These essays explore what happens when generative AI becomes part of professional work. Drawing on facilitation, evaluation, organisational learning and systems thinking, they consider how AI changes inquiry, participation, interpretation, judgement and learning.
Rather than focusing on the technology itself, the collection asks what better work with AI would look like, and how practitioners, teams and organisations can use AI in ways that strengthen rather than weaken professional practice.
The essays can be read individually or as a developing sequence, moving from personal and collaborative use through to evaluation, ethics and organisational learning.
Essays
AI as a thought partner: reflections on collaborative practice and systems work
A reflective post sharing personal experience of using AI as a quiet thought partner in facilitation, writing, and systems-focused work. It offers practical examples of how AI can support early thinking and drafting, while reinforcing the importance of relational, human-led practice.
AI prompts for shared thinking: a light framework for purposeful prompting
Introduces a simple prompting framework to support thinking, writing, and preparation. The approach keeps context, relationships, and human purpose in view when using AI tools. It provides a small set of practices that can help teams bring intentionality to how they engage with AI.
Working with AI in the room: authorship, responsibility, and collective judgement
Reflects on what changes when AI moves from individual use into shared work such as meetings, workshops, strategy sessions, and collaborative writing. The post explores how AI intersects with authorship, responsibility, trust, and collective judgement, and why unease often centres less on the tools themselves than on how shared sense-making is held.
AI and the authority of the first synthesis
Looks at what changes when AI is used to prepare early syntheses for teams, workshops and collaborative processes. This post explores how AI-assisted drafts can shape what a group notices, questions and treats as ready to work from, and why early syntheses need to be examined rather than simply improved. Useful for facilitators, evaluators and programme leads working with AI-generated summaries, themes, strategies or discussion papers.
AI can produce the record of participation without the participation
Examines what happens when AI can generate many of the visible products used to evidence participation, including transcripts, themes and reports. The post asks what those artefacts leave out, which forms of effort contribute to shared understanding, and how organisations can distinguish meaningful participation from a convincing account of it.
How do we know whether AI is improving the work?
Examines how to evaluate AI-supported products, services and professional practice beyond the immediate quality of their outputs. The post connects intended contribution, evidence, shared judgement and adaptation, drawing on established approaches to indicators, evaluative rubrics and monitoring, evaluation and learning. A composite consultation example shows how an important issue can be followed from an AI-generated summary into later discussion and decision-making.
What would better work with AI look like?
Brings together the main themes across the collection by asking what happens to professional practice when AI becomes part of everyday work. The post considers how practitioners, teams and smaller organisations can decide what better work would mean, notice what AI is changing around the work, and learn where its use should continue, change or stop. It connects questions of purpose, participation, interpretation, responsibility and organisational learning.
AI in place-based practice: what is shifting
Reflects on how AI is beginning to shape collaborative, place-based work such as freshwater management, land use, and community decision-making. Drawing on a futures lens, the post explores what is changing in how people prepare, frame issues, and make sense of complex situations, alongside emerging organisational responses. It highlights areas of uncertainty while reaffirming the importance of judgement, relationships, and collective sense-making in real-world settings.
Seeing the wider ethical picture around AI development and use
Maps the ethical landscape around AI by distinguishing between structural concerns, such as infrastructure, labour, and power, and practice-level issues that arise in everyday use. The post explores questions of reliability, judgement, and responsibility, and helps clarify why debates about AI often feel polarised. It offers a grounded way to understand what is at stake without reducing the discussion to simple positions.
Working with AI: where to begin
Offers a practical starting point for organisations, teams, or programmes thinking about how to engage with AI more deliberately. The post outlines five areas to work through, from getting the right people involved to building in regular review, with questions to help begin those conversations. It focuses on starting with what is already happening and building shared understanding through use, rather than putting a complete framework in place from the outset.
[* Image by Jintana / Adobe Stock]