Human-AI performance depends on explicit decision rights, not on a generic instruction to keep a person in the loop. This matters now because agent deployments can widen faster than organizations define supervision capacity or update role design.
IBM's report focuses on the operating model that determines where AI participates in work and where people retain authority. The study connects adoption barriers with unclear decisions, responsibilities, and the design of human-AI collaboration. The mechanism is a risk-tiered topology that assigns routine decisions to automated lanes, routes exceptions to qualified people, and records who remains accountable for the outcome.
Teams should map decisions and exceptions before tools, then measure intervention quality, delay, error recovery, and the development of human judgment. The evidence is IBM Institute for Business Value research and executive interpretation, not a universal causal model. Watch whether organizations can reduce unnecessary review while preserving accountability and improving outcomes in high-risk cases.