We’re observing a fundamental question being asked across NGOs and philanthropies committed to driving relationship-based system change. They’re investing in creating conditions for change — building field capacity, strengthening relationships, developing leadership, creating the infrastructure for coalition building. And they’re trying to figure out: how do we measure our impact?
Their stakeholders often want to know: What did you produce? What did your investment yield?? Did it work?
But systems change doesn't work like that. Conditions shift, relationships deepen, trust builds slowly. The work is non-linear, relational, and takes time.
So how do you measure something that's fundamentally about creating the conditions for outcomes you can't fully predict or control?
At Workshop, that's a question we’re deeply engaged in and work through with clients every day. The creative thinking happening across the field right now has us energized about what's possible and what we can help build.
The problem isn't rigor. It's the wrong kind of rigor.
Most of the frameworks that exist were built for work that's more linear, more attributable, and more predictable than systems change actually is.
The answer isn't to abandon rigor. It's to apply it differently. Systems change demands a kind of measurement that can hold complexity, track how enabling conditions evolve, and tell you whether the ecosystem is becoming more capable of strategic, coordinated action.
Here are a few of the principles we've found most useful, the ones we return to again and again in our client work.
Near-term Signals, Long-term Change
Because systems change is a long game, the outcomes we most care about may take a decade to materialize, but we also need to make decisions now.
The key is identifying near-term indicators that are predictive of long-term change. Signals that tell us the conditions are building, not just that activity is happening. For example, what does a more capable and coordinated field look like in the near-term, even if the big wins are still years away? What are you watching for that tells you the ecosystem is becoming more capable?
When near-term signals are clearly connected to long-term goals, you can course correct early and adjust your strategy as you go, rather than discovering what worked well after significant investment has already gone one direction.
Control / Influence / Monitor “Bullseye”
One framework we return to often with our clients is what we call the Bullseye, which maps outcomes across three zones: what you can directly control, what you can meaningfully influence, and what you need to monitor for context.
For systems change work, this distinction matters. The third zone, monitoring, is the one most often underdeveloped. Understanding what's happening in the wider field, the policy environment, and the funding landscape is what gives you the intelligence to learn from your results and adapt over time.
Seeing the system across data types and perspectives
A strong MEL design is intentional about combining different types of information: quantitative indicators alongside qualitative signals. But it's equally deliberate about whose perspective is in the room.
What a funder sees, what a grantee experiences, and what a community member observes are often telling different parts of the same story. Without that triangulation built into the design from the start, it's easy to over-rely on a single type of evidence and miss the relational dynamics or emergent patterns that matter most.
MEL as a collaborative practice
A good MEL framework isn't just an architecture for tracking outcomes. It matters how it gets built, and who it gets built for.
The most effective systems are shaped by the people closest to the field, not just those at the top of the funding and governance structure, who help define the learning questions and feedback loops from the start. That participation is what turns the process into a form of field-building in its own right, and produces a framework people will actually use: shared questions build relationships, common frameworks create alignment, and visible findings help people see what they're building toward together. What's really at stake isn't just accountability or learning. It's whether the infrastructure you build for understanding your work also strengthens trust and collaboration within the field.
Systems change is complex to measure because it's complex. The good news is there is a lot of creativity happening in this space. New approaches are emerging that can see what traditional frameworks miss: the power of networks, the value of connected ecosystems, the difference between a single win and shifting the terrain for good. In our next post, we'll explore what some of these look like in practice and what they offer organizations serious about building durable, collaborative change.
