This post shares practical reflections on how AI tools can support those working in contexts where collaboration, co-design, and facilitation are important. Drawing from experience, I outline four ways these tools have become quiet thought partners in my work — and reflect on where their strengths end and human judgment, relationships, and systems thinking still matter most.
In my own practice, tools like GPT — and more recently Perplexity — have become quiet companions in the background. I’ve been using AI in this space since it first became more widely available in early 2023 — not to outsource my thinking, but to support it. These tools have helped me with framing, exploring, writing, and preparing across a wide range of projects.
In practice, I’ve found AI tools to be especially helpful when working through ideas — whether I’m drafting a blog post, curating material for the Learning for Sustainability site, designing a workshop, or synthesising interviews and field notes. They help me surface insights, test interpretations, and gain clarity more quickly.
I was struck recently by a post from Michelle Wiles reflecting on AI’s role in consulting. She writes:
“AI will be used as a thought partner to speed up research and get to a plan faster… Persuading teams, building relationships — that’s not work an AI can do (yet).”
While her context is corporate strategy, the insight carries over. For those of us working in participatory, systems-facing settings — particularly in fields such as social research and evaluation — the real work is still relational. It’s about framing, convening, and supporting collaborative thinking and action. AI doesn’t replace that, but it can help us prepare more thoughtfully for it.
Here are four ways I’ve been using these tools in my own practice:
1. Exploring new research and perspectives
As someone who curates open-access resources for the Learning for Sustainability site, I’m regularly reading across disciplines. But when I need to explore a new topic, trace emerging framings, or look for fresh insights, GPT and Perplexity have become helpful companions.
With the right prompts, they can surface recent academic articles, policy papers, and blogs that might not turn up in a standard search. And while false citations used to be a problem, it’s far less common now — though I still always check and read the sources before drawing on them to ensure accuracy. It’s not a shortcut for reading, but a smart way to broaden the field and surface new material.
2. Supporting systems framing and complexity
Framing, not fixing. In systems work, I often find myself dealing with issues that don’t have tidy answers. Whether I’m preparing a theory of change, shaping a facilitation run-sheet, synthesising interviews and field notes, or developing reflective prompts for group work — these are all acts of framing. AI can’t do that work for me, but it can help surface framings, contrast assumptions, and reveal tensions I might otherwise miss.
Sometimes I use it to trial different starting points for a session, or to test how a set of themes might cluster. Other times it helps me rehearse arguments or explore ways to present a tricky issue. In each case, it provides scaffolding that helps me arrive at something more grounded and useful — part of what I described more fully in this post on systems framing and design.
3. Making writing less lonely
When writing — whether a blog post or a journal article draft — I often start with scattered notes and phrases. Having an AI model reframe those into a rough structure, or suggest alternative phrasings, has been surprisingly helpful. It’s like having a writing partner who never tires of being asked: “Can you say that more clearly?”
This kind of support doesn’t replace the creative process — it supports it. When time is short, it helps shape raw ideas into something usable so I can move more quickly into the parts that demand deeper care. It keeps the momentum going, even on slow days.
4. Testing how things land
Another way I use these tools is to explore how a piece of writing might be interpreted by different audiences. I might ask: How might this come across to a biophysical scientist? A social researcher? A policymaker? A practitioner working on the ground? It’s not about accuracy — it’s about stretching perspective and checking for potential blind spots.
This has become a regular part of how I write and review — especially when preparing public-facing material or strategic documents. It helps clarify tone, anticipate misreadings, and make language more inclusive. But it can’t stand in for lived experience or cultural ways of knowing — and it’s important to stay mindful of that, especially when working in equity-focused or cross-cultural spaces. This kind of reflective adaptation is key to how I think about co-design in complex settings.
Working with AI tools, I’ve found it important to think carefully about the ethical implications — especially around data sensitivity, privacy, and being transparent about when and how these tools are used. I’m also mindful of concerns around AI-generated content and source attribution — including the risk of unknowingly drawing on material that hasn’t been shared with permission. In work that values transparency and shared knowledge, these are important ethical boundaries to navigate.
Working in complementary ways
The examples above highlight how AI tools can support real work without replacing it. In practice, I’ve found it helpful to think about where these tools complement — rather than compete with — human judgment, facilitation, and collaboration. The table below sets out some of those distinctions, based on how I use AI in day-to-day practice.
Table: What AI can and can’t do in collaborative research and facilitation
| Task/Role | AI Strengths | Human Strengths |
| Literature search & synthesis | Rapid scanning, surfacing new sources | Critical reading, contextual judgment |
| Qualitative analysis & synthesis | Summarising interviews, clustering themes | Meaning-making, context, ethical interpretation |
| Framing & systems thinking | Suggesting framings, testing assumptions | Coherence, relevance, group-based interpretation |
| Drafting & rewriting | Generating outlines, phrasings, summaries | Voice, narrative shaping, purpose-driven writing |
| Testing audience response | Simulating reactions, checking tone | Knowing what matters, inclusive framing, cultural nuance |
| Facilitation & relationship work | (No real capability) | Trust, empathy, power dynamics, situational judgment |
What remains ours
Looking across that table, it’s clear that while AI can assist with analysis, drafting, and exploration — it can’t replicate the human dimensions that sit at the heart of participatory and systems-facing work.
What AI doesn’t do — and certainly won’t any time soon — is build relationships, facilitate shared understanding, or navigate power dynamics. Those are still human tasks. They require trust, timing, emotional intelligence, and a deep understanding of context.
In collaborative research, evaluation, and systems change work, success isn’t just about generating insight. It’s about connecting the dots ahead of time — spotting patterns, surfacing tensions, and helping people find clarity in complexity. It’s also about influence: shaping decisions, earning buy-in, and building shared momentum around ideas that matter — themes I’ve reflected on in more depth here.
At the core, what matters most is not the tool itself, but how we use it — and who we use it with. For me, these technologies work best when they stay in the background, helping me show up more clearly and thoughtfully in the real work of collaboration, learning, and systems change.
More information on the use of AI can be found through the LfS generative AI landing page. This includes links to the related resource pages Using AI in research and practice and and AI in context: the wider picture. A few related posts from that collection include:
- AI prompts for shared thinking: a light framework for purposeful prompting
- Working with AI in the room: authorship, responsibility, and collective judgement
- Seeing the wider ethical picture around AI development and use
- AI in place-based practice: what is shifting
[* Photo by:ThisIsEngineering | Pexcels]
