37% of Hourly Workers Report AI at Work

PYMNTS reports that 37% of US hourly "Labor Economy" workers said their employer introduced new automation or AI tools during the last 12 months, according to PYMNTS coverage of a study of low‑income hourly workers. PYMNTS states the study defines Labor Economy workers as hourly employees earning up to $25 per hour and generally under $50,000 annually. PYMNTS says this cohort represents about 60 million adults and roughly 15% of annual US GDP. The PYMNTS article notes sectors seeing adoption include warehouses, restaurants, hospitality, logistics and caregiving. PYMNTS reports that nearly 60% of affected Labor Economy workers said they did not receive training on the new technology, 42% said they received instruction, and 39% said they were confident they could find comparable‑paying work if their role were eliminated by technology.

Industry‑wide reporting shows enterprise automation tools are moving beyond back‑office pilots into frontline operations, a shift that typically increases the need for practical, role‑specific training rather than abstract AI literacy. Companies deploying AI into hourly workflows often face integration issues such as edge device management, low‑latency inference, and human‑systems handoffs. Observed patterns in comparable rollouts indicate adoption without parallel investment in upskilling commonly reduces worker confidence and raises operational risk related to misuse and workarounds.

Editorial analysis: The PYMNTS findings sit at the intersection of technology diffusion and labor economics. For practitioners designing or operationalizing AI systems for frontline workers, the report underscores a mismatch between deployment velocity and worker preparedness. Lower reported training rates and limited financial buffers among Labor Economy workers increase the practical importance of human‑centered change management, accessible training materials, and monitoring for unintended workflow disruption.

Industry observers should monitor follow‑up studies that disaggregate training by tool type, employer size, and sector to identify where training shortfalls are largest. Pay attention to whether labor organizations, regulators, or large employers publish guidance or agreements addressing training, redeployment, or transition supports for hourly workers. For product teams, metrics to track include training completion rates, task success rates post‑deployment, and worker confidence or churn in impacted roles.

(Reporting above is drawn from the PYMNTS article summarizing the study; analysis is LDS editorial commentary.)