Digital augmentation increasingly changes not only how work is performed but also who exercises judgment, how expertise develops, and when workers rely on technological recommendations. Existing scholarship often treats these outcomes separately or frames artificial intelligence primarily as automation, obscuring the organizational design choices through which human and technological contributions are combined. This integrative review examines how digital augmentation reshapes work through four connected domains: human agency, expertise development and erosion, trust in human–technology collaboration, and work-redesign configurations. The review combines theoretical, qualitative, experimental, field, and multilevel evidence while preserving differences in design, context, and inferential strength. Across the literature, augmentation does not emerge as a uniform condition. Human discretion can coexist with strong algorithmic control; short-term performance gains may accompany uncertain long-term skill trajectories; and reliance on artificial intelligence can support judgment while also creating risks of overreliance, knowledge convergence, or diminished developmental participation. The review therefore proposes that digital work redesign should be analyzed through configuration-specific allocations of authority, learning opportunity, and reliance rather than through technology labels alone. The resulting synthesis identifies organizational implications for accountability, participation, and capability development, while emphasizing that context, task structure, expertise, opacity, and level of analysis constrain transferability. The proposed framework is an organizing synthesis rather than a validated causal model and requires longitudinal, comparative, and multilevel testing.
