Neuro-Organization: Governing Uncertainty Without Repeated Transformation

Abstract Conventional digital transformation relies on mechanical models such as the Target Operating Model or Business Process Management, which treat the organization as a machine optimized for efficiency and predictability (Stacey, 1995; Uhl-Bien et al., 2007). These models, however, fail to capture weak signals, to value collective intuition, and to navigate the growing complexity of internal and external information flows (Weick, 1995; Snowden, 2005). This article proposes an alternative framework: the organization as a neuro-organizational system, capable of combining conscious governance with an organizational subconscious. The organizational subconscious captures and analyzes both structured and unstructured information flows, generating intuitions graded by levels of conviction, while the conscious level arbitrates and decides within an ethical framework (Sutcliffe & Weick, 2008; Teece, 1997). This dual architecture makes it possible to transform informational chaos into strategic opportunities, to accelerate organizational learning, and to increase resilience and agility in the face of uncertainty (Uhl-Bien & Arena, 2018). The approach is illustrated through concrete examples and implementation mechanisms, integrating technology as a perception amplifier without replacing human judgment. It offers leaders a new paradigm for transforming once and for all, by building adaptive, intuitive, and antifragile organizations capable of capturing and exploiting the latent potential of information flows in complex and constantly changing environments.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-01-20
DOI
https://doi.org/10.5281/zenodo.18311289
Primary Topic
Innovation, Sustainability, Human-Machine Systems
Type
article
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article

Neuro-Organization: Governing Uncertainty Without Repeated Transformation

Mourad TISSAOUI
Zenodo (CERN European Organization for Nuclear Research)
Innovation, Sustainability, Human-Machine Systems
article

Neuro-Organization: Governing Uncertainty Without Repeated Transformation

Mourad TISSAOUI
article en

Abstract

Abstract Conventional digital transformation relies on mechanical models such as the Target Operating Model or Business Process Management, which treat the organization as a machine optimized for efficiency and predictability (Stacey, 1995; Uhl-Bien et al., 2007). These models, however, fail to capture weak signals, to value collective intuition, and to navigate the growing complexity of internal and external information flows (Weick, 1995; Snowden, 2005). This article proposes an alternative framework: the organization as a neuro-organizational system, capable of combining conscious governance with an organizational subconscious. The organizational subconscious captures and analyzes both structured and unstructured information flows, generating intuitions graded by levels of conviction, while the conscious level arbitrates and decides within an ethical framework (Sutcliffe & Weick, 2008; Teece, 1997). This dual architecture makes it possible to transform informational chaos into strategic opportunities, to accelerate organizational learning, and to increase resilience and agility in the face of uncertainty (Uhl-Bien & Arena, 2018). The approach is illustrated through concrete examples and implementation mechanisms, integrating technology as a perception amplifier without replacing human judgment. It offers leaders a new paradigm for transforming once and for all, by building adaptive, intuitive, and antifragile organizations capable of capturing and exploiting the latent potential of information flows in complex and constantly changing environments.

Zenodo (CERN European Organization for Nuclear Research)
École de management de Lyon (FR)
Openalex Percentile: Top 86%
Innovation, Sustainability, Human-Machine Systems
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Neuro-Organization: Governing Uncertainty Without Repeated Transformation — Mourad TISSAOUI · Zenodo (CERN European Organization for Nuclear Research) (2026) | TGRS Research Map | TGRS