Beyond Artificial Intelligence: Toward a Generative Ontology of Organizational Innovation

1. Introduction

Over the past decades, artificial intelligence (AI) has progressively established itself as a central object of inquiry within management sciences, to the extent that it now structures a significant portion of contemporary research agendas (Administrateur, 2018). Whether examining its impact on organizational performance, decision-making processes, or innovation dynamics, the academic literature has expanded considerably, reflecting sustained and growing scholarly interest (Brynjolfsson & McAfee, 2014; Davenport & Ronanki, 2018; Raisch & Krakowski, 2021). Yet, beneath this proliferation of studies lies a persistent and largely unaddressed limitation: AI is predominantly conceptualized as a technological artifact or a strategic lever, while the ontological conditions that underpin its emergence and effects remain largely unexplored.

This reduction to an instrumental logic is not incidental. It is deeply embedded within a broader tradition in management research, where innovation itself is typically framed as a process, an outcome, or an organizational capability (Schumpeter, 1934; Rogers, 2003; Teece, 2007; Roumy, 2022 ; Moleka, 2024a ; 2026a). While such approaches have generated significant empirical insights and practical applications, they are grounded in a tacit assumption: that innovation constitutes an observable phenomenon whose determinants can be isolated, measured, and ultimately optimized. However, this assumption becomes increasingly problematic in light of contemporary organizational realities, where innovation unfolds through non-linear, emergent, and fundamentally unpredictable dynamics (Dorion et al., 2019; Arthur, 2009; Anderson, 1999).

More fundamentally, the persistent focus on observable outputs and formalized processes obscures what arguably constitutes the core of the innovative phenomenon: the underlying structures that make the emergence of novelty possible. As highlighted by Thomas Kuhn (2018) and Karl Popper (2005), scientific inquiry is always shaped by implicit frameworks that define what can be observed, conceptualized, and theorized. In the case of management sciences, these frameworks tend to privilege an ontology of action, decision, and control, thereby marginalizing alternative ontological perspectives centered on emergence, potentiality, and generativity.

Against this backdrop, a critical question arises: what if artificial intelligence is not the primary driver of organizational transformation, but rather the visible manifestation of deeper generative dynamics embedded within specific structural configurations? Such a proposition calls for a profound theoretical shift—from analyzing observable effects to interrogating the conditions of possibility that render those effects intelligible.

This article is situated within this broader intellectual movement. It seeks to reconceptualize innovation through the lens of a generative ontology, drawing on the emerging framework of Innovationology. The objective is twofold. First, it aims to move beyond dominant approaches that remain centered on processes and outputs, by reorienting attention toward the structural conditions that enable or constrain transformation. Second, it develops a theory of generative innovation structures capable of accounting for the emergence, stabilization, and failure of organizational transformations across diverse contexts.

2. Structural Limitations of Contemporary Management Sciences

Contemporary management sciences are characterized by a remarkable accumulation of empirical knowledge and an increasing sophistication of methodological tools. However, beneath this apparent richness lies a set of structural limitations that significantly constrain their ability to fully apprehend organizational phenomena in their deepest form (Sangaré, 2025; Segueni, 2022). These limitations are not merely empirical gaps or isolated theoretical disagreements; rather, they reflect deeper epistemological and ontological constraints embedded in the dominant modes of problem formulation within the field.

2.1. Disciplinary Fragmentation and the Disintegration of Analytical Frameworks

One of the most salient features of contemporary management research is its progressive fragmentation into relatively autonomous subfields—strategy, marketing, human resource management, information systems, entrepreneurship, and others—each governed by distinct theoretical traditions, preferred objects of study, and methodological conventions. While this specialization has undeniably enabled significant analytical progress, it has simultaneously produced a fragmentation of interpretive frameworks, thereby limiting the capacity of the field to develop genuinely integrative theories of organizations.

As Whittington (2006) argues, such fragmentation obstructs the construction of holistic explanations of organizational phenomena, particularly in contexts where technological, social, cognitive, and institutional dimensions are deeply intertwined. Similarly, Dumez (2013) emphasizes, within the francophone tradition, that the increasing compartmentalization of management research prevents a full understanding of organizations as dynamic and interconnected systems.

More fundamentally, this fragmentation reflects a deeper epistemological constraint: the difficulty of conceptualizing organizations as complex adaptive systems. Dominant approaches tend to isolate variables, stabilize causal relationships, and reduce uncertainty in order to make phenomena analytically tractable. Yet organizational reality is characterized by dense interdependencies, recursive interactions, and evolving configurations that resist such simplification (Morin, 2005 ; Moleka, 2024b). The resulting tension between empirical complexity and analytical reduction constitutes one of the most persistent structural weaknesses of the discipline.

2.2. Output Fetishization and the Reification of Innovation

A second major limitation lies in the tendency of management sciences to reduce innovation to its observable and measurable outputs—patents, new products, performance indicators, or technological artifacts—while neglecting the relational processes and generative conditions that make innovation possible (Segueni, 2022). This orientation is further reinforced by institutional measurement regimes, particularly those promoted by international organizations such as the OECD (2018), which privilege quantification, benchmarking, and comparability.

Within this framework, innovation becomes a stabilized and objectified entity that can be measured, ranked, and optimized. However, as Callon (1986) demonstrates, every process of measurement involves a transformation of the phenomenon itself through acts of translation. Consequently, by reducing innovation to its observable outputs, management sciences participate in a form of reification that obscures the relational, uncertain, and emergent dynamics constitutive of innovation processes.

This critique is further reinforced by Latour (2005), who shows that technological and organizational artifacts cannot be understood independently of the heterogeneous networks of actors, practices, and institutions in which they are embedded. From this perspective, innovation outputs are not autonomous entities but temporary stabilizations of broader, continuously evolving socio-technical configurations.

Thus, the fixation on outputs paradoxically impoverishes the theoretical understanding of innovation, as it privileges what is visible and measurable at the expense of what is in formation, unstable, and relationally constituted.

2.3. The Inability to Theorize Generativity and Emergent Dynamics

The third and perhaps most fundamental limitation concerns the difficulty of integrating the conceptual apparatus of complexity theory and emergence into mainstream management thinking. Although these notions are frequently invoked, their theoretical integration often remains metaphorical rather than substantive.

As Anderson (1999) highlights, complex systems exhibit emergent properties that cannot be deduced from the isolated analysis of their constituent elements. This insight directly challenges the linear causal models that continue to dominate large parts of management research. Despite this, prevailing approaches still prioritize predictability, stability, and control, thereby underestimating the inherently dynamic nature of organizational systems.

Morin (2005) has long argued for a “complex thought” capable of articulating order and disorder, stability and transformation within a unified epistemological framework. However, this perspective remains marginal within empirical management research, where methodological imperatives of validation and generalization often favor reductionist models.

Similarly, the philosophical contributions of Simondon (2024), particularly his theory of individuation, and Deleuze (1968), with his concepts of difference and virtuality, offer powerful resources for conceptualizing innovation as a process of becoming. Yet these perspectives remain largely underutilized in contemporary organizational theory.

As a result, management sciences struggle to adequately conceptualize generativity—that is, the intrinsic capacity of systems to produce novelty from within their own dynamics. This limitation severely constrains their ability to account for radical innovation, technological disruption, and deep organizational transformation.

2.4. Toward a Structural Reconfiguration: Reinscribing Management Within Generativity

Taken together, these three limitations—disciplinary fragmentation, output fetishization, and the inability to theorize generativity—converge toward a single diagnosis: contemporary management sciences remain anchored in an implicit ontology that privileges action, decision, and control, while systematically neglecting the conditions of possibility of emergence.

Overcoming these limitations therefore requires more than incremental theoretical refinement. It demands a fundamental epistemological and ontological reconfiguration of the field itself. Rather than continuing to develop increasingly sophisticated models of existing processes, management sciences must shift toward a science of the generative conditions of organizational systems.

This implies a transition from a discipline focused on the management of resources, structures, and processes to a discipline concerned with the production, maintenance, and transformation of generative configurations. It is precisely within this transformative horizon that the present article situates itself, by proposing a generative ontology of innovation and introducing the concept of generative innovation structures as a new analytical foundation for organizational theory.

3. Toward an Ontology of Innovation

If the limitations identified above are primarily epistemological, they ultimately point toward a deeper and more fundamental ontological issue that has remained largely implicit within management and organization studies: what is innovation, considered not as an empirical phenomenon, but as a mode of existence? In other words, what is innovation beyond its observable manifestations such as products, processes, capabilities, or measurable outputs? Addressing this question requires a profound conceptual displacement. It involves moving away from an ontology of innovation grounded in actuality—where innovation is equated with what is produced, implemented, or measured—and toward an ontology of generativity, in which innovation is understood as the expression of underlying potentials, structural conditions, and becoming-oriented dynamics. This shift reorients the analysis from visible effects to the deeper conditions of possibility that render emergence itself thinkable.

3.1. Innovation as an Ontological Phenomenon

In dominant management approaches, innovation is typically defined as the introduction of a new product, process, or service (Schumpeter, 1934; OECD, 2018). While operationally useful, this definition implicitly reduces innovation to its realized form, thereby conflating the phenomenon with its outcomes. Such a reduction becomes problematic once innovation is understood not as an objectified result, but as a generative capacity embedded within systems. From this perspective, innovation cannot be adequately captured as an observable state; rather, it must be conceptualized as a structural property of systems capable of producing novelty. It is therefore more accurately defined as an ontological phenomenon of generativity, in which the ability to generate difference precedes and conditions any empirical manifestation

This reconceptualization resonates with philosophical traditions that challenge static ontologies of being. Deleuze (1968) emphasizes that novelty emerges not as variation of the same, but as the production of difference in itself, while Simondon (2024) conceptualizes technical and organizational entities as ongoing processes of individuation rather than fixed objects. Extending these insights into organizational theory implies that firms and institutions should no longer be viewed as stabilized entities that innovate, but as metastable systems continuously traversed by tensions, potentials, and unresolved becoming processes that may or may not actualize under specific structural conditions (Arthur, 2009; Anderson, 1999).

3.2. Structured Potentiality as an Analytical Category

To move beyond the simplistic opposition between potential and actual, it becomes necessary to introduce the notion of structured potentiality. Unlike abstract or indeterminate conceptions of possibility, structured potentiality refers to sets of latent possibilities that are already inscribed within specific configurations of relations, constraints, and material-cognitive arrangements. These potentialities are neither infinite nor random; they are bounded, shaped, and differentiated by the internal organization of systems. In this sense, systems are not open to any possible future, but only to a finite range of trajectories that are structurally conditioned.

This idea extends classical Aristotelian distinctions between potentiality and actuality (Aristotle, 2008), while also aligning with Herbert Simon’s (1969) view of artificial systems as defined not by what they are, but by what they make possible. Within organization theory, this implies that firms differ not only in performance outcomes but in their underlying capacity architectures, which shape the space of possible innovations. Similarly, dynamic capabilities theory (Teece, 2007) can be reinterpreted not simply as an adaptive mechanism, but as a partial expression of deeper structural conditions that govern what kinds of novelty can emerge in the first place.

3.3. Innovationology as a Transdisciplinary Ontological Framework

It is within this context that Innovationology emerges as a transdisciplinary framework aimed at reconstituting the foundations of innovation theory. Rather than extending existing models, Innovationology proposes a shift in analytical level, moving from observable phenomena to the generative structures that precede and produce them (Moleka, 2025). It integrates insights from systems theory, complexity science, cognitive science, philosophy, and organization studies in order to construct a unified framework for understanding innovation as an emergent ontological process.

Three core principles define this approach. First, the principle of generativity posits that systems must be analyzed according to their capacity to produce novelty rather than reproduce stability. Second, the principle of relationality asserts that innovation emerges from configurations of relations rather than isolated entities. Third, the principle of emergence holds that innovative properties cannot be reduced to the sum of their components but arise from nonlinear interactions within complex systems (Moleka, 2026a-c). From this standpoint, artificial intelligence is not an autonomous driver of innovation but a localized expression of broader generative architectures embedded in organizational, institutional, and cognitive systems.

3.4. Theoretical Implications: From Observable Outputs to Generative Conditions

This ontological shift carries significant theoretical consequences for management and organization studies. First, it challenges the epistemic status of empirical artifacts by repositioning them as partial traces of deeper generative processes rather than self-contained objects of analysis. A technological innovation, for instance, is not simply a discrete outcome but the materialization of a specific configuration of relational and structural conditions. Second, it transforms explanatory logics by displacing linear causality with configurational reasoning, where outcomes are understood as emergent effects of interacting constraints and potentials. Third, it redefines the role of management science itself, shifting its focus from optimization of performance outcomes to the identification, modeling, and design of generative conditions that enable transformation.

4. Generative Structures of Innovation

If innovation is ontologically grounded in generativity, then the central analytical question is no longer how innovation occurs, but what kinds of structures make its emergence possible, directional, or constrained. This requires a shift from process-based or outcome-based explanations toward a structural ontology of innovation, in which the focus lies on the generative architectures that condition the production of novelty.

4.1. Formal Definition

A generative structure of innovation can be defined as a multi-layered, dynamic, and evolving configuration of relations, constraints, and latent potentials whose interactions produce, orient, and transform trajectories of novelty emergence. This definition implies that structures are not reducible to formal organizational charts, institutional arrangements, or resource endowments. Instead, they constitute relational configurations that integrate material, cognitive, symbolic, and institutional dimensions into a continuously evolving system.

The generative dimension of such structures implies that they do not merely enable innovation but actively produce it by generating bifurcations, recombinations, and emergent possibilities. Moreover, these structures are inherently recursive: the innovations they produce feed back into and modify the very conditions that generated them, resulting in continuous structural reconfiguration over time.

4.2. Core Properties of Generative Structures

4.2.1. Recursivity

Generative structures are characterized by recursive feedback loops in which outputs of innovative activity continuously reshape the conditions of their own production. This recursive dynamic implies that organizational systems are never fully stabilized; instead, they evolve through continuous self-modification driven by their own emergent outcomes. This property extends complexity theory (Arthur, 2009) by framing recursion not only as a system dynamic but as a constitutive ontological condition of generativity itself.

4.2.2. Non-Linearity

Within generative structures, causal relations are fundamentally non-linear, meaning that small perturbations may generate disproportionately large effects, while significant interventions may produce negligible outcomes. This non-proportionality undermines traditional causal models in management and necessitates analytical frameworks capable of capturing thresholds, bifurcations, and emergent discontinuities (Anderson, 1999; Stacey, 2011). Innovation, under such conditions, cannot be predicted or fully controlled; it must be understood as an emergent property of interactional complexity.

4.2.3. Self-Transformation

Generative structures possess the capacity for self-transformation, meaning that they can modify their own operational rules and internal logics through iterative processes of interaction and feedback. This aligns with Simondon’s (2024) theory of individuation, in which systems are not pre-formed entities but ongoing processes of becoming. In organizational contexts, this implies that the conditions enabling innovation are themselves continuously produced and destabilized through the system’s own evolutionary dynamics.

4.3. Typology of Generative Structures

Generative capacity varies across organizational configurations, allowing for a conceptual distinction between three ideal-typical structures.

Inhibiting structures are characterized by rigidity, low cognitive diversity, and strong reliance on established routines. These configurations prioritize stability over transformation and therefore tend to reproduce existing patterns rather than generate novelty. Innovation, when it occurs, is typically marginal or externally induced.

Neutral structures provide enabling environments for innovation without actively producing it. They are characterized by moderate openness and limited structural constraints but lack the internal dynamics necessary for sustained generativity. Innovation in such systems is episodic and largely dependent on exogenous shocks or individual agency.

Generative structures, by contrast, exhibit high relational density, cognitive heterogeneity, and a strong capacity to absorb uncertainty. In these configurations, innovation becomes a continuous systemic property rather than an exceptional event. These systems are inherently dynamic, self-reconfiguring, and capable of sustaining long-term transformative trajectories.

4.4. Analytical Consequences: From Events to Configurations

The introduction of generative structures fundamentally transforms the unit of analysis in innovation research. Instead of treating innovations as isolated events, attention shifts toward the underlying configurations that make such events possible. Explanatory emphasis moves from “why did this innovation occur?” to “what structural conditions made this emergence possible in this system but not in others?” This shift also reconceptualizes causality as configurational rather than linear, emphasizing interaction effects rather than isolated variables. Comparative analysis, therefore, must focus not on performance outcomes but on the generative capacity of systems.

4.5. Formalization of Generativity

At a more abstract level, generative structures can be represented as a function:

G = f(R, C, P)

where R represents relational configurations, C denotes structural constraints, and P refers to latent potentialities embedded within the system. Generativity (G) emerges not from any single component but from the dynamic interaction among these elements. This formulation opens pathways toward computational modeling, simulation approaches, and comparative empirical operationalization of generative capacity.

Within this framework, artificial intelligence must be reinterpreted not as an autonomous driver of innovation, but as a situated manifestation of deeper generative structures that condition its development, deployment, and effects.

5. Artificial Intelligence as an Ontological Manifestation

If the preceding sections have established that innovation and organizational transformation are grounded in deeper generative structures, then a necessary corollary follows: artificial intelligence cannot be conceptualized as an autonomous causal force driving organizational change. Instead, it must be reinterpreted as a situated ontological manifestation of more fundamental generative dynamics embedded within socio-technical, institutional, and cognitive architectures. This reframing challenges dominant managerial narratives that position AI as an exogenous technological shock, and instead situates it within a broader ecology of relational configurations that condition both its emergence and its effects.

5.1. AI as an Effect Rather than a Cause

Mainstream management and information systems literature has largely framed artificial intelligence as a primary engine of transformation, capable of reshaping productivity regimes, decision-making processes, and organizational architectures (Brynjolfsson & McAfee, 2014; Davenport & Ronanki, 2018; Agrawal et al., 2021). Within this dominant framing, AI is treated as an exogenous technological force that acts upon organizations from the outside, generating predictable gains in efficiency, optimization, and analytical capability. However, such an interpretation rests on a fundamental inversion of causality: it attributes agency to technology itself, while obscuring the structural conditions that render AI operational, meaningful, and impactful.

From a generative ontological perspective, AI systems do not precede organizational transformation; rather, they emerge from pre-existing configurations of data infrastructures, computational capacities, institutional logics, and distributed forms of coordination. As Raisch and Krakowski (2021) demonstrate, the organizational consequences of AI vary significantly across contexts, suggesting that technological effects are not intrinsic but deeply mediated by organizational design, interpretive frameworks, and governance regimes. In this sense, AI should be understood as an emergent effect of configurations characterized by high information density, scalable data accumulation, distributed computation, and increasingly networked organizational architectures. What is often described as an “AI revolution” is therefore better conceptualized as the historical intensification of latent generative structures rather than a discontinuous technological rupture.

5.2. Rethinking Organizational Uses of AI

This ontological inversion necessitates a fundamental rethinking of how artificial intelligence is conceptualized within organizational practice. Rather than treating AI as an independent variable producing uniform effects such as automation, augmentation, or substitution, it becomes necessary to examine the conditions under which these effects become possible, meaningful, and socially legitimate. In some organizational configurations, AI functions primarily as a reinforcement mechanism, amplifying existing logics of efficiency, standardization, and control without fundamentally altering underlying generative structures. In such cases, AI extends pre-existing managerial rationalities rather than transforming them.

In other configurations, however, AI may contribute to deeper relational reconfigurations, enabling new forms of coordination, distributed decision-making, and emergent creativity. Yet even in these cases, its effects remain structurally conditioned. They depend on the configuration of organizational interactions, the nature of governance regimes, the cognitive schemas of actors, and the institutional environments in which these systems are embedded (Leonardi, 2011; Kellogg et al., 2020). Consequently, AI does not generate innovation in itself; rather, it activates, amplifies, or suppresses potentialities already inscribed within generative structures. Its effects are therefore not technological in essence but configurational in origin.

5.3. Critique of Dominant AI Discourses

Contemporary discourses on artificial intelligence are frequently shaped by narratives of rupture, disruption, and radical transformation, which portray AI as an autonomous force reshaping the foundations of economic and organizational life (Marques, 2024; Agbe, 2025). While these narratives play a powerful legitimizing and mobilizing role in managerial and policy contexts, they tend to obscure the structural continuities that underlie technological change. By attributing transformative agency to AI itself, such accounts reproduce a technologically deterministic worldview that has been widely critiqued in the sociology of technology (Winner, 2017) and in critical organization studies.

From a generative standpoint, three critical re-interpretations become necessary. First, AI does not transform organizations indiscriminately; it transforms only those aspects of organizational reality that existing structures permit to be transformed. Second, AI does not introduce novelty ex nihilo but rather amplifies pre-existing relational, cognitive, and institutional dynamics. Third, AI systems simultaneously reveal and activate latent organizational capacities that may have remained previously underdeveloped or unobserved. These observations collectively undermine any simplistic causal attribution of organizational change to AI technologies themselves and instead reposition AI within a broader field of structured generativity.

5.4. AI as a Revealer of Generative Structures

Beyond its functional and operational dimensions, artificial intelligence can be understood as a privileged epistemic and ontological indicator of underlying generative structures. Rather than being merely a tool or a technology, AI functions as a revealing mechanism that makes visible otherwise implicit organizational dynamics, including the intensification of informational flows, the increasing centrality of relational architectures, and the growing importance of distributed learning systems. In this sense, AI does not simply act upon organizations; it discloses the structural conditions through which organizations already operate.

This perspective enables a more differentiated understanding of AI’s organizational effects. In inhibiting structures characterized by rigidity, low relational density, and strong hierarchical control, AI systems tend to produce limited or incremental effects, often constrained by existing governance and cognitive barriers. In neutral structures, AI may generate localized improvements or episodic innovations without fundamentally altering systemic dynamics. In contrast, within generative structures characterized by high relational complexity, cognitive diversity, and adaptive openness, AI can function as a catalyst of systemic transformation, accelerating recursive feedback loops and enabling new forms of organizational becoming.

Ultimately, this reframing positions AI not as a driver of organizational change but as a diagnostic and catalytic element within broader generative architectures. It shifts the analytical focus from technological determinism toward structural ontology, thereby reintegrating artificial intelligence into a more fundamental theory of organizational generativity.

6. Generative Organizational Architectures

If Section 5 established that artificial intelligence should not be understood as an autonomous causal driver of organizational transformation but rather as an emergent manifestation of deeper generative regimes, then a central theoretical implication follows: the locus of analysis must shift toward the concrete organizational configurations that sustain generativity. These configurations are conceptualized here as generative organizational architectures, which constitute the operational and structural substrate through which innovation is continuously produced, stabilized, and transformed within organizations. In contrast to abstract notions of generative structures, which operate at a higher level of theoretical generality, generative architectures refer to the situated, enacted, and materially embedded configurations that translate latent generative potential into observable innovative outcomes. They therefore bridge structural potentiality and everyday organizational practice, linking relational dynamics, institutional arrangements, and collective cognition into a unified systemic formation. Within this framework, artificial intelligence does not function as an independent agent of organizational change but rather as a diagnostic and catalytic device that renders visible the depth, coherence, and limits of an organization’s generative architecture (Marsault, 2023). In this sense, innovation ceases to be conceptualized as an isolated output or episodic event and instead becomes the emergent property of a multilayered system in which relational, cognitive, and institutional dimensions interact recursively to produce novelty over time (Roumy, 2022).

6.1. Definition and Conceptual Scope

A generative organizational architecture can be defined as the structured and evolving ensemble of relational patterns, organizational routines, cognitive infrastructures, and institutional dispositifs that collectively produce, sustain, and amplify an organization’s capacity for continuous novelty generation. Unlike static structural models of organization, this conception emphasizes dynamic coherence across multiple levels of organizational reality, including micro-level interactions, meso-level routines, and macro-level institutional logics. In this perspective, architecture refers not merely to formal design but to the operational ecology through which knowledge, action, and meaning are continuously recombined into emergent innovative configurations. The architecture thus acts as a mediating layer between latent generative structures and actual innovation processes, translating potentiality into enacted transformation. This interpretation aligns with contemporary relational and processual perspectives in organization theory, while extending them toward a more foundational ontology of generativity.

6.2. Conditions of Emergence

Generative architectures are constituted by three interdependent and mutually reinforcing dimensions that jointly determine the degree to which an organization is capable of sustaining innovation as an emergent and systemic property.

6.2.1. Relational Density

Relational density refers to the intensity, multiplicity, and qualitative richness of interactions among organizational actors across functional, hierarchical, and boundary-spanning domains. High relational density enables the continuous circulation of information, the recombination of heterogeneous knowledge domains, and the emergence of novel interpretive frames through interaction. Importantly, relational density is not reducible to structural connectivity; it also encompasses trust, reciprocity, and the presence of brokerage positions that enable actors to bridge otherwise disconnected knowledge domains. As Burt (2005) demonstrates, structural holes and brokerage positions significantly enhance creative recombination by exposing actors to diverse informational ecosystems, thereby increasing the probability of novel synthesis. In generative terms, relational density functions as the interactional substrate of emergence, where novelty is not transmitted but produced through relational coupling.

6.2.2. Institutional Plasticity

Institutional plasticity refers to the capacity of an organization to continuously adapt, reinterpret, and transform its formal and informal rules, routines, and governance mechanisms in response to internal and external complexity. It encompasses procedural flexibility, tolerance for experimentation, and the organizational legitimacy of failure as a learning mechanism. This dimension is critical because relational intensity alone cannot produce generativity if it is constrained by rigid institutional logics that suppress deviation and experimentation. Weick’s (1995) theory of sensemaking is particularly relevant here, as it highlights the role of retrospective meaning construction in enabling organizations to reinterpret disruption as opportunity rather than anomaly. Institutional plasticity thus constitutes the adaptive regulatory layer of generative architectures, enabling structural openness to emergence.

6.2.3. Cognitive Intensity

Cognitive intensity captures the organization’s collective capacity to generate, integrate, and transform knowledge across temporal and spatial scales. It includes mechanisms of organizational memory, knowledge articulation, and the dynamic interplay between tacit and explicit knowledge forms. Following Nonaka and Takeuchi (2009), knowledge creation is understood as a continuous process of conversion between tacit and explicit domains, enabling iterative cycles of learning and innovation. Cognitive intensity therefore operates as the epistemic engine of generative architectures, allowing organizations not only to respond to environmental complexity but also to anticipate and actively construct new possibilities of action.

6.3. Conceptual Model

Formally, generative organizational architecture can be represented as:

GA = f (RD, IP, CI)

where:

-GA = generative architecture

-RD = relational density

-IP = institutional plasticity

-CI = cognitive intensity

This formulation emphasizes that generativity is fundamentally non-linear and emergent. The interaction among these three dimensions produces disproportionate systemic effects, such that marginal changes in one dimension may trigger large-scale innovation dynamics, while deficiencies in any single dimension can significantly suppress the overall generative capacity of the system.

6.4. Managerial Implications

From a managerial standpoint, generative architectures fundamentally reconfigure the role of organizational leadership. First, innovation must be understood as an endogenous systemic property rather than an externally managed output, implying that managerial intervention should focus on shaping enabling conditions rather than controlling discrete innovation outcomes. Second, generative architectures are inherently dynamic and recursively self-transforming; therefore, managerial attention must shift toward continuous monitoring and adaptive redesign of relational, institutional, and cognitive configurations. Third, the managerial role evolves toward that of an organizational architect, responsible for orchestrating interactional density, institutional adaptability, and knowledge integration in ways that maximize systemic generativity.

6.5. Illustrative Cases

Empirical illustrations reinforce this conceptualization. Organizations such as Google demonstrate high generative capacity through dense cross-functional relational networks, institutionalized experimentation mechanisms such as time-allocation for exploratory projects, and extensive knowledge-sharing infrastructures that enhance cognitive integration (Venkataramani & Tang, 2024). Similarly, Tesla illustrates a manufacturing-oriented generative architecture characterized by real-time data integration, rapid iterative development cycles, and continuous recombination of engineering and computational capabilities (Yacoub, 2024). These cases suggest that technological intensity alone is insufficient; rather, it is the alignment of relational, institutional, and cognitive dimensions that produces sustained generativity, particularly in AI-augmented environments.

7. Epistemological and Methodological Implications

The reconceptualization of innovation as an ontological phenomenon grounded in generative architectures has profound implications for the epistemology and methodology of management science. Traditional approaches in organization studies have relied heavily on linear causal inference, variable-centric analysis, and output-oriented measurement systems. However, as Anderson (1999) argues, such approaches are fundamentally inadequate for capturing the behavior of complex adaptive systems characterized by emergence, non-linearity, and self-organization.

7.1. Toward a Post-Disciplinary Science

One of the most significant implications of a generative ontology is the emergence of a post-disciplinary epistemological stance. The fragmentation of management science into specialized subfields—strategy, marketing, organizational behavior, information systems, and human resources—has produced analytical silos that obscure the systemic interdependencies underlying innovation processes. In contrast, a generative perspective requires an integrative analytical framework capable of simultaneously capturing relational, institutional, and cognitive dynamics. This movement aligns with recent calls for complexity-oriented management science and with the emerging field of Innovationology (Moleka, 2025), which seeks to synthesize insights from complexity theory, cognitive science, systems theory, and organizational studies into a unified theoretical architecture.

7.2. Limitations of Conventional Methodologies

Conventional methodological approaches exhibit three fundamental limitations. First, they rely on linear causal assumptions that are incompatible with the recursive and non-linear dynamics of generative systems. Second, they prioritize observable outputs such as patents, financial indicators, or product innovations, thereby neglecting the underlying generative conditions that produce these outcomes. Third, they lack the capacity to capture contextual embeddedness, particularly the relational density, institutional plasticity, and cognitive intensity that define generative architectures.

To address these limitations, a shift toward hybrid and multi-method research designs is required. Simulation-based approaches, particularly agent-based modeling, enable the exploration of emergent macro-patterns arising from micro-interactions (Epstein, 2012). System dynamics modeling provides a formal representation of feedback loops and non-linear interactions between organizational variables (Sterman, 2002). Complementarily, intensive qualitative methodologies grounded in process theory allow for the fine-grained analysis of routines, sensemaking processes, and tacit knowledge flows (Weick, 1995). Together, these approaches constitute the basis for an experimental epistemology of emergence, in which research is not limited to measurement but becomes a tool for exploring generative potentialities within organizational systems.

8. Managerial Implications

If the previous sections have established that innovation is an emergent effect and that generative architectures constitute its ontological framework, this leads to major implications for management practice.

8.1. The Manager as an Architect of Conditions of Possibility

The classical managerial function—centered on decision-making, optimization, and control—becomes insufficient in a context of generativity. The manager is no longer merely a decision-maker but an architect of conditions of possibility. This means that the managerial role consists in:

-Creating and sustaining relational density: fostering interdisciplinary collaboration, internal and external networks, and the circulation of tacit knowledge.

-Developing institutional plasticity: encouraging experimentation, learning from failure, and the continuous revision of rules and procedures.

-Strengthening cognitive intensity: supporting continuous learning, organizational memory, and the capacity to combine tacit and explicit knowledge.

Thus, the managerial role shifts from resource management to the design of enabling conditions for emergence, orchestrating structures, practices, and collective cognition simultaneously.

8.2. Rethinking Strategy, Innovation, and Transformation

Strategy is no longer limited to setting objectives and planning their achievement. In a generative organization, it becomes an emergent and adaptive process that leverages interactions between actors and technologies to create new opportunities. Innovation ceases to be an isolated output and becomes an endogenous property of the organization, while organizational transformation is understood as a continuous recombination of existing potentials.

Managerial practices must therefore integrate tools and methods that support systems thinking, simulation, and co-creation, rather than relying exclusively on traditional performance indicators. Technologies such as artificial intelligence then act as amplifiers of generativity, but their effectiveness depends entirely on the pre-existing organizational architecture.

8.3. Implications for Governance and Leadership

Leadership in a generative organization involves:

-Systemic vision: understanding complex interactions and feedback loops between individuals, units, and technologies.

-Capacity to orchestrate plasticity: dynamically adjusting rules, resources, and routines in real time.

-Promotion of a culture of experimentation: valuing intelligent risk-taking and continuous learning.

This approach transforms governance into an adaptive and exploratory process, where supervision is less about control and more about enabling emergence and maximizing generative conditions.

9. Conclusion: Rethinking Management Sciences

This analysis highlights the need for a conceptual and methodological revolution in management sciences. Starting from the recognition of structural limits in contemporary approaches—disciplinary fragmentation, fetishization of outputs, and inability to account for generativity—we have proposed an ontology of innovation in which innovation is no longer a simple observable product but an ontological phenomenon embedded in structured potentials.

9.1. From Management to Generativity

The central shift in this re-foundation lies in a change of perspective: management is no longer primarily an activity of control and planning, but becomes a science of generativity, focused on producing and structuring the conditions of innovation. This transition involves three fundamental shifts:

• Epistemological shift: management science becomes post-disciplinary and integrative, capable of modeling complexity, emergence, and non-linear dynamics.
• Methodological shift: classical survey and measurement tools are complemented by simulation, complex systems modeling, network analysis, and exploration of potential states.
• Managerial shift: the role of the manager evolves from decision-maker to architect of conditions of possibility, orchestrating relational density, institutional plasticity, and cognitive intensity to maximize generativity.

In other words, management is no longer centered on controlling outcomes but on creating conditions for innovation and transformation to emerge autonomously and sustainably.

9.2. Proposal for a Global Scientific Program

Based on these theoretical foundations, a global scientific program for rethinking management sciences can be structured as follows:

• Mapping generative structures: analyzing and formalizing relational, institutional, and cognitive architectures that enable generativity.
• Integrating AI as a revealing mechanism: studying AI not as a causal driver but as a tool for measuring and amplifying organizational potentials.
• Innovative research methodologies: developing hybrid approaches combining agent-based simulation, systems dynamics modeling, intensive qualitative analysis, and dynamic network visualization.
• Ontology of innovation theory: formulating conceptual models that treat innovation as an emergent, recursive, and self-transforming phenomenon.
• Managerial applications: designing leadership and governance frameworks focused on shaping conditions of possibility rather than managing traditional resources and outputs.

This program opens the way to a management science capable of addressing complexity and long-term organizational transformation, while treating technological innovation as a catalyst rather than an autonomous cause. It represents a major step toward establishing Innovationology as a transdisciplinary field capable of producing original and radically innovative knowledge with concrete applications for 21st-century organizations.

9.3. Future Perspective and Horizon

By repositioning innovation at the core of organizational science, this work proposes a vision in which generativity becomes the central criterion for evaluating and guiding organizations. The implications are multiple: for research, it opens a field of integrative models and complex methodologies; for management, it transforms the role of leaders and strategic design; for society, it fosters the emergence of resilient, adaptive, and creative organizational systems.

Thus, the re-foundation of management sciences is not merely about introducing new tools or concepts, but about redefining the discipline itself so that it becomes capable of thinking and sustaining systemic generativity, continuous innovation, and long-term transformation in a complex and uncertain world.