
Navigating Complexity,
Driving Transformation
Insights and Tools for Human-AI Co-Creation and Beyond
Designing Ecosystems for the Age of Creative Relationality

About the book
Dynamic Relationality Theory of Creative Transformation offers a novel framework for understanding and navigating the complexities of machinic ecosystems grounded in life-experiences. Integrating insights from philosophy, mathematics, and interdisciplinary studies, DRT challenges conventional views of systems as static entities by reimagining them as dynamic, relational assemblages. This ethico-epistem-ontological approach bridges theoretical depth, methodological rigor, and practical applicability to address the challenges of our interconnected, digitalized world.
In a Nutshell
Theory
DRT draws on Deleuze and Guattari’s assemblage theory and advances concepts like counter-actualization, relational ontology, and rhizomatic system dynamics. It emphasizes the fluidity and co-evolution of human and artificial intelligences, introducing the concept of Machinic Life-Experience Ecosystems (MLXEs) to explain the interplay of virtual and actual dimensions in shaping emergent phenomena.
Methodology
DRT innovates by applying category theory, differential topology, and gauge theory to model relational dynamics across scales. Tools such as functors, sheaves, and diagrammatic logic provide a structured lens to analyze transformations within and between ecosystems. These mathematical insights offer a unique means to explore the evolution of organizational forms, stakeholder interactions, and socio-technical systems.
Practice
DRT equips leaders, researchers, and designers with tools to foster co-innovation, strategic architecturing, and adaptive ecosystem design. From healthcare systems to digital platforms, DRT demonstrates how creative transformation can emerge from aligning human values, technological capabilities, and relational dynamics.
Reviews
“AI is disrupting the modern enterprise ecosystem, raising many unforeseen opportunities for companies, but also posing relevant challenges for managers, who not always know how to best integrate AI within their strategic decision-making processes and how to effectively transform their organizations. The new book by Ozcan and Ramaswamy presents Dynamic Relationality Theory, a much-needed theoretical framework which models how organizations can be transformed through co-intelligence. The depth and rigor of the book makes it an essential reading for scholars. However, the very interesting practical examples will also help managers to navigate the complexities of the co-intelligent business landscape.”
Oriol Iglesias, Professor of Marketing, ESADE – Universitat Ramon Llull (Spain)
“Dynamic Relationality Theory of Creative Transformation presents a sophisticated and timely theorisation of evolutionary change within machinic life-experience ecosystems. Building on the concept of counter-actualisation and the MLXE framework, the authors illuminate the entangled dynamics of human–machinic relationality across diverse matrices. Particularly compelling is the book’s applicability to patient care, where complex, adaptive interactions among technology, clinicians, and lived experience increasingly shape therapeutic pathways. By demonstrating how technological platforms can be strategically integrated with existential territories and embodied experience, this work offers a vital contribution to posthuman thought, systems theory, and creative transformation across technological, organisational, and existential domains.”
Cristian Ortiz-Villalón, MD PhD PhD MBA, Karolinska Institute (Sweden)
“Moving from an ontology grounded in fixed essences to one attuned to relations and processes is the defining feature of all contemporary social and political theory. Yet this move continues to bedevil scholars concerned to map new empirical coordinates for the social sciences. Dynamic Relationality offers a welcome breakthrough by presenting a systematic guide for empirical inquiry equal to the grand challenges of the age. From generative AI and machine learning to robotics and synthetic biology, Dynamic Relationality Theory sets out a new empiricism for the assemblage, presenting critical new tools for thinking about the transformations of work, life and culture.”
Cameron Duff, Professor of Politics and Organisation, Centre for Organisations and Social Change, RMIT University (Australia)
“The book offers an emergent theory of Dynamic Relationality. It pulls both from the authors’ own impressive academic track record but also from a deep philosophical legacy. It is both timely and relevant in its treatment of interaction, experience and assemblage in the context of exploring the synergies between human and artificial intelligence. The level of the book is ambitious and will undoubtedly stimulate new and innovative research in relational dynamics. Their ideas of Machinic General Intelligence (MGI) and Machinic Life Experience Ecosystem’ (MLXE) are new and interesting and I am really interested to read further.”
Richard Ian Gyrd-Jones, Professor of Marketing, Center for Tourism and Hospitality Management, Copenhagen Business School (Denmark)
“I have been deeply impressed with the depth of thinking and rigor that both Kerimcan Ozcan and Venkat Ramaswamy have put into Dynamic Relationality Theory. I believe that the concepts outlined here can serve as the foundation for any future thinking related to the intersection of value creation, AI and other key technologies (blockchain, distributed ledger technology, etc.). The ideas presented here are powerful, and this book is must reading for anyone truly serious about the future of value measurement and value co-creation in technologically-enhanced environments and systems.”
Philip Sugai, Professor of Marketing and Director of Value Research Center, Doshisha University (Japan)
“Ozcan and Ramaswamy span an impressive theoretical umbrella across digital ecosystems, (generative) AI, and organizational transformation. In doing so, they achieve a remarkable conceptual comprehensiveness and cohesiveness that covers technological, machinic, and strategic aspects just as much as human, (co-)creative, and experiential dimensions—not in isolation but with an explicit focus on their underlying intricate interactions and interdependencies. While grounded in the fundamental philosophies of Deleuze and Guattari, Ozcan and Ramaswamy never lose sight of the practical and pragmatic, applying their foundational theory to crucial sectors like food, health, and education, and deriving important implications for sustainability, well-being, and welfare.”
Julian R. K. Wichmann, Assistant Professor of Marketing, Tilburg University (Netherlands)
DRT Use Cases
Interdisciplinary Research and Systems Modeling
DRT bridges disciplines by integrating assemblage theory, category theory, and differential topology to model complex systems. It appeals to researchers seeking to understand transformations across ecosystems, including environmental, technological, and societal systems, enabling innovative research in emerging interdisciplinary fields.
Organizational Morphogenesis and Transformation
Using tools like functors and colimits, DRT enables the modeling of organizational evolution as dynamic ecosystems. It helps businesses transition from rigid structures to adaptive, co-innovative entities by aligning local processes (molecular) with global strategies (molar). This application is particularly relevant for consulting on change management and digital transformation.
Strategic Ecosystem Design for Digital Platforms
DRT provides tools like diagrammatic logic and category theory to design and optimize digital platforms such as e-commerce marketplaces, AI-driven applications, or social media ecosystems. By modeling stakeholder interactions as dynamic assemblages, organizations can identify opportunities for co-creation, improve user engagement, and enhance platform scalability.
Human-AI Collaboration and Co-Creation
DRT’s concept of Machinic Generalized Intelligence (MGI) offers a roadmap for human-AI co-evolution, emphasizing co-creativity and ethical interaction design. It provides insights into aligning AI systems with human cognitive processes to foster innovation in industries such as autonomous vehicles, generative AI, and interactive systems.
Ethical Frameworks for AI and Digital Ecosystems
DRT introduces a value-based, relational approach to AI ethics, grounded in its ethico-epistem-ontological framework. It provides a systematic way to evaluate the societal impacts of digital systems, balancing innovation with ethical considerations, particularly in governance and regulation of AI ecosystems.
Healthcare Systems Innovation
DRT applies relational dynamics and methodologies like sheaf analysis to healthcare ecosystems. It helps analyze patient-provider interactions, adapt care pathways, and design personalized, data-driven health interventions. The framework is particularly useful for addressing systemic challenges, such as during crises like COVID-19, by visualizing complex interdependencies across stakeholders.
DRT in Detail
Part 1
Relational Dynamics
Chapter 1 introduces the foundational principles of Dynamic Relationality Theory (DRT), emphasizing the interplay between the Virtual (potentialities) and the Actual (realized possibilities) as the driving force behind creative transformation. It establishes that these dimensions are interdependent, with actualization transforming potentialities into realities and counter-actualization reshaping the Virtual to foster novelty and innovation. The framework situates DRT within the four domains of the creative plane of immanence: Existential Life Territories (identity formation), Energetic-Signaletic Flows (dynamic transitions), Incorporeal Universes (abstract guiding forces), and Abstract Machinic Phyla (systemic interconnections). These domains highlight how ecosystems adapt and evolve through deterritorialization (breaking boundaries) and discursivity (redefining meaning).Through double articulation, assemblages balance stability and fluidity, enabling co-evolution of content and expression. Integrating gauge theory, the framework provides a mathematical model for analyzing transformations in ecosystems. Chapter 2 expands the theory by introducing Machinic Life-Experience Ecosystems (MLXEs), where human and machinic intelligences co-create adaptive systems. It emphasizes the interdependence of actualization and counter-actualization and explores how life-experiences shape dynamic ecosystems. MLXEs are presented as vibrant, relational systems bridging the tangible and intangible. Applications in healthcare demonstrate the utility of DRT in fostering personalized, interactive ecosystems that enhance patient outcomes and organizational efficiency. Part 1 concludes with a forward-looking exploration of DRT’s capacity to analyze and guide adaptive ecosystems, laying the foundation for subsequent discussions on dynamic relationalities.

Part 2
Dynamic Relationality Theory
Part 2 establishes the conceptual and technical foundations for analyzing relational dynamics within and across ecosystems. It introduces Dynamic Relationalities, describing how systems evolve through interactions within assemblages, across strata, and between stacked strata. Chapter 3 begins with Dynamic Relationality #1, emphasizing localized interactions within assemblages. Drawing on category theory, smooth manifolds, and vector fields, it models assemblages as dynamic configurations animated by machinic desire. Processes of territorialization and deterritorialization shape the plane of consistency, balancing cohesion with adaptability. Chapter 4 extends this to strata, layers of machinic and collective assemblages. Dynamic Relationality #2 explores the stabilization of relationships through territorialization, while Dynamic Relationality #3 examines lines of flight, transformative pathways connecting strata. Functorial transformations and transversality provide mathematical tools to analyze interactions across strata. Chapter 5 focuses on stacked strata, introducing Dynamic Relationalities #4 (becoming through lines of flight) and #5 (reterritorialization). These concepts explain how systems adapt through the interplay of disruption and stabilization. The integration of Machinic Generalized Intelligence (MGI) highlights co-creativity between human and machinic intelligences, fostering innovation. Chapter 6 explores relational dynamics themselves, introducing Dynamic Relationality #6 (differential transformation). Using differential forms, homotopies, and a new monadology, it models continuous systemic evolution. Applications to AGI illustrate how these frameworks guide adaptive and ethical development. Part 2 provides rigorous theoretical and mathematical tools for analyzing and guiding transformation, equipping researchers and practitioners to understand complex, multi-layered ecosystems.

Part 3
Organizations and Creative Transformation
Part 3 focuses on the dynamics of creative transformation within and across organizations, using category theory and advanced relational frameworks to model and guide systemic evolution. It introduces critical tools and concepts for understanding how organizations align local (molecular) and global (molar) processes to foster adaptability and innovation. Chapter 7 explores Dynamic Relationality #7, focusing on molar/molecular segmentation to analyze how global transformations influence local actions and vice versa. By applying sheaf morphisms, it contextualizes local data within global structures, ensuring coherence across stacked strata. The concept of transversality is introduced to map transformations across layers, balancing systemic stability and adaptability. Chapter 8 introduces diagrammatic logic, using category theory to model organizational ecosystems as systems of relationships and transformations. Dynamic Relationality #8 emphasizes the role of limit objects (stable configurations) and colimit objects (growth potential), extending the analysis to higher-order categories for mapping complex ecosystem interactions. The chapter includes a healthcare case study demonstrating how diagrammatic tools optimize resource allocation and stakeholder collaboration. Chapter 9 examines organizational morphology and morphogenesis, classifying organizations into structured corporations, agenced corporations, structuring collectives, and agencing collectives. These forms are analyzed through limit/colimit dynamics, emphasizing the balance between stability and innovation. Chapter 10 focuses on architectural transformation in ecosystems. By extending sheaf theory to stacks, it enables multi-layered analysis of global-local dynamics. Dynamic Relationality #9 highlights strategic architecturing as a tool to design adaptive life-experience ecosystems, with a healthcare case study showcasing its application. Part 3 equips researchers, leaders, and designers with advanced tools for aligning relational dynamics and fostering systemic resilience.

Part 4
Complex Transformative Emergence and Evolution
Part 4 focuses on the emergent and evolutionary dynamics of Machinic Life-Experience Ecosystems (MLXEs). It examines how these ecosystems transition from virtual potentialities to actual realities while continuously evolving, emphasizing the role of immanence and relational dynamics in shaping systemic transformation. Chapter 11 explores emergent transformation, highlighting immanence as the self-contained capacity of MLXEs to evolve through relational interactions. It introduces a monadic framework differentiating ecosystems into four domains: existential territories, energetic-signaletic flows, abstract machinic phyla, and incorporeal universes. Through double articulation, MLXEs balance stabilization and innovation, enabling continuous adaptation. A case study on healthcare’s response to COVID-19 illustrates how global policies and local practices dynamically interact to manage crises and foster resilience. Chapter 12 addresses evolutionary transformation, applying gauge theory to model the dynamics of social-behavioral phenomena. It introduces a framework linking global immanence to societal outcomes such as well-being, wealth, empowerment, and welfare. The chapter emphasizes Dynamic Relationality #10, where immanent processes drive systemic emergence and evolution. By leveraging tools like virtual-to-actual differentiation and immanence-based strategies, practitioners can design adaptive, co-creative ecosystems. Part 4 integrates advanced mathematical tools with philosophical and practical insights, offering actionable frameworks for guiding transformative evolution in complex systems. It equips researchers, policymakers, and practitioners with strategies for fostering adaptability, resilience, and co-creative growth in dynamic ecosystems.

Authors

Kerimcan Ozcan

Venkat Ramaswamy
More about the authors
Dr. Kerimcan Ozcan is an Associate Professor of Marketing at the School of Business and Global Innovation, Marywood University, USA. His expertise encompasses co-creation, interactive platforms, digitalization, strategy, and various facets of marketing such as branding, customer service, and industrial/B2B marketing. His current work emphasizes the fusion of human-AI interactions and their transformative impact on organizational and societal systems. This research highlights how the dynamic relationalities of machinic ecosystems and life-experiences coalesce into strategic organizational transformations, helping organizations navigate their evolving ecosystems. Forthcoming contributions extend the current work into real-world applications, with projects that bridge theoretical advancements with hands-on strategies for organizational growth, including strategic engagement with AI, co-innovation platforms, and ecosystem-based value creation. Dr. Ozcan’s prior scholarly work includes The Co-Creation Paradigm co-authored with Dr. Ramaswamy, published by Stanford University Press, along with contributions to Journal of Marketing, Journal of Business Research, International Journal of Research in Marketing, and Harvard Business Review. Previously, he taught at several other universities (including University of Michigan and International University of Japan) and held engineering roles in various industries. His research has garnered support from entities such as Japanese Ministry of Education, Science, and Technology, and the University of Michigan Tauber Manufacturing Institute. Dr. Ozcan received a Ph.D. in Marketing and an M.A. in Applied Economics from the University of Michigan (Ann Arbor), an M.S. in Management from Georgia Institute of Technology, and a B.S. in Electrical and Electronics Engineering from Boğaziçi University.
Dr. Venkat Ramaswamy serves as Professor of Marketing at the Ross School of Business, University of Michigan, Ann Arbor, USA, contributing to innovation, strategy, marketing, branding, IT, operations, and organizational behavior. His scholarly work and practical implementation of ideas have earned him global recognition. In 2004, Ramaswamy, alongside C.K. Prahalad, introduced the concept of Co-Creation in The Future of Competition (Harvard Business School Press) offering a new perspective on value generation through experiential environments and collaborative practices. This concept was further explored in influential articles in Harvard Business Review and Sloan Management Review, focusing on interactive value creation and innovation from an individual experience standpoint. From 2005 to 2010, Ramaswamy’s research addressed the impact of digital and social media, technological convergence, and IT-enabled services on organizational engagement platforms, emphasizing the importance of using lived experiences to enhance interactions, as discussed in “Building the Co-creative Enterprise” (Harvard Business Review). In 2010, The Power of Co-Creation (Free Press) co-authored with F. Gouillart, examined co-creation platforms across various industry sectors, highlighting the transition from traditional product offerings to interactive, value-creating platforms. His 2014 book, The Co-Creation Paradigm (Stanford University Press) co-authored with K. Ozcan, advocated for a shift in individual and institutional engagement to foster a collaborative economy and society. Dr. Ramaswamy’s ongoing research focuses on systemic transformations through digitalized societal ecosystems, aiming to enhance wellbeing, wealth, and welfare. His mentorship extends globally, guiding enterprises in adopting co-creative practices and building management capabilities. Dr. Ramaswamy holds a Ph.D. in marketing from Wharton School of University of Pennsylvania and a B.Tech. in Mechanical Engineering from Indian Institute of Technology.
