Living Research · Version 1.0

Generative Change Management

Foundational Concepts for Human-AI Hybrid Productivity in Organizations

AI is spreading through organizations faster than organizations are learning to work differently because of it. A tool can accelerate a task without changing how a team reasons, decides, verifies, or learns from experience. That gap between adoption and transformation, not the technology itself, is the problem this framework addresses.

This project proposes Generative Change Management (GChM) as a conceptual framework for closing that gap. Its unit of analysis is not the tool being adopted but the articulation of capabilities: how human judgment and AI production combine across three levels that shift together—the professional relationship people build with AI, the work architectures that organize human and digital production, and the cultural frameworks through which organizations distribute authority, trust, and meaning.

A central claim follows: the resulting capacity—hybrid productivity—is not a property of the technology an organization licenses. It is a property of how the organization designs the relationship between human and artificial capabilities, and for that reason it is difficult to reproduce through access to technology alone.

Primary object Transformation Capability developed
Personal Professional relationship with AI and the recognition base. Redefinition of professional contribution. Expanded professional capability.
Operational Articulation of human capabilities and digital production units. Design of new work architectures. Hybrid production architecture.
Cultural Symbolic-algorithmic linkage, authority, trust, and meaning. Organizational coexistence with algorithmic intelligence. Hybrid organizational intelligence.
Integrated Recursive articulation of the three planes. Organizational learning and redesign. Hybrid productivity.
Table 1. Architecture of the GChM framework: three planes of organizational preparation and one integrated result.

The architecture of the proposal

The proposal is organized around one framework, developed across three interdependent planes, and enacted through a recursive intervention cycle.

One framework

Generative Change Management: a conceptual framework for how organizations deliberately integrate human and AI capabilities across professional contribution, work architecture, and organizational culture.

Three planes, one result

The personal, operational, and cultural planes develop in parallel. Their recursive articulation produces hybrid productivity—an organization-specific capability, not a byproduct of the technology in use.

A five-phase intervention cycle

Analysis, design, and implementation form the core cycle. Evaluation and learning convert experience into organizational knowledge, and redesign feeds that learning back into the next iteration.

A note on scope

“Generative” is used here as it is in the companion project on generative sociology: to describe the transformative effect of a sustained relationship, not the use of generative AI as a research tool. The purpose of this paper is foundational rather than conclusive. It defines the conceptual territory of GChM, its main categories, and their relationships, and opens a research agenda rather than closing one. Operational indicators, instruments, and empirical validation belong to the methodological development this paper makes possible, not to this paper itself.

Read and cite

Manucci, M. (2026). Generative Change Management: Foundational Concepts for Human-AI Hybrid Productivity in Organizations (Version 1.0). Zenodo. https://doi.org/10.5281/zenodo.22033562

This is a living document. To cite a specific version, use that version’s DOI.

Version history

v1.0

August 2026. Initial public version. Three planes of organizational preparation, thirteen foundational definitions, five-phase intervention cycle.

Part of an ongoing program

This framework extends the CODHZ research program from the analysis of strategic reasoning to the organizational conditions that make hybrid human-AI production possible. Its account of the attributional relationship builds directly on the generative attributional bond, operationalizing at the organizational level a coupling first described at the level of the individual user.

Explore the research program →