Conversations about AI futures often sit at a distance from practice, focusing on policy, risk, or speculation. This post begins from the ground: from the everyday work of evaluation, facilitation, and collaborative decision-making in place-based settings, where the issues at stake are often environmental, long-term, and contested. Drawing on futures thinking, it reflects on what seems to be changing as AI enters this work, what remains uncertain, and what others may be noticing in their own practice.
In place-based settings where people are working through complex, real-world issues together, often in catchments, regions, or institutional contexts, relationships and responsibilities persist beyond any single programme. Here, the future often arrives in quieter ways, through small shifts in how work is done rather than in dramatic change.
This shows up in the everyday work of evaluation, facilitation, and collaborative decision-making, often focused on issues like freshwater management, land use, or community change. In these settings, conversations are not abstract. They are about real places, with long-term consequences, where different perspectives and interests need to be worked through together.
In this context, AI is beginning to shape how people prepare, how ideas are formed, and how groups make sense of what is happening and what to do next. Alongside thinking about where AI is heading, it is useful to notice how it is already entering this work, and what seems to be shifting as a result.
In earlier posts, I’ve explored AI as a support for thinking, and the ethical questions that sit around its use. Here, I want to take a slightly different step. Drawing on futures thinking, not as prediction but as a way of noticing patterns of change, I’ve been reflecting on what seems to be moving, what feels more established, and what is only just beginning to appear.
Noticing what is shifting
One way I’ve found helpful is to think in terms of movement across practice. Not as a formal method, but as a way of noticing what ways of working are receding, which pressures or drivers are becoming more prominent, and what new possibilities, or risks, are just beginning to come into view.
Some assumptions that felt stable not long ago are starting to loosen. The idea that AI is optional, or something that sits at the edges of practice, feels less certain. In many settings, it is already part of how people prepare, draft, and reflect, whether this is named explicitly or not.
Questions about authorship, accountability, and transparency are becoming more visible, particularly in shared documents and collaborative processes. Teams are beginning to ask how AI-generated material should be treated, and where responsibility sits when outputs are collectively produced but partly generated.
Alongside this, expectations around speed and polish seem to be shifting. Early drafts can be produced quickly and fluently. This can be helpful, especially in the early stages of thinking. But it also changes the rhythm of work. There is less time spent sitting with partial ideas, and more emphasis on shaping and editing material that is already well-formed.
In place-based settings, this has a particular edge. AI-generated summaries can give a sense of coherence around issues that are, in reality, complex and contested. In work on catchments or land use, for example, differences in values, knowledge, and priorities are often central to the task. When those differences are smoothed too early, there is a risk that what matters most in a particular place becomes less visible.
This connects with something else that is beginning to surface. In the past, much early-career learning has come from working through messy material, drafting, reworking, and making sense of place-specific information. If more of that early work is now supported by AI, it raises questions about how that judgement develops over time, particularly in place-based work where learning is closely tied to context and to working through real, contested situations. It is not yet clear what this will mean in practice, but it feels like something to watch.
There are also early signals that expectations are beginning to move at an organisational level. Some funders and commissioners are starting to ask about AI use, or to assume a level of AI-assisted work, even where norms are not yet well defined. In some cases, this is happening ahead of clear guidance, leaving teams to work things out in real time as they navigate both the technology and the issues at hand.
What feels less settled
Alongside these more visible shifts, there are also areas where things feel less clear, particularly as these tools begin to shape how people come into conversations and how issues are framed.
One question is how collective judgement is shaped by what enters the room. If participants arrive having used AI to prepare their inputs, this may broaden the range of ideas. At the same time, AI-assisted preparation may introduce a degree of convergence in how issues are framed. In place-based work, where differences in perspective are often central, it is not yet obvious how this will play out.
There are also questions about what happens to the relational qualities that underpin this kind of work. Trust, timing, and presence are not easily captured in generated text, yet they remain central when people are working through contested issues about land, water, or community futures. It is unclear whether these aspects will become more visible as other parts of the work are supported, or whether they risk being taken for granted.
Another area of uncertainty is how organisational cultures will adapt. Some may develop deliberate approaches to AI use, creating space to reflect on how it is used and what it means in context. Others may move more quickly, driven by expectations of efficiency or output, without fully working through the implications. In practice, this variation is already beginning to show in different ways.
More broadly, I find myself wondering about the diversity of perspectives in collaborative processes. In place-based settings, local knowledge, lived experience, and different ways of seeing a situation are often critical. If AI tends to smooth language or align arguments, there is a possibility that these differences become less visible. At the same time, these tools can also help surface new connections or bring in wider perspectives. The pattern is not yet clear.
There is also a question about how expectations of practice may shift over time. If AI-assisted drafting and synthesis become normal, what is seen as competent or thorough work may change. That may bring benefits in some areas, but it may also alter how judgement is recognised and developed. At this stage, these changes are uneven and still forming.
What organisations and teams are starting to work through
In response to these shifts, there is a growing recognition that organisations and agencies need to become more deliberate about how AI is used in practice. In some areas, guidance is beginning to take shape, but much of this is still evolving.
These are not settled approaches, but they point to areas that groups are already working through, where some early indications of useful practice are beginning to take shape:
- Making use visible. Being clear about when and how AI has contributed to drafts, analysis, or synthesis, particularly in shared documents or decision-making processes.
- Keeping responsibility for judgement with those involved. AI can support thinking, but responsibility for interpretation, evidence, and decisions remains with the people and teams involved.
- Checking and grounding outputs. Treating AI-generated material as a starting point that needs to be tested against context, evidence, and local knowledge, rather than accepted at face value.
- Being selective about where it is used. Recognising that some aspects of work, particularly those involving sensitive relationships, local knowledge, or contested issues, may require more care or different approaches.
- Supporting staff and shared learning. Creating space for teams to experiment, reflect, and develop shared understanding about how these tools are used in their particular context.
In place-based settings, these are not just technical choices. They shape how conversations unfold, whose knowledge is visible, and how decisions are made. For many organisations, the challenge is less about having the right policy in place, and more about supporting ongoing discussion and shared judgement as these practices evolve.
What seems worth holding onto
In the midst of these shifts, some aspects of practice still feel steady, particularly where judgement, relationships, and collective sense-making remain central.
- Judgement still needs to be exercised. The presence of fluent, well-structured text does not remove the need to ask where ideas come from, how they are supported, and what they mean in a particular place. In work on catchments, land use, or community futures, this often involves working with partial evidence, different knowledge systems, and real consequences over time.
- Relationships also remain central. The work of building trust, understanding different perspectives, and staying with difficult conversations is not replaced by faster drafting or synthesis. In many place-based settings, these relationships are what make it possible to work through contested issues and move toward shared action.
- Collective sense-making continues to matter. The value in many of the processes I’m involved in lies not just in the outputs, but in the conversations that shape them. It is through these interactions that assumptions are surfaced, differences are worked through, and decisions begin to take form.
These are not simply things that AI cannot do. They are aspects of practice that matter because they are shared, situated, and developed over time, and because they shape what actually happens in the places people care about.
Closing reflection
I’ve used a futures lens here not to predict what will happen, but to help notice how AI is already entering practice, and where questions are beginning to surface. Different people will be seeing different patterns, depending on the contexts they work in. Some of what I’ve described may resonate. Other aspects may look quite different elsewhere.
It may be useful to take a moment to consider what is becoming visible in your own practice as AI becomes part of the workplace. What feels like it is changing, what remains uncertain, and what is only just beginning to take shape?
This post is part of a small set of reflections on using AI in practice, from individual use through to shared work and wider ethical considerations. You can explore the full set on the AI for reflective practice, research, and collaboration hub. A few related posts from that collection include:
- AI as a thought partner: reflections on collaborative practice and systems work
- 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
- Working with AI: where to begin
[* Image by Jintana / Adobe Stock]
