The Hidden Organization: Mapping the Networks That Run Your Company

Strategic execution is the biggest challenge facing organizations. See how a growing field of sociological research is revealing a hidden that drives results.

Executive Summary: Strategic execution is the biggest challenge facing organizations today. It accounts for 70% of the failure in strategic outcomes. In this article, we explore the origins of an increasingly popular sociological tool, and how network analysis mixed with behavioral economic thinking can reveal a hidden world where change and sentiment take shape.

Do some mid-level employees quietly hold more power than their C-suite executives?

In most organizations, the people who drive performance, innovation, and resilience don't always reside at the top of an official chart. They’re hidden in the web of relationships that, if revealed, would show how their power and influence ripple throughout an organization.

Take Maya. She wasn’t a manager. She didn’t have a direct team, didn’t hold a high-ranking title, and rarely appeared in leadership meetings. But, when critical questions came up—about customer needs, process gaps, or implementation plans—Maya’s name surfaced.

"Ask Maya," someone would say.

She worked in product management at a 700-person tech company, quietly sitting at the crossroads of engineering, product, and finance. She knew what was happening on the ground, how tools were used, and who had already solved someone else's problem.

No one had given her power. But everyone would seek her out.

Only after the company conducted an organizational network analysis did Maya become one of the most influential employees in the firm.

What Org Charts Miss (And Networks Reveal)

Org charts show who reports to whom—but not who actually makes things happen.

The real drivers of performance, innovation, and resilience often operate outside formal authority. These hidden influencers—connectors, brokers, quiet leaders—shape outcomes in ways job titles can’t capture.

Modern network analysis helps surface these informal power structures. With the right tools, you can map how work actually flows: who people trust, where collaboration stalls, and which employees hold leverage across teams.

In this article, we’ll explore where this thinking comes from—its scientific origins, psychological foundations, and how it’s now being used in forward-thinking organizations to improve everything from retention to post-merger integration.

In the modern workplace, it’s not hierarchy that determines success. It’s the network.

The Unseen Power Structures That Shape Every Strategy

Every organization operates on two levels: the visible structure shown on the org chart and the informal network that actually gets things done—what organizational sociologists like myself often call the “hidden organization.”

We’re all familiar with the formal structure: executives at the top, managers in the middle, and employees reporting upward in neat lines. This structure grants authority based on title—CEO, VP, manager. However, these roles can also invite distrust, either because they feel removed from frontline reality or carry the weight of institutional hierarchy.

Now, contrast that with the informal network—the web of relationships that flows beneath the chart. Here, authority isn’t assigned. It’s earned. It emerges through trust, credibility, and shared experience. In these networks, influence—like beauty—is in the eye of the beholder.

These unofficial leaders—cross-functional connectors and respected problem-solvers like Maya—hold real sway. They may not appear on leadership rosters but hold “earned authority.” Some have titles. Many don’t.

The trouble is, we often don’t recognize these individuals until they’re gone. And when they leave? Work slows. Decisions stall. Bottlenecks surface.

That’s why network analysis—used for decades by sociologists, mathematicians, and even intelligence agencies—has become essential for modern business leaders. It maps how influence ripples through an organization, who holds key knowledge, and where collaboration breaks down.

However, organizational network analysis (ONA) is just the starting point. It tells us what is—not what to do next. The real opportunity is learning how to tap these networks to spark change, accelerate trust, and build the kind of collaboration that charts don’t capture.

The Origins of Network Thinking—and Why It’s More Relevant Than Ever

Modern network analysis draws from three foundational disciplines—each offering a unique lens into how influence works beyond the org chart.

Psychology: Mapping Social Power in Plain Sight

In 1934, pyscho-sociologist Jacob L. Moreno began charting classroom relationships to explore how friendship, trust, and even bullying shaped behavior. His research found that formal structures—like teacher authority or assigned seating—often fail to capture informal power.

To surface these hidden dynamics, Moreno created visual diagrams—what he called sociograms—to reveal who was trusted, who was excluded, and who quietly led.

Image: Jacob L. Moreno. Source: Wikipedia, Creative Commons BY-SA 3.0.

Moreno’s early maps were startling in their clarity. Even 1st graders identified central figures. By 3rd grade, cliques had formed. By 4th grade, students had sorted into defined clusters—some tightly knit, others isolated on the periphery.

While Moreno visualized power in small groups, anthropologists took a broader, cultural view.

Anthropology: Culture, Kinship, and Social Trust

In the 1940s, anthropologist Alfred R. Radcliffe-Brown studied tribal societies across Australia to understand how social organization—not just hierarchy—shaped power.

What he found was striking: power didn’t always reside with the official leader. Instead, it flowed through those who managed relationships—individuals trusted to pass messages, settle disputes, or preserve rituals. Kinship ties, ceremonial roles, and social obligations created networks of influence that parallel formal authority.

In many communities, the person who understood and navigated the social fabric—not the one with the highest title—truly held power.

This anthropological lens revealed that influence often hides in customs, trust, and proximity, not institutional rank. It laid a conceptual foundation for modern network analysis, especially in cross-cultural or global organizations, where social norms and informal leadership can either strengthen or sabotage strategy.

Mathematics: The Hidden Geometry of Human Systems

Long before “social networks” existed, mathematicians were solving problems of connectivity.

In 1736, Leonhard Euler tackled the Königsberg Bridge Problem, modeling land masses as nodes and bridges as edges. This gave birth to graph theory—the foundation for understanding how structure, not just content, determines movement within a system.

Image: Königsberg Bridge Problem. Source: Wikipedia, Creative Commons BY-SA 3.0.

Fast-forward to 1959: Paul Erdős and Alfréd Rényi applied probability theory to networks. Their models showed how connections form, grow, and concentrate—leading to the emergence of hubs of influence.

Want a modern analogy? Think of Chicago O’Hare—not built to be a global hub but became one because connectivity attracts more connectivity.

Erdős and Rényi’s legacy gives us the mathematical language to map and measure influence—a toolkit now used in everything from digital platforms to internal org charts.

Who Really Holds Power? A Look at Network Centrality

In most organizations, power doesn’t just come from a title—it comes from where you sit in the network.

One key way to understand this is through centrality, which measures how connected someone is, how often they connect others, and how quickly they can reach across the system. But power is nuanced. It can come from access, influence, or being the bridge between disconnected groups.

Take Maya.

Her degree centrality—the number of people she directly interacts with—wasn’t particularly high. But her betweenness centrality is. This measures how often someone sits on the shortest paths between others, connecting people who might not otherwise collaborate.

Maya didn’t just know people—she connected teams. That made her a critical conduit for information, someone who enabled faster decisions and smoother coordination. Without her, communication between Product, Engineering, and Finance might have stalled or fragmented entirely.

She also had high closeness centrality, meaning she could quickly reach anyone across the organization. This made her especially effective during the pandemic—when remote work separated teams physically, Maya kept the network moving. She gathered updates, shared knowledge, and kept relationships warm.

But influence isn’t just about how many people you know or how fast you can reach them. It’s also about what kind of information you have access to.

Sociologist Ronald Burt introduced the idea of structural holes—gaps in the network where no direct connection exists. Those who bridge these gaps gain access to non-redundant, often novel information. In other words, they hear things no one else does.

If Maya could bridge the gap with, say, Sales—a team she wasn’t yet connected to—she wouldn’t just add another contact. She’d gain a new perspective. That’s where real power lies: the ability to see what others can’t and share insights no one else has.

This diversity in perspective drives innovation, creativity, and unexpected solutions—the kind that can’t be traced on an org chart.

A Quick Guide to the Four Types of Network Influence

But network science doesn’t just show who has access—it helps us understand how that access could be leveraged to generate power, influence, and impact. To get there, we need to understand the human side of networks: how relationships form, why people connect, and what makes some networks thrive while others stagnate.

How Relationships (Not Roles) Shape the Network

People don’t form workplace connections at random. Networks emerge from the way we build trust, share knowledge, and gravitate toward those we relate to—shaped by both psychology and social context.

Sociologist Pierre Bourdieu introduced the idea of social capital: the value we gain from relationships. Unlike money or skills, social capital doesn’t belong to any one person—it lives between people, and it only works if it’s maintained. This explains why some relationships last while others fade, and why certain people become central to a network—even if they hold no formal authority.

This brings us to two types of ties that play very different roles:

• Strong ties are the close, trust-based relationships we form with immediate colleagues or teammates. These connections offer stability, psychological safety, and deep knowledge sharing—the people you turn to when things get tough.

• Weak ties, on the other hand, are more casual or distant connections. You don’t work with them daily, but they offer something different: fresh perspectives. Sociologist Mark Granovetter’s landmark theory, The Strength of Weak Ties, showed that people often find jobs through acquaintances—not close friends. Why? Because weak ties connect us to new ideas and networks we wouldn't access otherwise.

In organizations, innovation often moves along these weaker ties—jumping silos, challenging groupthink, and bringing in outside energy.

But networks are always changing. Their structure matters too:

• Density shows how tightly connected people are. Dense networks spread information quickly—but can also become echo chambers where nothing new gets in.

• Centralization shows whether influence is spread out or concentrated in a few key people. Centralized networks move fast—but are vulnerable if a central person leaves.

• Clustering happens when small groups talk mostly to each other. It can foster trust—but also block new thinking.

A 2018 study by Kumar et al. looked at echo chambers on platforms like Twitter (now X). People inside these clusters mostly reinforced the same ideas—a perfect example of dense, strong-tie networks.

But when someone bridged between groups—even accidentally—new ideas spread. These weak-tie connections acted as unexpected pathways for influence and information.

Turning Network Data Into Business Action

Seeing Resignations Before They Happen

Changes in an employee’s network position—whether they become more peripheral or communicate less—often precede formal resignation. Research shows that employees who reduce their interactions are significantly more likely to leave within six months.

In short: people quit socially before they quit physically.

Similarly, some organizations have begun using network surveys to detect early signs of disengagement. Research shows that network-based insights can predict over 70% of voluntary departures—compared to just 48% accuracy using traditional tools like performance ratings or engagement scores.

The signals are subtle but powerful: decreased meeting attendance, shorter email responses, and fewer new connections. These network shifts often begin three to six months before an employee officially decides to leave. For organizations, this early warning system provides a critical window to intervene—whether through re-engagement strategies, role adjustments, or succession planning.

Rethinking Succession: Beyond Titles and Performance Scores

Traditional succession planning often misses the mark because it focuses on titles and performance reviews, not real influence.

The best leaders aren’t just high performers. They’re the ones their peers trust, seek out, and follow when things get messy. This is where organizational network analysis (ONA) adds value. It highlights those who broker knowledge, build trust, and bridge silos—people with network capital, not just technical skill.

Often, the most influential players are the ones we least expect.

Take Maya. Her company was planning a restructuring. On paper, she wasn’t identified as high-potential. But when network analysis revealed her central role—connecting teams and coaching new hires—leadership saw her in a new light. They brought her into strategy sessions. They gave her visible roles in onboarding. And when leadership positions opened up, she was ready.

Succession isn’t just about promoting from within—it’s also about helping external hires succeed. Technically brilliant outsiders often struggle in their first six months—not because they lack skill, but because they don’t yet understand how the culture works.

In Maya’s organization, influence didn’t look the same across every team.

• In Engineering, it came from deep technical expertise—being the person who could untangle complex problems when no one else could.

• In Sales, it was about presence and persuasion—those who could win over clients and rally peers around a pitch.

• In Operations, trust was built on consistency—the quiet pros who always delivered and kept things from falling apart.

Maya’s strength was her ability to navigate these differences. She understood how each group defined credibility—and adapted her communication style without losing authenticity. That’s what made her such a powerful connector.

In both internal promotions and external transitions, network analysis helps companies move past subjective instincts to make smarter, more strategic leadership decisions.

Why M&A Fails Without Network Integration

Mergers and acquisitions—where I first started working with ONA—don’t typically fail because of bad strategy or even culture clashes. They fail because of a lack of integration at the human network level.

Traditional M&A playbooks focus on financials, product alignment, and leadership structure. But they often overlook the invisible influence networks that determine whether employees collaborate across the newly combined entity.

Take the 1998 Daimler-Chrysler merger. The failure wasn’t only about cultural mismatches between the German and American automakers. Reports suggest that years after the merger, employees continued operating in silos. Their collaboration patterns stayed separate, and a unified network never formed. Had the company identified informal influencers and connectors early on, they potentially could’ve built cross-company bridges and accelerated integration.

More recent deals suggest that companies are learning from past mistakes. Microsoft’s acquisition of LinkedIn in 2016 has often been cited as a smoother integration. Microsoft gave LinkedIn operational autonomy, while gradually connecting capabilities and systems over time. While there’s no public proof that ONA was used, Microsoft’s interest in workplace analytics suggests they likely considered internal networks as part of the strategy.

Today, private equity firms are increasingly using network analysis to assess acquisitions. By mapping informal influence structures, they can identify whether a company's operational strength is deeply embedded or overly reliant on a few key individuals. This approach helps avoid post-acquisition surprises—such as discovering that much of a company's value "walks out the door".

Measuring Inclusion Beyond Headcounts

Traditional diversity metrics count representation but miss inclusion. Network analysis reveals whether diverse employees are truly integrated into influence networks or isolated on the periphery. Research consistently shows that underrepresented groups often have fewer network connections despite similar qualifications—not because of ability but because of systemic barriers to relationship formation.

Major companies now use network analysis to ensure diverse employees are integrated into influence networks. They identify where certain groups or individuals are isolated and create cross-functional projects, mentorship programs, and collaboration spaces designed to build connections.

Getting Started: How to Bring Network Thinking Into Your Org

Implementing network analysis doesn’t require a massive investment or a company-wide overhaul. It starts with intention.

1. Identify a Focus Area

One of the more impactful uses for ONA is improving cross-functional collaboration. Start by forming a hypothesis based on your observations. For example:

• Where has coordination stalled?

• Are critical teams—like Product, Engineering, and Sales—functionally well together?

• Who are the go-to connectors between departments?

2. Validate with Existing Data

Use existing data to confirm your initial observations. Email metadata, calendar invites, messaging tools, and surveys can paint an initial picture of your network. But proceed with caution. Automated tools can show who's connected to whom—but not influence.

As one leader put it, “Yes, she’s in every meeting—but she never says a word.”

Raw connection doesn’t equate to influence. Complement these tools with qualitative insights to understand trust, credibility, and shared history.

3. Pilot Before You Scale

Choose a team or department as your first test case—especially one experiencing collaboration pain. This focused approach lets you:

• Refine your methods

• Demonstrate quick wins

• Build credibility across the org

From there, expand to broader areas of the business.

4. Blend Metrics with Human Insight

Graphs show structure. But understanding why connections exist—and how work gets done—requires talking to people.

Pair network data with surveys, interviews, or team workshops. These insights help explain gaps, uncover hidden talent, and provide context behind the lines.

5. Choose the Right Tools

Enterprise-grade platforms offer robust functionality, but smaller orgs can start with open-source tools like NetworkX or UCINET. Choose tools that align with your goals—not the other way around.

6. Move from Mapping to Action

The goal isn’t just to visualize the network. It’s to improve it. Use insights to:

• Bridge silos through cross-functional projects

• Recognize and elevate informal influencers

• Support onboarding by helping new hires build early connections

When done well, network analysis becomes a tool for transformation—not just diagnostics.

7. Protect Privacy, Always

Organizational network analysis touches on your most valuable asset: your people.

Be transparent about what data you’re using and why. Anonymize wherever possible. And always prioritize ethical data use over expediency.

The Future Belongs to the Well-Connected

Network analysis is a strategic tool. The most effective uses go beyond simply identifying the structure; they focus on understanding the human dynamics and designing interventions that enhance collaboration and build resilience.

The companies that succeed won't just analyze networks—they'll actively shape them, using data to foster connection, innovation, and resilience. As work becomes more complex and collaborative, understanding organizational networks isn't just a competitive advantage—it's a necessity for survival.

The challenges organizations face today—innovation in a rapidly changing environment, knowledge transfer in an aging workforce, and collaboration across global and virtual teams—are all fundamental network challenges.

The formal organization will always matter. Hierarchies provide clarity, accountability, and efficiency. But beneath that visible structure lies the organization's true operating system—the network of relationships through which information flows, decisions are influenced, and work gets done.

The future of work belongs not just to those who manage well—but to those who connect well.

References

Moreno, Jacob Levy (1934). Who Shall Survive? A new Approach to the Problem of Human Interrelations. Beacon House

Burt, RS (1992). Structural Holes: The Social Structure of Competition. Harvard University Press

Burt, RS (2004). The Social Capital of Structural Holes. The New Economic Sociology: Developments in an Emerging Field. Princeton University Press; 2004. p. 148-189.

Cross R, Parker A (2004). The Hidden Power of Social Networks: Understanding How Work Really Gets Done in Organizations. Harvard Business School Press

Freeman, LC (1979). Centrality in Social Networks: Conceptual Clarification. Social Networks. 1(3):215-239.

Granovetter, MS (1973). The Strength of Weak Ties. Am J Sociology. 78(6):1360-1380.

Krackhardt D, Hanson JR (1993). Informal Networks: The Company Behind the Chart. Harv Business Review. 71(4):104-111.

Kumar, et al (2018): MIS2: Misinformation and Misbehavior Mining on the Web.

Milgram, S (1967). The Small World Problem. Psychology Today. 2(1):60-67.

Radcliffe-Brown, A.R. (1952). Structure and Function in Primitive Society. Free Press.

Erdős, P., & Rényi, A. (1959). On Random Graphs I. Publicationes Mathematicae.

Euler, L. (1736). Solutio problematis ad geometriam situs pertinentis. Commentarii Academiae Scientiarum Imperialis Petropolitanae.

Bourdieu, P. (1986). The forms of capital. In Richardson, J. (Ed.) Handbook of Theory and Research for the Sociology of Education. Greenwood Press.

*Since you’re still here, you may be interested in seeing Organizational Network Analysis in action. I wrote about our partnership with a healthcare facility that was struggling with inconsistent processes across several of its sites. The study reviews how we used network science to implement novel strategies that uncovered broken communication channels, innovation islands, and the hidden influencers that brought the entire firm back together.
You can find that story here on the 3Fold Outcomes Substack: