Learning to Innovate with AI: Organizational Learning Mechanisms in Business Model Experimentation
Keywords:
artificial intelligence, business model innovation, socio-technical systems, human–AI collaboration, digital transformation, adaptive value creationAbstract
Artificial intelligence is increasingly reshaping how organizations create, deliver, and capture value in digitally mediated environments. Despite its rapid diffusion across industries, existing research remains conceptually fragmented in explaining how artificial intelligence interacts with human expertise to transform business model innovation . Prior studies emphasize algorithmic capabilities and automation, while business model research focuses on value creation architectures, leaving the socio-technical mechanisms linking these perspectives underexplored. This article addresses this gap by developing a conceptual framework explaining the emergence of hybrid human–AI business models. Adopting a theory-building approach based on conceptual synthesis across artificial intelligence, business model innovation, and socio-technical systems theory, the study identifies three core mechanisms: cognitive augmentation, task reconfiguration, and adaptive value creation. These mechanisms explain how algorithmic capabilities interact with human expertise to reshape value proposition design, transform value delivery architectures, and enable the continuous adaptation of business models. The framework contributes by integrating fragmented literature and conceptualizing AI-enabled business models as hybrid socio-technical systems, while offering a foundation for future empirical research on human–AI collaboration and strategic transformation in digital economies.
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