Document Type

Article

Source of Publication

Knowledge and Process Management

Publication Date

3-12-2026

Abstract

Organisations struggle to optimise human–AI collaboration in knowledge-intensive decision-making. This paper proposes the Trust–Complementarity Model of Collective Intelligence (TCM-CI), explaining how calibrated trust and complementary capability utilisation drive superior organisational performance. Through systematic synthesis of human–AI interaction and knowledge management research, we identify three core mechanisms: (1) calibrated trust maximises collective intelligence by balancing appropriate reliance with necessary oversight, (2) complementarity–trust interaction determines optimal performance when high capability utilisation combines with appropriate trust levels and (3) dynamic feedback loops create reinforcing organisational learning cycles. The framework provides practical guidance for executives designing human–AI teams, developing trust calibration training, and establishing performance metrics. By integrating psychological trust factors with cognitive capability optimisation, this model offers actionable insights for knowledge management practitioners implementing AI-augmented decision systems while advancing theoretical understanding of human–AI collaboration effectiveness.

ISSN

1092-4604

Publisher

Wiley

Disciplines

Business

Keywords

decision-making performance, human–AI collaboration, knowledge management, organisational learning, process optimisation, trust calibration

Scopus ID

105032583895

Creative Commons License

Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.

Indexed in Scopus

yes

Open Access

yes

Open Access Type

Hybrid: This publication is openly available in a subscription-based journal/series

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