How GenAI will impact the labor market

The role of GenAI in augmenting human skills varies significantly across different domains, reflecting the diverse capabilities and limitations of AI technologies. Unsurprisingly, looking at a subset of 10 human skills, we find that programming and mathematics present the highest correlation with our GenAI augmentation score, while active listening and speaking rank the lowest.

GenAI’s ability to automate coding tasks, such as debugging and writing simple code, enhances productivity and allows programmers to concentrate on complex, creative aspects of software development. Similarly, in mathematics, AI excels in performing complex calculations quickly and accurately, proving invaluable in data-intensive tasks.

Conversely, areas such as learning strategies, speaking and active listening exhibit lower AI augmentation scores. While AI can support learning and communication processes, it still requires significant human input and oversight, particularly in tasks involving complex human interaction, creativity and emotional understanding. These scores underline the ongoing potential of AI technologies and their varying degrees of impact across the skills spectrum.

AI exposure scores vary greatly across sectors

Rather than replacing human workers, GenAI will serve as a powerful tool to augment and evolve roles, enhancing the productivity and capabilities of employees across various sectors. In our second and third articles of our “Economic impact of AI” series, we showed that the capital investment and productivity benefits from AI could be significant: totaling between US$1t and US$2t in additional gross domestic output (GDP) over the next decade in the US, and between US$2t and US$3.5t in additional global output.

At the micro level, we used the BLS occupational hierarchy data to categorize 800 occupations into 22 different major occupational groupings (Chart 4). We found that GenAI is transforming the workforce by enabling workers to delegate routine, data-heavy tasks to GenAI systems, thereby enhancing their focus on areas where human skills like strategic thinking, empathy and creativity are paramount. This evolution signifies an elevation and enrichment of human intervention.

For roles that involve repetitive, data-intensive tasks, such as scheduling, reviewing, designing, inspecting, debugging and coding, GenAI could have a significant impact. By automating these processes, AI enables workers to focus on more complex, strategic aspects of their roles. For example, in plant and system operations, AI can monitor system status and operational data, automating routine tasks and allowing human operators to concentrate on critical decision-making and problem-solving. This shift not only improves efficiency and accuracy but also allows workers to upskill and engage in more fulfilling and intellectually stimulating tasks.

On the other hand, roles that require a high degree of human interaction, empathy, creativity and judgment are less likely to be impacted by GenAI at first. Tasks that involve management oversight, strategic decision-making, interpersonal care, emotional perception and relationship building remain predominantly human-driven. For instance, in education, while GenAI can assist in creating and organizing teaching materials, the “personal touch” of teachers remains irreplaceable. Teachers and instructors tailor their teaching to individual student styles and needs, drive classroom engagement and provide the emotional and empathetic support that GenAI is less adapted to do. Similarly, functions in health care could benefit from the augmentation of GenAI, which can handle administrative or analytical tasks, allowing professionals more time and energy to focus on the high-value, human-centric aspects of their work.

Most and least exposed occupations

The breakdown across sectors shows that major industries with limited differences in AI augmentation scores can still display a wide range in the augmentation scores across sub-occupations. This is more evident when looking at the top and bottom 10 occupations among the more detailed categorization of 94 minor occupation groups. 

Looking at the top 10 occupations with the highest AI augmentation scores, professions like plant and system operators, physical scientists, agricultural workers, drafters, programmers, engineers, and architects involve a high degree of repetitive and data-driven tasks that AI can automate. These include data analysis and monitoring, operations scheduling, document review, design work, and safety inspection processes.

Although these occupations are significantly exposed to AI, total automation is unlikely as workers remain indispensable for overseeing processes, strategic decision-making and tasks requiring nuanced judgment. Indeed, the wide dispersion of AI augmentation scores for the top 10 of the 94 minor occupations group illustrates the importance of human intervention.

The 10 minor occupations with the lowest AI augmentation scores have the highest intrinsic human elements needed for their functions. For example, roles like post-secondary teachers require human interaction and ability to customize learning. Occupations such as health care practitioners demand personalized care and critical decisions that AI currently can’t provide. Roles such as cooks and firefighters necessitate precise physical intervention and high-stakes decision-making in unpredictable situations. Other roles, like sales representatives and administrative assistants, require relationship building and human judgment. Consequently, these roles, which require human interaction, decision-making, physical intervention and personalization, face lower risk from AI exposure at first.

Still, as is the case for major occupations with high AI exposure scores, there is wide dispersion across the low-AI-exposure occupations. Even occupations that require human interaction have some sub-functions that can be augmented via GenAI. Think of grading or course material preparation for a teacher, or prevention, monitoring and early risk detection for firefighters, or even email programming for sales representatives.

The relationship between GenAI exposure and wage levels in various occupations appears to exhibit a slight positive correlation, albeit with some nuances. An analysis of the 20 major industry sectors based on two-digit North American Industry Classification System (NAICS) structure and their respective average wages shows that higher AI exposure scores are generally correlated with higher wages.

Sectors such as information; finance and insurance; utilities; and professional, scientific and technical services report higher yearly wages (in the range of US$85,000 to US$92,000) and show slightly higher AI exposure scores. This could suggest that sectors requiring more advanced skills and therefore having higher salaries have greater exposure to AI. On the other hand, sectors like retail trade, accommodation and food services, and arts and entertainment report lower yearly wages (in the range of US$35,000 to US$50,000) and are associated with lower GenAI exposure scores.

Still, it’s important to stress the widespread diffusion of occupations across these major sector groupings both in terms of AI exposure and salaries. Health care is a prime example where both high-skill/high-salary functions and low-skill/low-salary functions have high AI exposure. For instance, a radiologist (higher skill/higher salary) is highly exposed to AI for detection and diagnostics, just like a nurse (lower skill/lower salary) is for administrative tasks.

Summary

This is the fourth installment of the EY-Parthenon macroeconomic article series on the economic impact of AI. The series aims to provide insights on the economic potential of generative AI, including new developments and actionable insights to arm companies’ decision makers. The fourth article in this series covers our four key findings regarding the use of GenAI and the potential impact of GenAI on the labor market.