Stanford Study Urges Human Oversight for Complex Tasks in AI-Assisted Work

Stanford's SALT Lab has released a large-scale study examining how AI agents and humans can collaborate effectively across 104 occupations in the U.S. workforce. The research, built on a database of 2,131 AI-actionable tasks assessed by 1,500 domain workers and 52 AI researchers, finds that workers broadly support automating repetitive, low-value tasks but want to retain control over complex or high-stakes decisions. The team introduced the Human Agency Scale, a five-point framework measuring how much influence workers prefer to keep over a given task, rather than simply whether AI is technically capable of performing it. The study also categorizes AI-actionable work into four decision zones to help organizations distinguish tasks suitable for full automation from those better suited to human-AI collaboration. Rather than treating automation as a binary choice, the research advocates a task-level approach to deploying AI agents responsibly.