Building Human-AI Fit: Evidence-Based Strategies for Adaptive Performance in AI-Augmented Work

Abstract: Generative artificial intelligence is reshaping knowledge work, yet organizational success depends not merely on deploying advanced systems but on cultivating productive human-AI relationships. This article synthesizes emerging research on human-AI fit—the cognitive and operational alignment between workers and AI systems—to identify evidence-based strategies that support adaptive performance while preserving critical human judgment. Drawing on adaptive structuration theory, experiential learning frameworks, and person-environment fit perspectives, the analysis examines how organizations can design AI-enabled work systems that balance technological responsiveness with user agency. Recent empirical evidence suggests that high adaptive performance emerges through multiple pathways: technology-driven configurations combining responsive AI systems with strong relational alignment, and human-driven configurations pairing proactive user engagement with perceived fit. Across both pathways, human-AI fit serves as a core relational condition linking system capabilities and user initiative to performance outcomes. The article presents organizational interventions spanning transparent AI interaction design, structured experimentation protocols, cognitive friction safeguards, platform governance frameworks, and continuous learning systems. These strategies aim to support not only short-term productivity gains but also sustainable collaboration patterns that maintain worker autonomy, professional judgment, and organizational accountability in AI-augmented workplaces.

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