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

2026-05-20

AI Summary

This work addresses the persistent “collaboration gap” in human-AI teamwork—where combined performance often fails to exceed the sum of individual human and AI capabilities—by proposing a systematic design space comprising six interconnected dimensions: sociotechnical context, decision framework, human agents, AI capabilities, interaction modalities, and holistic evaluation. For the first time, this framework unifies terminology and integrates insights from human factors engineering, AI system design, and sociotechnical systems theory. It offers practitioners a shared vocabulary for constructing hybrid systems and provides researchers with a conceptual lens to analyze collaboration patterns. Critically, the framework advocates evaluating human-AI decision-making through comprehensive quality metrics rather than narrow accuracy benchmarks, thereby transcending conventional limitations centered solely on explainability or interface design.

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.

Problem

• human-AI synergy
• synergy gap
• human-AI collaboration
• decision-making
• design space

Innovation

• human-AI synergy
• design space
• synergy gap
• sociotechnical context
• holistic evaluation