From workshops to workflows: a journey with AI CoLab and Dragonfly Thinking

By Lize van der Walt, Engagement and Insights Lead – AI CoLab, Commonwealth Treasury

When Chloe Tallentire first attended an AI CoLab workshop, she didn’t anticipate it would lead directly to reshaping how her own team approached their most pressing challenges. Yet, through a collaboration between Dragonfly Thinking and the AI CoLab, Chloe created something genuinely innovative: a practical, structured foresighting tool designed specifically to empower smaller APS teams to spot hidden risks and better navigate complexity with confidence.

Chloe’s journey highlights the broader ambition of the AI CoLab, a cross-sector alliance supported through the APS Capability Reinvestment Fund’s “AI for Integration and Inclusion” initiative. Under this initiative, the Department of Industry, Science and Resources partnered closely with the AI CoLab and Dragonfly Thinking to pilot practical uses of structured AI tools. The pilot specifically encouraged teams across multiple APS agencies to codesign and experiment collaboratively, actively ensuring the practical relevance and applicability of these AI innovations to real-world policy, strategy and risk management needs.

Dragonfly Thinking in the AI CoLab Environment

Central to this collaboration was the Dragonfly Thinking RRR AI tool, which uniquely combines structured methodologies - including the Risk, Reward and Resilience framework - with generative AI. The Dragonfly Thinking tool guides teams step-by-step through systematic reasoning processes, mapping complex issues, uncovering hidden risk and exploring strategic alternatives collaboratively. These methods provided pilot participants with structured pathways to approach complexity with greater confidence and clarity.

Throughout the pilot, Dragonfly’s team actively collaborated with the AI CoLab to facilitate hands-on workshops, enabling participants from across 20 APS organisations to test these analytic methods directly on authentic policy and risk challenges. These structured environments supported practical experimentation, linking tools directly to participants' day-to-day work.

Chloe’s Journey: From Idea to Impact

Chloe, then a risk advisor at the Department of Finance, exemplifies what this collaborative codesign approach enabled. Through active participation in regular AI CoLab workshops, Chloe developed a structured risk foresighting tool specifically designed to assist smaller APS teams without dedicated analytic resources. This tool allowed teams to proactively identify gaps between their internal risk registers and external indicators, such as OECD reports and global risk indexes.

Reflecting on the development process, Chloe described the clear practical value she experienced using Dragonfly’s structured approach:

‘I’d used AI quite a lot but I had never seen a tool like Dragonfly before. It gave me something concrete I could show, adapt and use.’

Chloe also emphasised how the broader AI CoLab environment significantly strengthened her technical knowledge and confidence, particularly highlighting a practical session delivered by Felix Barbalet, a GovHack winner and independent software developer. Felix’s session provided a hands-on demonstration of Google's AI tools, including NotebookLM for turning policy documents into podcasts, as well as AI Studio, BigQuery, and Gemini for sentiment analysis, data classification and public outreach. Chloe described this particular workshop as ‘a real eye opener,’ significantly broadening her perspective on how AI could practically support her day-to-day work.

An image of Chloe Tallentire in a meeting room leading a discussion on Dragonfly Thinking

Through iterative codesign - testing, refining, and incorporating direct feedback from her colleagues - Chloe successfully integrated the foresighting tool into her team’s operational practices. She applied the prototype to drafting committee briefings and supporting strategic conversations about organisational capabilities and risk analysis. Chloe highlighted the tangible impact on her team’s workflows:

‘With Dragonfly’s support, we were able to do the kind of comparative risk work that would normally take days, freeing up time for strategic thinking.’

Chloe’s experience clearly demonstrates how the AI CoLab, through structured collaboration, enabled the development of innovative, practical solutions to real policy challenges.

Shared Learning and Collaborative Benefits

Chloe's experience resonated with other participants from across multiple APS agencies, who similarly emphasised how engaging with Dragonfly Thinking within the AI CoLab environment helped them strengthen their own capabilities and confidence. One participant articulated clear improvements in their research depth and efficiency:

‘Dragonfly expanded the depth of my research by at least three times and cut discovery time from weeks to days.’

Another highlighted the shift in team dynamics enabled by Dragonfly’s structured methods:

‘We started using it to explain our thinking to others. That’s when it clicked - we were using AI to align the team, not just analyse the problem.’

A participant focusing specifically on resilience offered another clear insight into the structured approach’s value:

‘Dragonfly’s structured approach supports the kind of thinking that human teams often find difficult to sustain unaided ‘

Together, these reflections illustrate the real-world impact of structured codesign environments such as those facilitated by AI CoLab and Dragonfly Thinking—strengthening decision-making, building shared capability, and growing professional confidence.

Looking Ahead: Sustaining Collaborative Momentum

Dragonfly Thinking continues its active involvement with the AI CoLab Alliance, building ongoing interest across diverse APS agencies. Insights from the pilot reinforce the essential role of structured, collaborative AI methodologies in addressing complexity, providing clear pathways forward for teams seeking responsible and practical analytic solutions.

The Dragonfly–AI CoLab collaboration shows that structured methods can position AI as a trusted, practical partner in decision-making. Collaborative experimentation actively accelerates collective learning and capability-building across teams, ensuring that practical innovations translate into sustainable, impactful solutions. Above all, structured collaborative reasoning delivers longer-lasting benefits and deeper strategic clarity than rapid, isolated analysis ever could.

Ultimately, Chloe’s journey and the broader pilot experience clearly illustrates the practical, human impact of collaborative AI codesign: not just as a technical achievement, but as a way to help APS teams approach complexity deliberately, together, and with greater clarity.