Artificial intelligence is reshaping the workplace, but its impact on job quality is far from automatic. A new study from the American Job Quality Study — a collaboration between Jobs for the Future, The Families and Workers Fund, and Gallup — reveals that while many employees report that AI speeds up their work, whether that translates into more rewarding roles, learning opportunities, or simply more assignments hinges on decisions made by employers.
Among U.S. workers who have used AI at least a few times in the past year, 63% say it helps them complete tasks faster. Additionally, 46% report higher-quality output, and 47% say they spend more time on the most interesting aspects of their jobs. These numbers suggest AI's immediate benefits are tangible, but they also mask significant disparities in who gets to use the technology.
The study found that 40% of college graduates use AI at least weekly, compared with just 17% of workers without a degree. Managers are also more likely to be regular users than individual contributors. This gap matters because 52% of regular AI users hold jobs that meet the study's multidimensional quality criteria, versus 32% of non-users. However, the authors caution against interpreting this as evidence that AI creates better jobs. Instead, it may simply reflect that those in higher-quality roles—often more educated and managerial—are also the ones with greater access to AI.
Christos A. Makridis, an associate research professor at Arizona State University and a senior researcher at Gallup, notes that the adoption divide does not appear across every reported benefit. Among those already using AI, improvements in creativity and work quality are similar across education groups. College graduates are more likely to report speed gains, but the gap in adoption does not extend to all advantages. This suggests that broadening access to AI could help level the playing field, rather than reserving its benefits for the already tech-savvy.
The study also highlights that many differences in AI use reflect the nature of the work itself. Not every job offers regular access to digital devices or clear opportunities to integrate AI. A sensible policy approach should acknowledge these variations, rather than aiming for identical adoption rates across all occupations.
Beyond individual benefits, the research explores macro-level effects. In a working paper co-authored with Bureau of Economic Analysis economists Tina Highfill and Jon Samuels, Makridis found that industries within states with higher worker-reported AI use in 2025 and 2026 had experienced stronger output growth since 2020. The link is clearer for output than for employment, where estimates are less precise but still positive.
However, the real challenge lies inside workplaces: how can organizations redesign jobs so that both businesses and employees benefit? Another study by Makridis, following more than 30,000 employees through the Gallup Workforce Panel, found that workers who perceive a clear organizational AI strategy are substantially more likely to use AI frequently, even after controlling for demographics and other workplace perceptions. Among frequent users, greater trust in leadership is associated with larger self-reported productivity gains. This points to communication and trust as essential complements to the technology—not optional extras.
Worryingly, 31% of AI users say their use has led employers to assign them more responsibilities. This could be a path to advancement, but it might also mean more work without increased authority or pay. Meanwhile, 52% of all employees say they have less influence over the adoption of new technology than they would like. Employers should take this seriously: consulting workers before changing their tasks is a practical way to identify what deserves automation, where human judgment remains necessary, and what support employees need.
Makridis recommends that employers start with paid time for employees to learn and test approved tools on real job tasks. Managers should clearly explain which uses are permitted, how outputs will be verified, and how responsibilities may change, while encouraging deliberate experimentation. Training should reach frontline workers, not just those who volunteer for AI pilots. Employees should also feel safe to flag errors or question proposed uses without being labeled as resistant to innovation.
Workers are already seeing time savings from AI, but turning those savings into better jobs requires deliberate choices about what employees do next, what they learn, and how their contributions are rewarded. That part of AI adoption cannot be left to the software—it must remain a human-driven exercise.
