What We’re Learning About Learning: Insights from the Scaling Deep Inquiry
We’ve been deep in our research inquiry—sitting in conversation with practitioners, evaluators, and funders across fields. I get off of every Zoom call inspired by the depth, creativity, and honesty that’s being shared. People are sharing their experiences, challenging long-held assumptions, and imagining better ways forward.
As we continue to explore themes of scaling deep, funding, and evaluation, one pattern keeps rising to the surface: the growing shift from traditional measurement toward collective learning—not just as a buzzword, but as a real, grounded practice that redistributes power and reorients how change happens.
At the System Sanctuary, collective learning has always been baked into our DNA. It’s in how we gather, how we reflect, and even how we define our purpose. We’ve seen again and again that when learning becomes the driver—not just a reporting requirement—it changes who holds knowledge, who gets to ask the questions, and how systems evolve.
This shift challenges old paradigms. Traditional evaluation often centers the expert, the funder, or the leader. But learning—as we’re seeing it practiced—is something different. It’s messy, participatory, and relational. It requires diverse perspectives. It thrives on shared sensemaking. And it moves not through control, but emergence.
Scaling deep challenges dominant assumptions about who produces knowledge, and for what purpose. Traditional evaluation and research methods often extract insights from communities and “participants,” while retaining ownership and authority elsewhere. Collective learning invites a redistribution of that power—centering shared authorship, relational inquiry, and learning as something co-created, not harvested.
“Evaluation should shift from extraction to co-creation. Evaluators become partners in learning, not external judges.”
“Real learning isn’t solitary; it's the shared sensemaking, coming back together and asking: What’s happening here? What does it mean?”
— Inquiry participants
We’re calling the scaffolding that enables this shift learning infrastructure. And though it shows up in many different forms, it tends to serve the same larger function: making it possible to do things differently. Below is a synthesis of what we’re seeing—how learning infrastructure is showing up in practice, and what makes it matter.
Forms of Learning Infrastructure
Learning infrastructure isn’t one thing—it’s many interconnected functions that support people, organizations, and ecosystems in making sense of complexity together. Across our conversations, we’ve seen that while the language may differ, many groups are building similar forms of infrastructure that enable collective learning, emergent strategy, and deeper systems change.
These forms are not static. They’re adaptive, relational, and context-dependent. They help shift evaluation away from oversight and toward inquiry, shift power away from institutions and toward ecosystems, and shift knowledge production away from extraction and toward shared meaning-making.
Here are some of the most common and essential forms of learning infrastructure we’re seeing across the field:
🟡 Systems Change Evaluation Methods
Tools and approaches that move away from rigid metrics and toward insight, adaptation, and real-time responsiveness. More on this soon!
🟡 Sensemaking Platforms
Spaces where people interpret data, reflect on experience, and surface patterns together—not alone, not in silos.
🟡 Networks
Learning-rich ecosystems where relationships enable knowledge to flow, strategies to align, and collective action to emerge.
🟡 Communities of Practice
Peer-driven groups that learn in real time, share what’s working, and evolve together over time.
🟡 Backbone Organizations
Entities that hold shared measurement, coordinate learning, and ensure knowledge moves across—not just up or down.
🟡 Shared Learning Agendas
Co-created priorities for what matters most to learn together—guiding inquiry, not just tracking outcomes.
Key Elements That Make It Work
Having the right structures in place is only part of the equation. What makes learning infrastructure effective—what actually allows it to shift power, foster insight, and support systems change—are the underlying elements that shape how learning happens. These are the practices, norms, and design choices that turn tools into transformation.
They ensure that learning is not just happening, but happening in ways that are inclusive, responsive, and grounded in lived realities. Without these elements, even the best-designed infrastructure risks becoming performative or extractive.
Here are some of the essential ingredients we’ve seen consistently across initiatives doing this work well:
🔵 Culture of Learning
Norms of reflection, curiosity, and shared vulnerability—without which infrastructure won’t stick.
🔵 Peer Learning
Opportunities for sharing across the circle of participants including peer exchange of experience, practices and resources where sharing is rooted in context and lived expertise.
🔵 Feedback Loops
Timely, community-informed insights that inform decisions and build responsiveness.
🔵 Pattern Recognition
Tools and practices to connect dots, surface trends, and make the system visible.
🔵 Knowledge Stewardship
Capturing and curating learning so it survives transitions and fuels long-term work.
🔵 Accessible Storytelling
Turning complex data into usable, relatable narratives so more people can engage.
🔵 Equity-Centered Design
Learning processes that redistribute power, elevate lived experience, and redefine whose knowledge counts.
Beyond the Evaluator: Building Ecosystem Learning Roles
“We need evaluators who are relational weavers—connecting, holding complexity, not just assessing outcomes.”
— Inquiry participant
As someone who came into this work through systems change—building networks, stewarding ecosystem initiatives—it’s been illuminating to connect the dots between systems practice and evaluation. For a long time, these worlds felt separate. But in reality, evaluation has the potential to evolve into something much more powerful: a vehicle for collective learning and strategic insight across an entire system.
Scaling deep asks us to challenge the power dynamics baked into traditional evaluation. Too often, evaluation is something performed for funders—used to assess the “worthiness” of grantees, projects, or partners. It becomes a checkbox, a backward-looking report, rather than a forward-looking tool for learning, reflection, or adaptation.
Even when funders support innovative methodologies—developmental evaluation, participatory methods, feminist approaches—the starting point still matters. If evaluation begins as a tool of oversight or judgment, it’s structurally hard to shift into a space of shared learning and inquiry.
One of the clearest patterns we’ve seen is that learning often remains in service to the funder. Even when funders invest in learning across grantees, it tends to center their perspective and learning goals. This limits the potential for true ecosystem-level initiatives—ones that are non-hierarchical, inquiry-driven, and grounded in collective experience.
It leaves us with an important question:
How might learning infrastructure be re-oriented to serve the ecosystem—not just the organization, funder or institution?
How might it be shaped by systems approaches, feminist values, and emergent strategy—rather than by traditional accountability models?
This shift would require rethinking who holds learning, and how.
Rethinking Roles in Learning Infrastructure
In the field, we’re seeing a range of roles emerge—each contributing differently to how learning is held, facilitated, and activated:
🟢 Weavers & Strategy-Learning Leads
These are the bridge-builders—people who hold the “we” across networks or initiatives. They integrate insights, identify patterns, and connect strategy to learning in real time.
🟢 Researchers Using Action-Based or Participatory Methods
Grounded in inquiry and co-creation, these roles resist extractive knowledge production and instead prioritize relational, contextual research practices.
🟢 Systems Change Facilitators
While their core role is process design and group dynamics, they often hold learning as a parallel track—creating containers where insights emerge through reflection, storytelling, and emergent sensing.
🟢 Visual Scribes and Knowledge Artists
These creative roles are often overlooked, but they play a powerful part in surfacing patterns and making collective learning visible. They translate complexity into shared language and visual narrative.
🟢 Systems Change Evaluators
This includes those using developmental evaluation, participatory approaches, and other emergent methods. They help navigate complexity, track change without reducing it, and bring rigor without closing off emergence.
What’s Still in Tension
“We’re doing this work without stable infrastructure ourselves—fighting for legitimacy, funding, and space.”
- Inquiry participant
Even as collective learning practices gain traction, they still struggle for legitimacy, funding, and long-term support. Many of the people leading this work are doing so without stable infrastructure—often working upstream in systems that continue to privilege metrics over meaning, compliance over curiosity, and control over emergence.
A core barrier is fragmented field architecture. Most organizations still operate as stand-alone entities. Learning infrastructure is hard to fund, hard to package, and often invisible. System leadership and learning functions don’t fit neatly into existing roles or funding categories. Ecosystem-level efforts tend to rely on the passion and persistence of a few, rather than being supported as shared infrastructure.
We also see field-level disruptions—policy shifts, funding cycles, institutional turnover—undermining some of the progress made in building collective learning cultures. It’s a fragile moment.
There’s also a disconnect: we don’t yet have a widely shared understanding—especially between funders and practitioners—about what this work is, why it matters, or how to resource it. As a result, promising approaches remain fragile or small-scale, even when the need for them is clear.
And yet, we believe this shift is essential. Collective learning is not a side function. It’s a different way of doing systems change.
Where We Go From Here
The work of building learning infrastructure is far from finished. If anything, we’re just starting to uncover what’s possible—and what it requires.
As we continue analyzing interviews, gathering case studies, and mapping practices, we’ll be sharing more examples of how learning infrastructure is evolving across contexts. We’re especially interested in stories of people building collective learning in hard-to-fund, hard-to-measure, and hard-to-hold places.
In the meantime, here are a few emerging possibilities we see for moving this work forward:
Create cross-issue (intersectional) connections – Encourage unlikely collaborations, like linking agricultural initiatives with queer organizing efforts. These intersections surface new insights and challenge single-issue thinking.
Develop loose coordination mechanisms – Not everything needs to be centralized. What’s often needed is minimal structure to help efforts stay in relationship, communicate, and align.
Build platforms and networks for knowledge sharing – Create digital or in-person spaces where systems leaders, evaluators, organizers, and funders can exchange learning across silos.
Link levels of work – Scaling deep happens locally, municipally, nationally, and globally. We need pathways for those doing the work at each level to interact, inform, and support one another.
Adopt an ecosystem lens – The key is moving from fragmented initiatives toward a field-level view. What if we designed for mutual learning, distributed leadership, and coherence without uniformity?
This is what learning infrastructure can support—not just better reporting or more rigorous evaluation, but a fundamentally different way of organizing knowledge, power, and change.
In Closing
The System Sanctuary has been practicing collective learning for the past eight years. And we’ve learned—often the hard way—that while the practice is transformative, integrating it into the wider field is not easy. The bigger challenge—and the bigger opportunity—is to shift from siloed efforts and individual initiatives toward collective strategy and shared infrastructure.
This isn’t just a technical shift. Learning infrastructure is deeply political and cultural. It determines who gets to learn, who holds knowledge, and how power moves. When built with care, it can help systems become more just, more adaptive, and more alive.
This is the heart of scaling deep. And it’s not work any of us can do alone. That’s why we’re gathering insights, stories, and case studies from across sectors and scales—to learn how others are building learning infrastructure in complex, grounded, creative ways.
We’ll keep learning in public. And we hope you’ll join us.