Redefining Boundaries: A Comparative Analysis of Human-AI Collaboration Dynamics in European Healthcare and Financial Services

Abstract

Purpose: This paper examines how human-AI collaboration boundaries are conceptualized and implemented differently across two key European sectors: healthcare and financial services. Using a narrative literature review methodology and the AI-Human Collaboration Theoretical Framework, it analyzes sectoral variations in collaborative dynamics.

Design/methodology/approach: A narrative literature review approach is employed to synthesize existing knowledge across disciplines. The AI-Human Collaboration Theoretical Framework is applied to analyze sector-specific patterns in human-AI interaction based on professional identities, risk models, and expertise traditions. Hypothetical case illustrations demonstrate practical applications.

Findings: The research identifies distinct human-AI collaboration models across sectors: healthcare prioritizes clinical authority; and financial services implements tiered authority models based on risk profiles. Sectoral contexts significantly shape collaborative boundaries through professional traditions, regulatory environments, and knowledge integration patterns.

Research limitations/implications: Limitations include the theoretical approach and use of hypothetical cases, which may not capture the full complexity within sectors. Future research should validate findings through empirical studies, examine boundary evolution longitudinally, and examine cross-sector learning opportunities.

Originality/value: This research contributes to innovation management theory by demonstrating how sector-specific professional identities and expertise traditions shape collaborative boundaries between human and technological agents. It offers a structured comparative framework for analyzing human-AI collaboration and provides actionable insights for designing collaborative systems that improve human capabilities within sectoral contexts.