Addressing the Synergy Gap: The Six Elements of the Design Space
For researchers and practitioners building human-AI systems, this paper offers a conceptual framework to systematically address the synergy gap, though it is a conceptual proposal without empirical validation.
The paper identifies the 'synergy gap'—the rarity of human-AI combined performance exceeding either alone—and proposes a six-element design space (sociotechnical context, decision frameworks, human participants, AI capabilities, interaction, holistic evaluation) to address it, providing a shared vocabulary and analytical lens for practitioners and researchers.
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.