future.

?? Today’s generation of GenAI tools are not “very likely” — literally — to replace a competent human worker in mastering and performing even a single one of thousands of common work skills.

Every occupation requires a worker to utilize a mix of skills across three key areas: Obtaining, retaining, and providing theoretical knowledge. But a digital “worker” powered by GenAI, while potentially strong across one or even two of these measures, will struggle to achieve the same seamless balance across all three, according to a new interesting research published by Indeed researchers using data from more than 2,800 skills common in US job postings published on Indeed between August 01, 2023, and July 31, 2024.

?Researchers’ Methodology

Researchers identified more than 2,800 common work skills, from “account management” to “wound care.”

With this detailed skills taxonomy, Hiring Lab then developed a sophisticated prompt framework to evaluate these skills using GPT-4o, a large language model developed by OpenAI. The model was asked to assess its own ability to perform a skill across the three dimensions noted above.

?GenAI excels at moderate problem-solving but struggles with more complex issues

Researchers discovered that Generative AI rated its ability to provide theoretical knowledge quite highly, though it rated its problem-solving capabilities slightly lower.

For 79.7% of the skills evaluated, Generative AI gave itself a score of 4 (‘good’) for providing theoretical knowledge. It rated its ability to solve ‘moderate’ problems (a 3 on the 5-point scale) using 70.7% of the skills. Importantly, physical execution was considered ‘essential’ (a 1 on the 5-point scale) or ‘highly necessary’ (a 2 on the 5-point scale) for a slight majority (54%) of the skills analyzed."

?Gen AI possesses a moderate capability for problem-solving.

Researchers found that for more than 7 in 10 skills assessed (70.7%), GenAI’s ability to solve problems was rated a 3 (“moderate”).

For more than a quarter (27.7%) of skills rated a 3 for problem-solving, the likelihood of GenAI replacing a human was also rated a 3. And for the 2.8% of skills at which GenAI rated its problem-solving ability a 4 (“good”), it also assigned itself a human replacement rating of 4 (“likely”).?

?GenAI faces significant challenges when it comes to meeting physical demands

Researchers also observed that physical execution was deemed ‘slightly necessary’ or ‘not necessary’ for all 101 skills that received a ‘good’ rating (a 4 on the 5-point scale) for problem-solving.

These findings indicate that even if Generative AI demonstrates strong problem-solving abilities with certain skills, it will likely remain limited in replacing humans for tasks that require hands-on execution.

?Potential for Generative AI to Replace Skills Across Various Occupations

Researchers noted that the distribution of skills where Generative AI excels or falls short varies significantly across the thousands of occupations performed by millions of workers daily.

For instance, many tech roles demand a higher proportion of digital skills that require minimal hands-on execution, making them more likely to be effectively performed by a Generative AI model.

?? In contrast, care roles, such as nursing or childcare, involve a broader range of skills where Generative AI might provide some knowledge but often require physical execution, limiting its potential to fully replace human workers.

?The Influence of physical execution and problem-solving on Generative AI’s replacement Potential

Researchers revealed that skills requiring high levels of physical execution have a low potential for replacement by GenAI.

Researchers also emphasize that a strong problem-solving ability is linked to a higher likelihood of eventually replacing human workers. However, at present, Generative AI is not particularly adept at solving problems using skills common in many jobs

?? Researchers highlighted accounting as an example of an occupation with a significant proportion of skills that are neither highly likely nor highly unlikely to be replaced by Generative AI.

The majority (58%) of skills commonly listed in accounting job postings are rated as having the possibility (a 3 on the 5-point scale) of being replaced by Generative AI. The ultimate impact of Generative AI on the accounting field will depend on various factors that are yet to be determined.

?? Finally, researchers conclude that the most significant barrier to widespread Generative AI adoption may be the demanding nature of the tools themselves:

?? In jobs with a high share of skills that require hands-on execution, including nursing, GenAI could help with some repetitive tasks (like documentation) and allow workers to refocus on the core skills necessary in these roles.?

?? In more stereotypical “office jobs,” including software development, GenAI may potentially be able to offer significant knowledge and solve modest problems, emphasizing the importance of continued upskilling and ongoing learning for human workers.

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This remarkable research on the impact of Generative AI on workers highlights that problem-solving is a hallmark of human ingenuity. The more adept a Generative AI model becomes at using a given skill to solve problems, the higher its likelihood of replacing a human.

It’s important to remember that these tools currently lack physical ‘bodies’ to perform manual tasks, though this may change with future advancements in robotics. Until then, the need for manual, hands-on execution of certain skills significantly influences their potential for replacement by Generative AI.

??Thank you Indeed researchers team for sharing these insightful findings:

Dr. Annina Hering Arcenis Rojas

Dave Ulrich

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