How do you pick the right people to lead a project when there’s no perfect data → just a lot at stake?
That’s the challenge we faced when setting up our Innovation Lab, part of a project to help tackle family separation in Roma communities.
🔎 Quick background
The Lab’s goal was to co-design new solutions through a community-led, collective process — grounded in partnership, disrupting power dynamics, and learning through testing.
Finding the right local co-convenors was critical. They wouldn’t just lead the work. They’d ensure it was rooted in real community needs.
But reliable data was missing. So we built a process based on relationships, local insight, and trust.
Here’s how we approached it, and what we learned along the way:
1. Start with the problem, not the place
We initially tried to identify ‘high-need’ areas based on data about family separation. But it wasn’t specific or accessible enough to be useful.
So we pivoted. From finding the right place to finding the right people: those already trusted in communities, already asking tough questions, and already supporting Roma families.
Biggest learning:
When data is limited, start with people who know the landscape. And trust lived experience as a valid form of evidence.
2. Open the door widely, then listen carefully
We shared an open call through NGO portals and informal networks. We received eight responses from across Bulgaria!
We followed up with every respondent, not just to assess them, but to learn from them. These conversations helped us understand community context and co-design the selection process in real time.
Biggest learning:
The selection process is part of the work. It builds trust and surfaces insight.
3. Know what really matters. And weight your criteria!
We assessed applicants using a clear set of values-led criteria: trust, community embeddedness, interest in working in a new way, experience facilitating, and relevance to the issue.
But we didn’t treat every factor equally. Motivation and trust mattered more than formal credentials or ‘ideal’ locations.
Biggest learning:
Engagement and credibility within the community outweighed technical experience.
4. Choose for difference, not similarity
We ended up selecting two very different locations and co-convenor teams. Not by accident.
We wanted contrasting contexts to test different approaches and challenge assumptions.
Biggest learning:
Diversity in setting sharpens insight and strengthens learning.
5. Don’t lose valuable voices
Some applicants weren’t selected as local co-convenors, but we still invited them to join our non-geographical Lab group. This made sure their experience informed the broader process.
Biggest learning:
Create space for contribution beyond binary ‘yes/no’ decisions.
In summary?
This process wasn’t perfect. Nor linear! But it worked.
The co-convenors we selected are shaping the Lab in their communities in ways we couldn’t have designed from the outside.
If you’re working on something similar, we invite you to steal these insights and adapt them.
