In Shenzhen, China, a photo from September 22, 2026, shows icons for ChatGPT, Meta’s Muse, and Anthropic’s Claude in an ‘Artificial Intelligence’ folder on a smartphone screen. This reflects the growing demand for Meta Platforms’ Muse AI assistant, sparking investor interest in AI ahead of Meta’s Connect conference. The conference will explore advances in AI, smart glasses, and virtual reality.
When evaluating AI’s economic impact, focusing solely on job creation or elimination misses the full picture. It’s crucial to consider changes within jobs, such as reduced time on tedious tasks, skill development, and opportunities for advancement. Speeding up tasks is valuable but distinct from improving job quality.
According to the American Job Quality Study by Jobs for the Future, The Families and Workers Fund, and Gallup, 63% of U.S. employees using AI in the past year report increased task completion speed. Furthermore, 46% say AI enhances work quality, and 47% note more time for interesting job aspects. While faster task completion can lead to new opportunities, their value depends on employer decisions.
AI usage varies among employees. About 40% of college graduates use AI weekly, compared to 17% of those without degrees. Managers are more likely to use AI regularly than individual employees. Among frequent AI users, 52% have jobs meeting multidimensional quality criteria, compared to 32% who never use AI. This suggests AI use aligns with quality job holders rather than directly creating better jobs.
Remote workers frequently use AI. However, simply making AI available doesn’t equalize the playing field. Workers without degrees can still gain from AI, as improvements in creativity and work quality are consistent across education levels.
The nature of work impacts AI accessibility. Jobs may lack regular digital device access or clear AI usage opportunities. Approaches should consider these differences rather than encouraging identical adoption rates across occupations. The real question is not simply how broadly AI is used, but how it boosts business productivity and whether employees benefit from these gains.
Research co-authored with economists Tina Highfill and Jon Samuels reveals industries with high AI usage reported stronger output growth from 2020 to 2026. Although the employment impact is less clear, the focus should remain on workplace dynamics.
Another study involving over 30,000 employees through the Gallup Workforce Panel highlights that employees with a clear organizational AI strategy frequently use AI more, increasing productivity. Trust in leadership plays a key role in these gains. Effective AI adoption requires communication and trust, not merely installing the software.
Thirty-one percent of AI users attribute increased responsibilities to AI use, which can mean advancement or simply more work without greater authority or pay. Notably, 52% of employees express limited influence over new technology adoption. Employers should consult workers about automation needs, human judgment requirements, and necessary support.
Employers should allocate paid time for employees to learn new tools and apply them to their tasks. Managers should clarify permissible tool usage, outputs verification, potential responsibility changes, and encourage strategic experimentation. Training should encompass frontline workers, not only those volunteering for AI pilots. Employees need a channel to flag errors or critique proposed uses without being seen as resistant.
While AI enables time savings, turning these into better jobs requires choices about employee roles, learning opportunities, and contribution rewards. These are human-driven aspects of AI adoption that software cannot dictate.
Christos A. Makridis is an associate research professor at Arizona State University and a senior researcher at Gallup.
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