How AI breakthroughs really happen

2026 Summer Edition: The interaction between Network Leadership and the challenges of successful AI implementation is the focus of this Edition of The Ripple. Based on the 7 Practices of a Network Leader, the 10

10 Questions every Leader must ask about AI How AI breakthoughs really happen

The RIPPLE Reflections on Network Leadership and Effective Collaboration

Summer Edition 2026

What if the greatest risk in AI is not artificial intelligence, but organizational intelligence?

Organizations are pouring billions into AI, yet many projects stall in pilots or deliver only incremental gains.

Perhaps the problem is not the models we buy or the code that is written Perhaps it is organizations into which we bring AI models or code

AI never arrives in a vacuum

It lands inside existing networks of relationships, trust, conversations, and shared meaning. It is these networks that decide whether AI amplifies human judgment or quietly erodes it.

In this series that reflects on the relationship between AI and Network Leadership, we explore a divergent question:

What kinds of organizations allow AI to flourish?

Drawing on Sandy Pentland's research on human learning networks together with the principles of Network Leadership, the message of this Summer Edition of The Ripple asserts that: The AI winners will be those who build the strongest human networks around AI, not simply the most advanced algorithms. As leaders let us begin to ask a different question than whether AI will change our organizations. Let us begin by asking the question: How will we redesign our organizations so that AI makes us wiser, not just faster? Our reflections in this Edition’s newsletter were written to disrupt a number of assumptions about AI and spark conversations which leaders and the people throughout their organizations eager to have.

Table of Contents 1 2 3 4 5 6 AI adoption rarely fails because of data or capability Pages 9 -10 Are you seeing the entire AI system - or just the software? Pages 5 - 8 Influence determines if AI will be adopted (or not) Pages 11 - 14 Participation is the real driver of sustainable AI adoption Pages 15 - 17 AI breakthroughs occur at the intersections where distinct worlds collide Pages 18 - 20 Networked systems rarely behave in neat, linear patterns Pages 20 - 24 The leader’s role to care for and shape the human side of AI Page 25 -27 A Paradigm Shift in Leadership Page 31 Seven Practices enable leaders to effectively undertake AI challenges Page 28 9 10 The 10 questions every leaders should be asking about AI Page 29 - 30 7 8

Successfully navigating in a world of flux: Network Leadership Page 32

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Are you seeing the entire AI system - or just the software?

If intelligence emerges through networks, why are so many leaders still managing AI in silos?

Practice #1 See the System

This is where many AI efforts quietly begin to break down. Leaders invest in promising use cases, track performance by team, and optimize pilots for local success. And then they wonder why adoption stalls and momentum does not spread. The problem is not usually the technol- ogy. It is that AI does not scale at the level of projects.

Its success depends on how people learn, how ideas travel, and how decisions form across the network. If leaders are not seeing those dynamics, they are often missing the forces that actually determine whether AI takes hold.

Beneath every AI initiative, three forces are always at work

People are shaping meaning through the stories they tell about what AI is and what it means. Connections across boundaries determine whether ideas move beyond isolated pockets. Shared understanding ultimately determines what gets adopted and what quietly fades. These dynamics will not be found in a dashboard. However, they often make the difference between isolated wins and system-wide transformation. Seeing the System means shifting attention from projects to patterns of interaction, from outputs to flows of learning, and from the formal orga- nizational chart to the real networks through which influence and insight travel.

These question are not secondary

They are central to whether AI spreads. Many leaders never look here. That is why AI can remain impressive in pockets, yet invisible at scale. Leaders who see the system do not simply push harder. These leaders intervene differently. They strengthen connections, amplify learning flows, and remove friction that prevents ideas from traveling.

They understand something fundamental:

It means asking different questions

Where does experience actually move across the organization, and where does it get stuck? Who connects worlds that would otherwise remain separate? Where is alignment genuine, and where is it merely compliance?

Y ou do not implement AI into an organization. You introduce AI into a living system.

The system then decides what happens next.

AI does not fail because people do not know enough

AI fails because people stop asking powerful questions

Practice #2 Lead with Curiosity

Most organizations are built to reward answers.

Clear answers signal expertise. Fast answers signal competence. Confident answers signal leadership Over time, this framework creates a culture where the goal is not to explore, but to respond.

summarizes, recommends, predicts and optimizes. By doing so, it creates the impression that the thinking has already been done.

Here is where things begin to go wrong

AI fits seamlessly into this pattern. It produces answers instantly as well as

When teams move too quickly from questions to answers.

What is lost when teams move too quickly from questions to answers? The most important step is often skipped: To understand what they are actually trying to learn

Instead of expanding thinking, AI starts to reinforce established thinking patterns.

Consider a common strategy scenario:

Where might the model be blind because the future will not look like the past? The strategy becomes highly opti- mized by what is already known but is less capable of discovering what comes next.

A leadership team uses AI to analyze market trends, competitor moves, and customer data to define its next strategic priorities. The AI system identifies the most attractive seg- ments, highlights patterns in past success, and recommends where to focus.

This is the process by which AI accelerates assumptions

The output is clear, data-backed and convincing

Without powerful questions, AI does not deepen thinking. It scales what- ever thinking already exists, including its blind spots. If intelligence emerges through networks, it is curiosity that keeps these networks alive . Curiosity fuels exploration across boundaries by inviting people people to share not just conclusions but experiences.

The team aligns quickly on this recommendation. The result of the adoption of the AI’s recommenda- tion, is that the team stops asking more challenging questions.

What assumptions are we carrying forward from the past? What emerging signals do not not yet fit the pattern?

AI adoption rarely fails because of data or capability

AI fails because people stop trusting what happens to their contribution once it enters the system

Practice 3 Foster Trust

When this loss of trust happens, people stop contributing openly.

Most leaders treat trust as a “soft” cultural condition. It is seen as some- thing important. However, somehow trust is secondary to strategy, structure, and tools.

When trust weakens, the system does not collapse. It quietly constricts as information flow becomes more selective. People become cautious and, as a result, learning slows long before anyone notices it emerging in the performance metrics.

In networked organizations, trust is not “soft”

Trust is infrastructure

Why trust - not technology - is the foundation for AI adoption This weakening of trust becomes especially visible in strategic decision-making

A leadership team introduces AI to improve strategy formation by integrating data across business units. The intent is clear: Better visibility, advance faster synthesis, and foster more aligned decisions.

When an environment lacks trust, the AI may become even more all- encompassing. However, the reality AI has to sustain becomes less honest. Trust is quietly reshaping and downgrading intelligence. Intelligence emerges through networks. It is trust that deter- mines whether networks reflect reality or a “managed” version of it. Trust shapes whether uncertainty travels freely, whether early signals are shared without fear, and whether cross-boundary learning shows up in real time or only after the fact. Leading with trust requires more than encouraging openness. It requires actively designing environments where people do not have to protect themselves from the system they are feeding.

This intent works initially

However something subtle begins to shift.

Business units start to filter what they share. It is not because the business unit disagrees with the initiative. They have become unsure how their data will be interpreted, compared, or used in future evaluations. The system becomes filled with carefully curated inputs over time. Nothing is explicitly withheld. However, everything converts into slightly softened, slightly shaped, slightly safer inputs.

Influence determines if AI will be adopted (or not)

Even the strongest ideas rarely fail because they are wrong

Practice #4 Activate Connectors

More often they ideas fail because the ideas never really travel throughout an organisation. AI initiatives are still treated as rollout exercises by many organizations. A central team designs a solution, defines the process, and distributes it across the organization. The expectation? Adoption will naturally follow if the value is clear enough.

However, change does not spread that way in a networked organization.

Ideas move through people, not through structure

More precisely, ideas move through connectors

Who are the connectors in your organization who will promote AI?

Connectors are often not the people one thinks they may be: senior or visible individuals Connectors are the people who move easily across boundaries and connect teams, disciplines, and communities. These individuals translate ideas from one context into another. Moreover, connectors support others to make sense of what something means in their own business domain. Even strong initiatives tend to stall when connectors are not involved in the process. AI implementation initiatives makes the role of connectors especially visible Imagine a company that is introducing an AI-driven decision support tool. This AI is designed to improve the organization’s operational efficiency across multiple functions. The AI system is technically strong and the pilot results are promising. Leadership expects rapid scaling across the organization.

However, adoption across functions remains uneven. Some teams integrate this AI-driven decision support tool quickly and begin to see value. Others experiment with the tool briefly but eventually disengage while a few teams or functions never really interact with the tool at all. What looks like resistance is often something more subtle: Disconnection There are usually informal connectors at work in teams where adoption of an AI practice takes hold. These informal connectors are the people who sit between functions, are trusted across groups, and help translate what the tool genuinely means for a work practice. They do not just communicate the change.

The connectors in your organization can make (or) break an AI initiative

These informal connectors help to interpret, adapt, and make the change usable in a business context. On the other hand, the system fragments where connectors are not active. This fragmentation occurs not because people reject an idea, but because the idea itself never fully arrives in a form that people can employ.

Leading through connectors requires a shift in how change is understood by an organisation

Intelligence emerges through networks of people

Adoption is not primarily a communication issue but a relational one

Connectors are the people who enable this intelligence to circulate

The leadership of one organization decided to initiate its AI implemen- tation by mapping informal influence patterns across teams rather than launching a broad AI rollout. Individ- uals who were frequently sought out for information or support across functions were identified. The trust of the judgement of these individ- uals by others was the key selection criteria, not their role. Once identified, these individuals were brought into the AI process early. They were targeted as co-

What does the activation of a connector predict?

A shift away from formal rollout structures. It is the informal path- ways through which influence actually travels that are adopted instead. Activating this shift requires identifying the people who naturally bridge boundaries. It also implies involving these individuals at the very beginning of an implementation process - not just after adoption slows down.

Connectors make successful AI implementation possible

shapers of the AI system, not as end users. These connectors enabled the organization to interpret the AI tool in a spectrum of different contexts. This led to a swift surfacing of mis- understandings. Additionally, the connectors served to disseminate not only the results of experimen- tation but also the examples of best practice use of the AI tool throughout separate parts of the organization.

What followed was not immediate uniform adoption

What looks like resistance to AI

It was something more powerful: An organic diffusion of AI

is often something much more subtle:

The AI tool did not simply get imple- mented. It spread via trusted relation- ships throughout the organization. This kind of successful AI imple- mentation is what connectors make possible. It is accurate that strategy defines an organization’s direction and technology enables capability. However, it is the individuals who determine whether anything actually moves forward (or not). These are the organization’s unique connectors.

Disconnection

Participation is the real driver of sustainable AI adoption

Assuming that communication creates commitment is one of the biggest mistakes leaders make with AI. It does not.

Practice #5 Design for Participation

People may understand an AI initiative perfectly well and still feel no real ownership. They may attend the presentations, complete the training, and even repeat the language of the transformation without ever truly integrating it into how they think or work. People support what they help shape, not simply what they are told to use

This is the tipping point where many AI transformations quietly lose energy. AI systems are introduced to people rather than developed with them. Adoption may appear successful on the surface.

However, underneath, engagement remains shallow and fragile.

The emerging AI future has to be discovered collectively Participation strengthens all three human learning networks

1. It creates stories as people share experiences about how AI actually works in practice. 2.It enables learning to travel across communities as teams discover and adapt ideas in different contexts. 3.Most importantly, participation creates the ownership and legitimacy that consensus networks depend upon.

Imagine a company that is introducing AI into its customer service opera- tions. A central implementation team defines the primary use cases and establishes workflows. It trains employees on how the system should be used. The rollout is disciplined and efficient. However, over time, something unexpected happens. Some frontline teams begin using the AI in ways that were never anticipated. They discover that the system is not only useful to effect- ively handle customer requests, but also to identify recurring sources of customer frustration as well as to surface product design issues much earlier than previously.

Participation matters for another reason as well.

No leadership team, no matter how capable can fully predict where AI will create value how people will adapt to it,

or which use cases will ultimately matter most for the organization.

This emerging future cannot simply be designed centrally. It has to be discovered collectively This is often where many organizations become too narrow in their approach to AI.

Employee participation makes AI a living learning experience

Emerging insights never appeared in the original implementation plan

They emerged through participation

Not because leadership predicted them, but because people closest to the work explored possibilities that the designers could not fully see from the center.

share what they were learning.

This is the deeper value of participation

Some ideas failed quickly. Others spread unexpectedly across the network. A finance team discovered an approach that later transformed procurement. Customer service insights informed product develop- ment. HR teams adapted tools that were originally created for operations. None of this emerged from a master plan. It emerged because participa- tion allowed intelligence to circulate across the network. That is what participative leadership makes possible. While centralized systems can deploy AI efficiently, participative networks discover what AI is actually capable of becoming.

When people are invited to experi- ment, adapt, and contribute to how AI evolves inside the organization, its implementation becomes a living learning process rather than a fixed rollout exercise.

Use cases multiply Learning accelerates Ownership deepens

The leadership of one organization intentionally designed AI implemen- tation around distributed experi- mentation. Instead of prescribing all applications centrally, teams across functions were encouraged to test small-scale uses and openly

AI breakthroughs occur at the intersections where distinct

worlds collide AI value emerges where it moves throughout an organization

Practice #6 Work across boundaries

The real value of AI rarely emerges where it is first introduced into a specific business domain. Most organizations still treat AI as something that belongs inside functions:

Each of these domains optimizes locally, builds expertise internally, and improves performance within its own boundaries. The most transformative value almost never appears inside a domain’s boundaries

Finance uses it for forecasting HR uses it for workforce analytics, Operations uses it for efficiency

It appears between boundaries

Cross-community learning is key to develop AI insights

The moment AI insights cross from one domain into another, something distinct happens.

Patterns become visible that no single function could have seen on its own.

Assumptions get challenged and new possibilities emerge that were not part of any original AI roadmap.

Consider a common situation in a large organization that is introducing AI-driven insights to better under- stand customer behavior. Marketing teams use the system to optimize engagement. Product teams use AI to refine features. Customer service uses AI to understand pain points. Individually, each function observes improvement. However, the break- through concept only happens when these perspectives are combined. Entirely new patterns emerge when marketing insights are viewed along- side product data and service inter- actions. A recurring customer frustra- tion that looked like a service issue plays out to be a design constraint of a product. Frontline service experi- ervices challenge AI marketing

This is where cross-community learning becomes critical.

These boundaries are not technical in many organizations

They are cognitive

Different functions develop a distinct language, their own assumptions and particular definitions of what matters. Islands of intelligence are created over time that are strong internally but weakly connected externally. AI can unintentionally reinforce this fragmentation. This is especially the case if it is deployed in isolation within each domain. In contrast, AI becomes something else altogether when it is used across boundaries:

A connector of perspectives

Leading across boundaries connotes deliberately designing for connection

assumptions about user behavior. A product decision is reframed by customer engagement data. None of these insights exist in isolation. They only become visible when boundaries are crossed. When intelligence emerges through networks, it is the cross-boundary movement that allows the insights of this intelligence to coalesce. Leading across boundaries connotes deliberately designing for connection rather than jurisdiction. It means treating functions as parts of a larger learning network, not as separate systems to be indepen- dently optimized. Moreover, it recognizes that some of the most valuable AI insights will not originate where the data is richest, but where diverse perspectives collide. AI-generated insights from different business units in one organization were not kept inside domain-specific dashboards. Instead, these insights were deliberately shared in cross-

functional “learning loops”. Teams were encouraged to react not only to their own outputs, but to other emerging patterns elsewhere in the organization. The result was not just better coordination. It was a shift in the way problems were understood. Issues that once appeared locally were revealed as systemic issues which needed attention. Opportun- ities that appeared to be incremental became strategic prospects.

This is the outcome when boundaries become permeable.

Real transformation happens when the deepened expertise of AI inside a domain crosses the boundaries into other business domains.

Networked systems rarely behave in neat, linear patterns Organizations often speak

about AI as though its effects were contained and predictable

Practice # 7 Think in Ripples

A tool is introduced, a process improves, efficiency rises, and the impact appears measurable and local.

Networked systems rarely behave in neat, linear patterns.

This gradually reshapes how people think, learn, and interact with one another. A forecasting system may begin by improving operational planning. Yet over time this system can alter how managers perceive uncertainty and risk.

The moment AI enters an organi- zation; it begins influencing far more than the task for which it was originally designed. Its effects move outward through conversations, habits, decisions, and assumptions.

AI changes the pathways through which information flows

A recommendation engine designed to optimize customer engagement may slowly reshape employees’ attention to specific issues and what is considered important. A decision-support tool may subtly influence who speaks with confidence in meetings and which perspectives gain legitimacy:

What specific impact does AI have on information flows in an organization?

This shapes the forms of judgment that people learn to trust

Stories begin to change

The most important transformations are often indirect. However, by the time they become visible, these transformations may already be deeply embedded in the organi- zational culture. If intelligence emerges through networks, AI does not simply automate activity.

People develop new assump- tions about what success looks like and which kinds of reasoning carry authority

Ideas move differently across communities

Consensus forms through new patterns of legitimacy and persuasion

AI changes the pathways through which

No AI initiative is ever truly local

learning, influence, and judgment move throughout the organization

Every implementation sends ripples throughout the wider system

Leaders need to sense the secondary effects of AI in their organzations

Consider a company that is introducing AI into workforce planning . The benefits of AI initially appear straightforward: better forecasts, more efficient staffing decisions, and smoother allocation of resources. Over time however, quieter shifts begin to emerge. Managers start aligning decisions more closely with the logic of the model. Teams become less willing to pursue approaches that fall outside predicted outcomes. Conversations that once included exploration and debate now act to slowly narrow toward consensus of validation and justification. No formal policy required this kind of change. It was through the repeat- ed interaction with the system that the network absorbed it gradually. The technology did more than optimize planning. It influenced the types of thinking that AI recognized was legitimate, responsible, and intelligent.

To think in ripples means to recognize that leadership today extends beyond AI implementation itself Leaders must pay attention not only to direct outcomes. They also need to notice the secondary effects that are spreading through the organi- zation: the subtle narrowing of curiosity, the amplification of certain voices, the quiet disappearance of dissent, and the gradual normalization of assumptions that once might have been previously questioned. Leaders who recognize this impact of AI create opportunities for reflection before invisible shifts harden into culture.

This is the deeper dimension of AI transformation

New leadership capacities are required to effectively master the AI challenge

These leaders strengthen feedback loops across boundaries so that emerging effects can be recognized early. They continually ask not only whether AI is improving performance, but what kinds of behavior and judgment it is encouraging over time. While AI may enter the organization as a technology initiative, its deepest effects are always human. This is why leaders need to cultivate new capacities which will enable them to observe AI from a different perspective:

Every AI implementation sends ripples throughout the entire system in various ways

A human-centered one

The leader’s role to care for and shape the human side of AI Most organizations are trying to solve an AI problem.

What they actually have is a network problem

This is the missing ingredient in most AI strategies.

This may sound surprising. After all, the headlines are filled with discus- sions about models, agents, copilots, governance frameworks, and productivity gains. At the same time, organizations are investing billions in technology while leaders race to determine where AI can create value.

Beneath all of this activity lies a quieter reality: AI does not operate in isolation AI enters an existing network of relationships, conversations, assumptions, and decision-making processes. The quality of that network largely determines whether AI strengthens human capability or weakens it.

Limitation of networks throughout an organization disrupts AI efforts

The overreaching message of this series of reflections on the inter- relationship between the cultivation of new leadership capacities on Leadership and AI is: If stories do not travel , AI cannot turn experience into shared wisdom. If communities remain disconnected, AI cannot help ideas recombine into innovation.

nology when the conditions for intelligence have not been cultivated throughout the human system of networks in the organization.

If consensus is weak, AI cannot create the trust and legitimacy that effective decisions require.

The results are predictable:

AI accelerates activity without necessarily increasing capability

Each of these observations about the influence of AI clarifies that:

AI produces more answers without improving judgment

The limiting factor of AI is not technology, It is the network

AI creates more information without generating more wisdom

As a result, many AI initiatives struggle despite impressive tech- nical capabilities. Organizations deploy powerful tools into environ- ments where trust is low, silos are strong, participation is weak, and learning flows poorly. They expect intelligence to emerge from tech-

Despite best intentions, AI weakens the very capabilities organizations are trying to strengthen.

An alternative to the traditional AI scenario

This alternative scenario begins with a distinct assumption:

Intelligence does not reside solely in individuals

Nor does it reside in machines.

It emerges through networks

This intelligence emanates from:

Collectively these 7 Practices create the conditions in which AI can amplify human capability rather than replace it The most important leadership challenge of the coming decade is not building smarter machines, but building smarter human networks. Organizations that understand networks will use AI to enhance judgment, deepen learning, and strengthen collective intelligence. Organizations that ignore networks may achieve greater efficiency, but they will struggle to achieve wisdom. In an age increasingly shaped by artificial intelligence, wisdom may become the ultimate competitive advantage.

Stories that carry experience Connections that allow ideas to travel Through conversations that transform diverse perspectives into shared understanding These sources of intelligence that emerge from an organzation’s unique networks provide the rationale for a shift to the practice of Network Leadership. The Seven Practices of an Effective Network Leader are not simply new leadership methods. These practices are approaches that strengthen the human networks through which intelligence emerges.

Seven Practices enable leaders to effectively undertake AI challenges

The Seven Practices of an Effective Network Leader empowers leaders throughout an organization to: See the entire system especially the human-learning network Lead with curiosity by keeping an open mind about how AI could support your business Build trust that AI is supporting human capabilities, not just replacing them Activate connectors through- out the organization, business and customer ecosystem to identify influencers to discover new applications and spread best practices Design for participation within the organization as well as in the entire business and customer ecosystems to foster ownership of AI initiative Work across boundaries inside the organization and with partners and customers to discover true AI innovation for an organization Think in ripples by looking beyond the operational impact to the invisible effects if AI spreading throughout the organization

These practices are not isolated leadership techniques.

These practices are ways of understanding how intelligence actually evolves inside living system

In the end, leaders are never simply implementing AI.

Leaders are shaping how the network learns and ultimately, what it becomes.

Top 10 Questions for Leaders in the Age of AI

AI won’t remake organizations simply because models get smarter.

Real transformation happens when leaders create the conditions for human and artificial intelligence to work together. These conditions advance better judgment, faster learning, and stronger collective capability. By exploring the 10 TOP Questions for Leaders in the Age of AI questions, you can assess whether your organization’s approach to Ai will amplify wisdom, not just speed.

The real test for any AI strategy is simple: The future will be shaped less by algorithmic sophistication than by the quality of the human networks that surround those algorithms. Use the questions on the following page as a diagnostic, a conversation starter, and a roadmap for redesigning how your organization learns and co-creates a successful AI initiative.

Top 10 Questions for Leaders in the Age of AI By exploring these 10 questions, you can assess whether your organization’s approach to AI will amplify wisdom, not just speed. 1. Are we treating AI as a learning transformation, not just as a tech rollout? AI’s biggest payoff is changing the way knowledge flows, decisions are formed, and people learn together - not only to automate tasks. 2. Are we investing in the human networks that give AI meaning? Scale without context is noise. Are we building relationships, trust, and feedback loops that turn data into wise judgment? 3. Where is our most valuable knowledge stuck, and how will AI free it? If expertise is siloed, AI will only create silos more swiftly. How are we enabling experience and insight to travel throughout our organizations? 4. Are we designing AI to extend human judgment or to replace it? The aim of human-driven AI is to expand curiosity and reflection, not to outsource thinking to a dashboard. 4. Are people shaping how AI is introduced and used? AI Adoption follows ownership. Are employees co-designing use cases, surfacing risks, and redefining workflows? 6. Is AI connecting communities or hardening boundaries between them? Breakthroughs happen at intersections. Are we using AI to surface cross- disciplinary perspectives and new collaborations in our organizations? 7. How are we preserving the stories and context behind our data? Raw information is brittle. Are we capturing the reasoning, trade-offs, and lessons that make knowledge actionable? 8. Are we creating space for dissent and alternative views? Learning requires friction. Do our systems surface minority perspectives or only accelerate consensus? 9. What unintended ripples is AI creating across roles and relationships? Every intervention shifts behavior. Are we tracking indirect effects on trust, incentives, and culture? 10. If AI amplifies the network into which it enters, what is exactly the kind of amplification which will take place? Will AI strengthen trust, curiosity, and collaboration—or accelerate fragmentation, dependency, and control?

The Paradigm Shift in Leadership

Our digital era—with its rapid change, technological advances, and complex interdependencies—demands a new leadership paradigm, one that values collaboration, adaptability, and distributed influence. Network Leadership emphasizes the collective power of networks to solve problems, spark innovation, and build resilience. Invite Jeffrey Beeson to share usable insights and motivate future-ready leaders throughout your company to cultivate a more resilient and innovative organisation to boost business performance.

The Organisation of the Future: Leading in Networks Leading the Ripple Effect: Network Leadership in an AI World Successfully Integrating AI into Human Networks The 7 Practices of Highly Effective Network Leaders Rewiring Innovation: From Spark to Scale The Culture Code: The Secret of High-Impact Organizations

Explore Jeffrey’s insightful keynotes to boost your business success

Represented by Chartwell Speakers https://cli.re/Speaker-Profile

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Leaders must create the conditions that enable their organizations to harness the full potential of their networks, by promoting shared purpose, connection, and self- organisation. My book proposes actionable insights for leaders in agile, decentralized organizations who seek to build adaptable, customer- focused, and innovative systems.

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