AI is now embedded in healthcare, finance, policy, and many other domains, yet genuine human-AI synergy - combined performance that exceeds what either party achieves alone - is uncommon. Meta-analyses show that AI assistance tends to improve human performance compared to working alone, but studies finding true synergy are scarce. We call this persistent shortfall the synergy gap. Most current work treats human-AI combination as an engineering problem and concentrates on interpretability, trust calibration, or interface design. These matter, but they cover only part of what determines whether combination works. Closing the synergy gap, we argue, requires explicit engagement with a wider design space. We map that space through six interconnected elements: sociotechnical context, decision-making frameworks, human decision participants, AI capabilities, interaction, and holistic evaluation. For each element, we describe what it covers, how it shapes the others in practice, and what it implies for design. The result is a shared vocabulary for practitioners building hybrid systems, an analytical lens for researchers studying combination patterns, and a starting point for evaluators interested in the full quality of human-AI decision-making rather than accuracy alone.

翻译：人工智能现已嵌入医疗、金融、政策及众多其他领域，然而真正的人机协同——即联合绩效超越任何一方单独达到的水平——仍不常见。元分析显示，与单独工作相比，人工智能辅助往往能提升人类表现，但发现真正协同效应的研究却很少。我们将这种持续存在的不足称为协同差距。当前大多数研究将人机联合视为工程问题，聚焦于可解释性、信任校准或界面设计。这些因素固然重要，但仅涵盖决定联合是否有效的部分内容。我们认为，要缩小协同差距，需要明确地探索更广泛的设计空间。我们通过六个相互关联的要素来映射这一空间：社会技术语境、决策框架、人类决策参与者、人工智能能力、交互机制及整体评估。针对每个要素，我们阐述其涵盖范围、在实践中如何影响其他要素，以及对设计的意义。最终成果是为构建混合系统的从业者提供共享词汇表，为研究联合模式的分析者提供理论视角，并为关注人机决策整体质量（而非仅准确率）的评估者提供起点。