Helping Climate Advocates Navigate AI on Their Own Terms
At a Glance
Client: Climate Advocacy Lab
Engagement: Custom Consulting — AI Training Series Design & Delivery
Duration: December 2025 – April 2026 (scoping through delivery and evaluation)
Co-consulting partner: Ryann Miller, Spark & Signal
Core challenge: Equipping the Climate Advocacy Lab to strengthen the climate movement through AI education by providing a space for climate advocates to explore AI responsibly.
Key outcome: A six-session training series that met participants where they were, built confidence across a wide spectrum of readiness (9.0/10 NPS recommendation score), and created a replicable model for the Lab's member organizations.
When the Climate Advocacy Lab reached out about building an AI training series for their members we knew the first question we’d have to answer was: How do you help people explore such a disruptive technology that is actively (and harmfully) contributing to the very problem they’re working to solve? We couldn’t begin with simply what tools should be used for what use-cases. We had to address the elephant in the room.
That tension between AI's potential to help advocacy organizations deliver on their mission more effectively and its very real environmental and community costs shaped everything about this engagement. And what we learned is that leading with this complexity allowed for a deeper and more authentic learning experience.
The Context
The Climate Advocacy Lab is a membership organization serving over 5,000 climate advocates — organizers, communications staff, researchers, funders, and organizational leaders working across the environmental movement. Their members represent organizations ranging from the Sierra Club to small community-based groups like a renters' rights coalition in Buffalo. The Lab had already committed to developing an AI Knowledge Hub as a centralized resource for how AI is being tested and deployed in climate campaigns. The training series was the first major piece of that initiative.
But this audience came with a particular set of concerns, as we learned through our initial expression of interest outreach. Some were expressing genuine curiosity while others were naming deep concern about AI's climate impact, and several were flat-out opposed to any use of AI. The people signing up weren't going to be part of a contained cohort; they would self-select into individual sessions based on interest, and they'd be bringing different levels of comfort and skepticism. Some would be already using AI tools (whether openly or not). Others weren't sure they should be. Others would be working within organizations that were already down the path of responsible AI use while some might have no organizational direction at all.
The Lab needed more than a typical training vendor. They needed partners who could hold the climate conversation with credibility, design for a diverse and shifting audience, and build something that positioned the Lab as a thoughtful, practical resource for their community.
The Approach
Ryann Miller of Spark & Signal and I came to this project as a team — Ryann through her existing relationship with the Lab and deep expertise in progressive nonprofit digital and AI strategy, nonprofit change management and business development, and me through my background in instructional design, organizational learning, and AI strategy for mission-driven organizations. We knew from the start that complementary skills would serve this project better than a solo practitioner.
The engagement unfolded in three phases:
Scoping and co-design (December–January). We worked closely with Raz Pollex and the Lab team to understand not just what they wanted to teach in terms of content, but who their members are, how they learn, what makes for the most useful and engaging webinar experience. We weren't handed a content brief and asked to build slides. We were invited into the thinking and visioning for the series.
Iterative curriculum development and delivery (February–April). We designed six sessions that connected to each other while remaining accessible to drop-in participants. The arc moved from climate implications and responsible use, through AI foundations and governance, to advanced skills, real-world advocacy applications, and the future of work. Each session balanced conceptual grounding with hands-on, practical takeaways — because we've learned that every group wants both theory and practice, and you can't front-load one without losing people.
Evaluation and debrief (April). Following the final session, we conducted a series debrief with the Lab team and gathered participant feedback through a post-series survey. Following the final session, we conducted a series debrief with the Lab team and gathered participant feedback through a post-series survey (17 respondents).
What made this engagement distinctive was how we built it. We didn't come in with polished decks. We came in with deep content knowledge that we shaped specifically for this community. Every design choice was responsive to what we were learning about the participants and what was happening in the broader conversation about AI in the environmental sector.
Our big first decision: We led with the climate question. Whereas many AI training sessions begin with a 101 and primer on AI fluency, we felt that our approach had to be different. We designed Session 1 to open with AI's environmental footprint including energy use, water consumption, data center siting, environmental justice. We knew that if we didn't address the elephant in the room first, nothing else would land. Pedagogically, this made sense: the first question a climate advocate needs to answer is "Can I even touch this?", so the series had to start there. This was a risk that we weren’t sure would land, but it turned out to be a really critical moment that built safety, credibility, and trust with the participants.
Our other key design choices and what we learned:
We brought in guest voices at the opening and closing. We featured a guest in Session 1 to ground the climate conversation in real organizing experience, and invited the Lab’s ED to Session 6 to speak to what AI means for teams and institutional capacity. This was an intentional choice. Having guests in the bookend sessions positioned us as partners in this learning alongside the community – equally curious, not just experts delivering knowledge. These sessions were incredibly well received and helped expand the perspectives for participants.
We designed for the full room. In any given session, we were speaking to skeptics who weren't sure AI belonged in advocacy, individual users looking for practical guidance, team leaders thinking about governance, and organizational leaders weighing policy decisions. We addressed all of them by moving intentionally between individual, team, and organizational frames. The session on governance, for example, introduced three distinct layers (policy, guidance, and learning culture) so that someone at any level could see where they fit.
We built in real case studies from the advocacy world. Session 5 featured three concrete examples — the Lab's own paid media analytics project, Fair Count's use of AI in GOTV canvassing, and the Wilderness Society's donor outreach — so participants could see AI applied in contexts they recognized. We weren't teaching abstract “capability development”. We were showing what it looks like when people like them actually do this.
What Shifted
“It was encouraging and exploratory without the guilt.” - Training participant
What we observed over the course of the series was a room that kept showing up and kept engaging. Attendance was considerably higher than the Lab's typical training sessions. Chat participation, often a good proxy for how comfortable people felt contributing, was consistently above what the Lab team was accustomed to. And the questions varied from surfaced anxiety and broad ethical concerns to specifics: "How would I set this up for my team?" "What does a governance conversation actually look like at a 10-person org?"
From the client's perspective, the Lab gained clarity on what they want to do next to support their members around AI, and they recognized that this series was a valuable model for beginning their engagement with members around the topic of AI.
Outcomes
Six sessions delivered between March and April 2026
Attendance: 987 total registrations; 337 total attendees across the series
Chat engagement: 624 participant messages, with participation rates between 41–67%
NPS-equivalent recommendation score: 9.0/10 (62% gave a perfect 10)
100% of survey respondents took at least one concrete action
86% shared learnings with colleagues outside the training
71% started team conversations about AI governance
The series also produced a substantial body of materials (session slides, run-of-show documents, curated resource lists, facilitation guides) that the Lab can continue to use and adapt as their AI work evolves.
Perhaps most importantly, this engagement surfaced a scalable model. The structure we designed (values-first opening → foundations → governance → advanced skills → applied use cases → future implications) is adaptable for member organizations with their own AI learning needs. That replicability is part of what makes this work meaningful beyond a single training series.
"I loved working with Valerie and Ryann. When we started working together, we knew what we wanted (a multipart AI series for climate advocates), but we weren't sure where to start with such a huge and contentious topic. Valerie and Ryann really helped us narrow in on the most important topics, as well as the right approach. They were able to design a curriculum that met our learning goals while also pushing us to expand those goals. They were also easy to work with - professional, collegial, and very focused. We also heard great feedback from webinar attendees."
"The CAL AI training series helped me organize and refine my thinking on how we should govern AI at our organization. The series included policy tips, thoughtful conversations on the ethics and environmental impacts of AI use, and guidance on best practices. 10/10" — Training participant
Reflection
What Ryann and I feel especially proud of with this project is that we held and managed the messy, complicated reality of the climate movement in its early stage of trying to make sense of AI. AI in climate advocacy is genuinely complicated — environmentally, ethically, practically — and we didn't smooth that over. We designed an experience that leveraged our empathy, humor, and flexibility to hold a safe space for a range of questions. We built a structure that could hold nuance, complexity, and real disagreement, while still engaging people. We moved at an intentional pace. We zoomed in and out between the theoretical and the applied. We addressed the skeptics and the enthusiasts and the leaders who just needed to know what to tell their staff or boards.
And in every session, we did it in a way that invited everyone into the room.
That's what we think good AI capacity building looks like for mission-driven organizations: not pushing people toward adoption, not protecting them from it, but creating the conditions for them to figure out what's right for their work, their values, and their communities. And then giving them the skills and frameworks to act on what they decide.
Interested in exploring something similar for your team or organization? Let's start a conversation.