Addressing the Synergy Gap: The Six Elements of the Design Space

TL;DR

This paper explores the comprehensive design space for human-AI synergy, identifying six interconnected elements crucial for achieving performance beyond individual capabilities.

Contribution

It introduces a six-element framework to better understand and design human-AI systems that foster genuine synergy, beyond traditional engineering approaches.

Findings

Mapped the six interconnected elements of the design space.

Provided a shared vocabulary for practitioners and researchers.

Highlighted the importance of holistic evaluation of human-AI decision-making.

Abstract

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.