The Human Side of Generative Artificial Intelligence in Business Model Innovation

Generative artificial intelligence (GenAI) is growing in popularity, yet insights on its application to business model innovation (BMI) are scarce. Drawing on a qualitative, multiphase study involving interviews, focus groups, and digital diaries with strategy consultants, we explore how professionals engage with GenAI during BMI. Our findings suggest strategy professionals engage with GenAI through what we term reflexive augmentation, which represents the deliberate, critical engagement with GenAI to decide which tasks should and should not involve GenAI and which tasks to automate or augment through GenAI. We show how this process is shaped by four tensions related to trust, skills, value-add, and client disclosure. We offer actionable insights for managing human-AI collaboration, advancing debate on augmentation and automation at the micro-level, and suggest how organizations can support effective GenAI integration in innovation contexts.

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Publication Details

Journal
California Management Review
Published
2026-05-11
DOI
https://doi.org/10.1177/00081256261445458
Primary Topic
AI in Service Interactions
Type
article
Field-Weighted Citation Impact
0.00
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article

The Human Side of Generative Artificial Intelligence in Business Model Innovation

Gerda Gemser, Edward Giesen, Norman Azabagic
California Management Review
AI in Service Interactions
article

The Human Side of Generative Artificial Intelligence in Business Model Innovation

Gerda Gemser, Edward Giesen, Norman Azabagic
article en

Abstract

Generative artificial intelligence (GenAI) is growing in popularity, yet insights on its application to business model innovation (BMI) are scarce. Drawing on a qualitative, multiphase study involving interviews, focus groups, and digital diaries with strategy consultants, we explore how professionals engage with GenAI during BMI. Our findings suggest strategy professionals engage with GenAI through what we term reflexive augmentation, which represents the deliberate, critical engagement with GenAI to decide which tasks should and should not involve GenAI and which tasks to automate or augment through GenAI. We show how this process is shaped by four tensions related to trust, skills, value-add, and client disclosure. We offer actionable insights for managing human-AI collaboration, advancing debate on augmentation and automation at the micro-level, and suggest how organizations can support effective GenAI integration in innovation contexts.

California Management Review
The University of Melbourne (AU), Parks Victoria (AU), IBM (Netherlands) (NL), Monash University (AU)
Industry, innovation and infrastructure
Openalex Percentile: Top 5%
AI in Service Interactions
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