Communities are engines of collective intelligence
For much of the modern workplace, communities have been viewed as engagement programs. They were designed to connect employees, share information, and create opportunities for collaboration beyond organizational structures. While these outcomes remain important, they no longer fully capture the role communities play inside organizations.
As work becomes increasingly distributed across people, teams, knowledge systems, and intelligent technologies, communities are evolving into something more significant. They are becoming the environments through which intelligence flows, capabilities develop, trust is established, and culture is reinforced. In many ways, communities have become one of the most important components of an organization's operating ecosystem.
This shift reflects a broader reality that sits at the center of Devonport’s research. Organizations do not adapt because information exists. They adapt because information moves. They do not learn because individuals learn, it’s because knowledge is shared, challenged, refined, and transferred between people. Communities provide the conditions that make this possible.
Peter Senge argued that learning organizations are distinguished by their ability to continuously expand their capacity to create desired results through collective learning rather than individual expertise alone.¹ Today, as organizations navigate accelerating technological change, that observation has become even more relevant.
Communities shape behavior because environments shape people
The traditional view of communities focuses on participation. How many people joined? How often did they engage? How many messages were exchanged?
These metrics may indicate activity, but they reveal little about impact.
A more useful perspective begins by recognizing that communities are environments. Like any environment, they influence the behaviors that emerge within them. The relationships people build, the information they encounter, the expertise they can access, and the norms they observe all shape how they think, learn, and contribute.
This principle aligns closely with Devonport's concept of corporate epigenetics. Just as biological environments influence how genetic potential is expressed, organizational environments influence how individual and collective potential is expressed. Communities are one of the primary mechanisms through which these environmental conditions are created.
Research consistently supports this relationship. Amy Edmondson's work on psychological safety demonstrates that people are significantly more likely to share ideas, ask questions, and contribute knowledge when they feel safe doing so.² The environment itself influences whether intelligence remains hidden or becomes accessible to others.
When communities are intentionally designed, they can accelerate learning, increase participation, strengthen trust, and encourage experimentation. When neglected, they often become fragmented, transactional, and disconnected from organizational outcomes.
Support for this idea
The Community Roundtable
The Community Roundtable's annual research has consistently found that mature communities outperform less mature communities across engagement, retention, knowledge sharing, and business value metrics.
Some reported findings across various years include:
Mature communities are significantly more likely to demonstrate measurable business outcomes.
Organizations with community strategies and dedicated community leadership report higher member participation rates and stronger organizational alignment.
Community maturity correlates with increased knowledge sharing and member satisfaction.
The challenge is that many of their reports are gated and the specific percentages vary by year, so I'd want to verify exact figures before publishing them.
McKinsey – Social Technologies and Collaboration
One of the most frequently cited studies found that organizations effectively using social collaboration technologies could improve the productivity of knowledge workers by 20–25%.
Their reasoning wasn't that the technology itself created value, but that it improved:
Information discovery
Expertise location
Knowledge sharing
Collaboration
Which is essentially the primary function of communities. This is a powerful statistic because it supports the argument that improving intelligence flow creates measurable business value.
Deloitte / Bersin Research
Several Bersin studies found that organizations with strong learning cultures are:
Up to 92% more likely to innovate
Up to 52% more productive
Up to 17% more profitable
These numbers are often cited in learning and culture literature.
While they aren't community-specific, communities are one of the primary mechanisms through which learning cultures operate.
Google's Project Aristotle
Google found that psychological safety was the strongest predictor of team effectiveness.
Not necessarily a "community" metric, but highly relevant because communities often create or reinforce psychological safety.
Teams with higher psychological safety demonstrated:
Greater participation
Better collaboration
More learning behaviors
More innovation
This supports your "learning, trust, experimentation" argument.
MIT Collective Intelligence Research
Anita Woolley and Thomas Malone found that collective intelligence explained a substantial portion of variation in group performance.
One of the most interesting findings:
Group success was less dependent on the intelligence of individual members.
Group success was more dependent on communication patterns, participation equality, and social sensitivity.
Microsoft Work Trend Index
Recent findings show:
Employees increasingly rely on peer networks to learn AI skills.
AI adoption often spreads through local champions and social influence rather than top-down mandates.
High-performing AI adopters are more likely to share practices across informal networks.
This directly supports your Trust Transfer Model and Adaptation Velocity thinking.
The question is no longer whether communities influence behavior. The question is whether organizations are intentionally designing communities that reinforce the behaviors they want to see.
Communities are engines of collective cognition
Organizations are often described as collections of people. Increasingly, however, they are becoming networks of intelligence. The value of an organization is not determined solely by the expertise of its employees, but by how effectively that expertise is discovered, shared, understood, coordinated, and applied. This is where communities become essential to new behavior activation.
Etienne Wenger's foundational research on Communities of Practice demonstrated that learning frequently occurs through participation in social networks rather than formal training programs.³ Knowledge is transferred through interaction, observation, shared experience, and collective problem solving.
Communities create the conditions for collective cognition—the process through which intelligence emerges from interactions between individuals, teams, knowledge, systems, and shared experiences. They help organizations surface expertise that would otherwise remain hidden, connect people facing similar challenges, and accelerate the movement of ideas across traditional boundaries.
Research from the MIT Center for Collective Intelligence suggests that group effectiveness is influenced not simply by the intelligence of individuals, but by how effectively groups coordinate, communicate, and share information.⁴ In other words, intelligence itself can become an emergent property of a network.
In practice, this means communities serve as engines for knowledge transfer, capability development, decision support, innovation, and problem solving. They allow organizations to learn faster than any individual could learn alone.
The most effective communities are not measured by the volume of conversation they generate. They are measured by the quality of intelligence they help create and distribute.
Some may even argue that the principle of collective cognition is why working in an office together is better than working from home.
The three layers of community development
As communities become increasingly important to organizational performance, community development must evolve beyond communication and engagement strategies.
Three interconnected layers help explain how modern communities create and distribute value.
The intelligence layer
Every organization contains a vast network of expertise, experience, relationships, and institutional knowledge. Much of this intelligence remains invisible and latent.
Thomas Davenport has long argued that knowledge workers create value through expertise, judgment, and information exchange rather than routine execution.⁵ Yet many organizations struggle to identify where expertise resides.
The intelligence layer focuses on understanding where knowledge exists, who holds influence, what expertise is available, and how trust is distributed throughout the organization.
Communities help make these networks visible. They reveal subject matter experts, connect emerging leaders with experienced practitioners, and create pathways for knowledge to move more efficiently across the organization.
The connection layer
Knowledge alone creates little value if it cannot move. The connection layer focuses on building relationships that enable information, experience, and expertise to travel between individuals and groups. Strong communities create bridges between functions, disciplines, and perspectives that might otherwise remain isolated.
Manuel Castells described modern organizations as increasingly dependent on network structures rather than traditional hierarchical models.⁶ Communities form many of these critical networks, creating connection and cognition pathways through which intelligence can travel.
These connections strengthen trust, reduce duplication, improve collaboration, and create the social infrastructure necessary for collective problem solving.
The activation layer
The ultimate purpose of a community is not simply to connect people. It is to create action. The activation layer focuses on translating knowledge into capability, behavior, and outcomes. This occurs when communities encourage participation, support learning, facilitate experimentation, and create opportunities for members to contribute.
When intelligence, connection, and activation work together, communities become catalysts for innovation and organizational adaptation.
Communities support humanᴬᴵ
The emergence of artificial intelligence does not diminish the importance of communities. It increases it. As intelligence becomes more abundant, organizations face a new challenge. The question is no longer how to access information, it is how to coordinate it.
Microsoft's Work Trend Index consistently shows that employees increasingly rely on peers and informal networks to learn new technologies and develop AI-related capabilities.⁷ Because adoption spreads socially before it spreads structurally, people are learning more and more by observing peers, experimenting together, sharing successes, and collectively navigating uncertainty. Communities provide the environment where this adaptation occurs.
This aligns with the humanᴬᴵ philosophy. The greatest opportunity presented by artificial intelligence is not simply automation, it is the elevation of human capability exponentially amplified through coordinated intelligence.
As organizations move toward ecosystems of distributed intelligence, communities will increasingly include both human and non-human participants. Intelligent agents will contribute knowledge, support decision-making, automate tasks, and influence workflows. Humans will continue to provide judgment, context, creativity, empathy, and purpose.
Together, these interactions will create new forms of collective intelligence that neither humans nor machines could achieve independently.
Communities and adaptation velocity
Throughout history, organizations have competed through scale, capital, technology, and operational efficiency. Increasingly, they will compete through adaptation velocity. The organizations that learn fastest, share knowledge most effectively, and distribute emerging capabilities most broadly will outperform those that cannot.
Communities are one of the few organizational mechanisms specifically designed to accelerate adaptation. Communities can be the heartbeat of an ecosystem. When built with strategic architecture, they can organically reduce friction in knowledge transfer, increase visibility into emerging practices, strengthen trust networks, and help organizations respond more quickly to environmental change.
In this sense, communities are no longer merely cultural assets, they are strategic and living assets.
Communities as operating infrastructure
Organizations often invest heavily in technology, processes, and organizational structures while treating communities as secondary initiatives. Yet communities are frequently the mechanisms through which these investments create value. They influence how knowledge spreads, how trust develops, how culture evolves, and how people adapt to change.
For this reason, communities should no longer be viewed as engagement programs or communication channels, they should be viewed as operating infrastructure. The organizations that thrive in the coming decade will not be distinguished solely by the intelligence they possess. They will be distinguished by how effectively they coordinate intelligence across people, systems, and emerging technologies.
Communities are the environments where that coordination begins. They are the connective tissue of collective cognition, the foundation of adaptation, and one of the most important mechanisms through which organizations transform intelligence into meaningful action.
References
Senge, P. (1990). The Fifth Discipline: The Art and Practice of the Learning Organization.
Edmondson, A. (1999). Psychological Safety and Learning Behavior in Work Teams.
Wenger, E. (1998). Communities of Practice: Learning, Meaning, and Identity.
Woolley, A., Chabris, C., Pentland, A., Hashmi, N., & Malone, T. (2010). Evidence for a Collective Intelligence Factor in the Performance of Human Groups. Science.
Davenport, T. (2005). Thinking for a Living: How to Get Better Performance and Results from Knowledge Workers.
Castells, M. (1996). The Rise of the Network Society.
Microsoft. (2024–2025). Work Trend Index Reports.
Happe, R., & The Community Roundtable. State of Community Management Research.
Malone, T. (2018). Superminds: The Surprising Power of People and Computers Thinking Together.
Devonport research domains 1–8 (2026).