Refers to the idea that artificial intelligence (AI) systems and humans can work together in ways that improve performance, productivity, decision-making, creativity, or problem-solving beyond what either could accomplish alone. AI complementarity focuses on how AI can support, extend, or enhance human capabilities rather than replace human workers. In these models, humans and AI systems often perform different but connected tasks based on their respective strengths. For example, AI systems may rapidly analyze large amounts of data, identify patterns, automate repetitive tasks, or generate drafts, while humans provide judgment, context, ethics, relationship-building, creativity, oversight, and decision-making. Rather than assuming AI will eliminate most jobs, complementarity models examine how work roles may be redesigned so humans and AI systems contribute together.

The term is increasingly used in workforce and labor market research, economics, business strategy, higher education and workforce development, human resources and talent development, and public policy discussions about AI and the future of work and is becoming increasingly important in discussions about job redesign, reskilling and upskilling, skills-first hiring, human-AI collaboration, workforce productivity, and organizational change.

Examples of AI complementarity include:

• Healthcare: Clinicians use AI to identify possible medical conditions while clinician makes final treatment decisions.
• Education: Faculty members use AI tools to help generate lesson outlines while instructors provide expertise, teaching judgment, and student engagement.
• Customer service: Workers use AI-generated summaries or recommendations during conversations with clients.
• Researchers: Use AI tools to organize and analyze large collections of information while interpreting meaning and implications.