10X ORG – A manager’s guide to elevate business performance with people and AI

The book uses two intertwined layers. In the first layer, we follow Hanna and her colleagues as they navigate real tensions around AI, performance, learning, and organizational design. The story regularly pauses for theory sections that unpack what is happening and connect it to real-world organizations. Each section explains a principle of 10X Org: own, not rent as the backbone; drive performance with org design to make ownership concrete; shape intelligence with mandates to translate design into performance; fit for purpose to support intentional design; change through a learning loop to enable continuous evolution; follow the value – learn and deliver to avoid endless reorganizations; make the org goal win to prevent local optimization; embed multi-learning to unlock human potential; and accelerate with strategic AI to support the chosen direction.

Organizational design

Strategy sets the intent, organizational design provides the enabling structure, and performance emerges from their alignment. The authors build on Jay Galbraith’s Star Model. They emphasize five core elements: design criteria, structure, processes, rewards, and people. These elements form a star-shaped system that highlights their interdependence. When the elements align and reinforce one another, performance becomes more reliable and less accidental. When organizational design aligns with strategy, it acts as a force multiplier.

Org topologies

Org topologies (OT) offers an approach to elevate performance through people-centered, strategy-driven, and AI-ready design. The OT map, a 2×2 grid, provides the core visual. The horizontal axis represents the scope of the skills mandate, from narrow to broad. It shows how many skills a unit possesses and is authorized to apply. The vertical axis represents the scope of the work mandate, also from narrow to broad. It shows the type of work a unit is trusted to handle.

In the lower half of the map, teams execute narrowly defined tasks with clear inputs and outputs, without needing to understand the bigger picture. In the upper half, units own outcomes. They make decisions, weigh trade-offs, and shape the work itself. The OT map creates a shared language to understand how work is structured, how it aligns or misaligns with strategy, and which structural shifts improve performance sustainably.

Each organizational unit carries a mandate: a bundle of authority and responsibility to decide, explore, act, and deliver results within a defined scope. Narrow mandates limit problem-solving and create dependencies, coordination overhead, and delays. Broad mandates enable units to take ownership of broader outcomes with fewer handoffs. This capability – what an organization can collectively see, learn, and solve – defines its organizational intelligence, and mandates set its ceiling.

Combining skills and work mandates in the 2×2 reveals four forms of organizational intelligence: doing, directing, delivering, and driving intelligence. These combine into three common org topologies: resource topology, delivery topology, and adaptive topology.

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The Resource Topology

The resource topology behaves like a mechanical system. It relies on frozen functional archetypes, each specialized in a single domain. Its purpose is to maximize resource utilization. Organizations often implement this topology through resource management approaches, where managers plan and monitor allocation and utilization.

The Delivery Topology

The delivery topology behaves like a structured network. It improves flow by reducing dependencies and typically forms cross-functional teams. These teams enable smoother delivery of fully completed work. In some domains, this resembles a feature factory, where teams produce a continuous stream of features, with a strong focus on outputs and local efficiency.

The Adaptive Topology

The adaptive topology behaves like a living ecosystem. It integrates directing, doing, and delivering into unified units. Humans, AI agents, and robots collaborate on complex problems and continuously adapt. These units explore, learn, and steer themselves. The goal is adaptiveness and customer-centricity, enabling continuous learning and the delivery of customer value.

MADE-Repeat

The org topologies change method builds on the Star Model by adding a continuous loop: map, assess, design, elevate, and repeat. The MADE method functions as a learning system grounded in systems thinking. Organizational change is not purely technical. It directly affects people’s sense of competence, status, and control. Any redesign inevitably touches identity, power, and relationships.

The appendix includes around 30 elevating Katas. These structured, repeatable routines translate design intent into daily behavior. They provide practical ways to reshape structure, mandates, roles, processes, and incentives while learning from real work. Teams mainly apply them in the elevate step of the MADE method.

To support reflection, analysis, and learning, the book introduces Aiden, a specially trained custom GPT that helps apply the concepts in practice.

Conclusion

This book presents a coherent and practical approach to organizational design by combining theory, narrative, and actionable methods. It clearly shows how mandates, structure, and learning loops shape performance and organizational intelligence. The strength lies in linking strategy, design, and execution through the OT map and the MADE cycle, supported by concrete practices such as Katas. At the same time, the authors make clear that organizational change is not just structural but deeply human. By positioning design as a continuous learning process and integrating AI as an accelerator, the book offers a compelling perspective on how organizations can evolve toward higher performance in a sustainable way.