Human AI teamwork shapes future performance by design choices
Adaptive Complementarity in Human-AI Systems: Architecture as a State-Shaping Choice
Human-Computer Interaction
Summary
Working together, humans and AI systems can improve how tasks are done, but the way they interact also changes what they can do in the future. The authors introduce a way to think about these interactions, focusing on things like who sees what information, how tasks are divided, and when people and AI communicate. These choices affect the growth of skills and knowledge for both humans and AI, influencing success later on. They also explain when sticking to a known way of working is better than constantly changing, and how past interaction shapes what is best in the future.
Human-AI interactionAdaptive complementarityInformation exposureTask allocationCapability evolutionCommunication timingStrategic interdependenceInformation governanceWorkflow evaluationState feedback
Authors
Babak Heydari
Abstract
Human-AI interaction can improve current performance while changing the capabilities and relationships on which future performance depends. We develop adaptive complementarity, a framework for choosing interaction architecture with these state consequences in view. Access, information exposure, task allocation, timing, and communication can alter which arrangement will be valuable later; their settings can often be reset faster than the capabilities, search patterns, or conventions they create. Three mechanisms organize the argument: information exposure and collective search, delegation and capability evolution, and strategic interdependence and information governance. Their integration yields cross-mechanism implications, including conditions under which a loss of expertise heterogeneity increases the information differentiation required to preserve independent search. We distinguish strong human-AI complementarity from advantage over another workflow and from advantage over an evolving reference policy. A knowledge-coverage illustration shows how different interaction histories can reverse current workflow rankings even at equal human competence. It also separates that result from the incremental value of state feedback, which can be small when a well-chosen stable workflow anticipates learning. The framework directs evaluation toward the states present interaction creates, their consequences for later architectural fit, and the conditions under which observing and responding to them is worthwhile.