Note from Joost about AI Co-Creation:
This article was created in dialogue with my co-creative AI sparring partner. It started with my personal reflections from the week. The AI helped identify a relevant theme, asked targeted questions to deepen the insights, and then drafted this piece using my input and answers.
While the AI structured and drafted the text based on our interaction, the core ideas, experiences, and insights are mine. I've edited the result carefully to ensure it accurately reflects my voice, perspective, and intent, turning raw reflection into a shareable 'field note'.
My aim remains to foster an environment where we can learn together, and to embrace curiosity about these new ways of working and the insights they can help surface.
When AI sparks ownership: reflections on co-creating a transformation plan
Sometimes, reflecting on a recent experiment can yield unexpected insights. I'm currently involved in supporting a large-scale transformation plan within a healthcare organization – a complex process already well underway. Recently, the project lead and I introduced an AI partner, specifically Dembrane, to help synthesize one of our working sessions. A follow-up conversation with the manager overseeing the entire initiative, reflecting on this initial experience working with AI, sparked some profound thoughts on collaboration, technology, and ownership. It highlighted the evolving ways we can partner with AI, moving far beyond simple efficiency gains.
The moment of recognition: beyond fixing the output
What struck me most during our conversation wasn't just the acknowledgment that AI helps converge ideas faster, although that's certainly valuable. It was a more nuanced point the manager made about interacting with AI-generated content, based on his reflection of the output from our first AI-assisted session.
He described the common scenario: you ask AI to draft something, you get it back, and you're not quite satisfied. The immediate temptation, he noted, is to jump in and start tweaking the text yourself. "But," he mused (and I'm paraphrasing slightly), "if you pause and instead choose to give the AI more feedback, more suggestions on how to improve it, you often end up with something that aligns much better with what you actually need."
This resonated deeply. It’s a shift from viewing AI as a simple instrument that produces a first draft to be fixed, towards seeing it as a collaborative partner you can guide and iterate with. It’s about engaging in a dialogue with the technology to refine the output, rather than just taking the initial result and running with it (or fixing it yourself). Hearing this level of understanding about iterative AI collaboration from a manager focused on the complexities of healthcare transformation felt significant. It suggested a readiness to explore AI's potential on a deeper level within this ongoing project.
From insight to action: can AI help weave ownership?
This insight opened up a fascinating conversation. The manager acknowledged the inherent "politics" of such a large-scale transformation – the many stakeholders who need to sign off on the plan, each with their own perspectives, worries, and interests. How could we ensure the final plan genuinely reflects these diverse viewpoints and fosters a sense of shared ownership?
An idea emerged: what if we could work with AI not just to document, but to actively help navigate these complex dynamics?
We talked about potentially interviewing key stakeholders, perhaps starting with the manager himself, to map out the underlying "rules of the game" – the forces, concerns, and values at play within the organization. This understanding could then inform how we collaborate with AI throughout the process.
Imagine using these insights to craft specific AI prompts designed to:
- Facilitate dialogue: Structuring conversations in a way that surfaces and acknowledges different perspectives respectfully.
- Synthesize and feedback: Analyzing conversations to extract not just key themes, but also the specific language and, crucially, the overlapping values expressed by different stakeholders.
The goal would be to produce summaries and eventually a draft plan where individuals and groups can genuinely recognize their input and feel assured that their core concerns and values are reflected. "Ah yes," they might think, "if we approach it like this, I can see myself in this plan."
We even floated the idea of incorporating AI collaboration directly into sessions with the group of leaders who will ultimately sign off on the plan, making the co-creation process itself more transparent and iterative.
Connecting the dots: AI stimulating bottom-up effects?
Now, the explicit working style for this transformation plan isn't necessarily defined as "bottom-up." However, my own experience in facilitating bottom-up change tells me that fostering ownership and ensuring people see their contributions reflected are fundamental principles.
And this is where AI could offer intriguing possibilities, perhaps creating a hybrid approach within the existing structure. By consciously working with AI to:
- Listen deeply: Capture the nuances of stakeholder input across various conversations.
- Perform meta-analysis: Identify recurring themes, core values, and potential points of friction or alignment across different groups.
- Ensure representation: Help ensure that the language and priorities of diverse stakeholders are woven into the fabric of the plan.
- Enable iteration: Continuously refine our prompts and even our facilitation methods based on the insights surfaced through our AI collaboration.
...we might be able to stimulate some of the effects of a bottom-up process. It’s about partnering with technology to enhance our ability to listen, synthesize, and reflect the collective voice in a way that builds trust and commitment. It's still hypothetical at this stage, but the potential feels real.
The power of being seen and heard
This connects back to something powerful we've already witnessed in sessions where we've worked with AI for real-time synthesis: the impact of rapid, accurate feedback. When participants see the essence of their conversation captured almost instantly, often using their own words, there's a palpable sense of recognition and validation. "Yes, that's what we said. That's what matters." This immediate feedback loop seems to significantly accelerate understanding and build ownership over the output.
Extrapolating this, I wonder about the impact on energy and focus. If leveraging AI effectively in these complex collaborative processes makes alignment and documentation more seamless, perhaps it frees up valuable human energy. This could allow people in demanding sectors like healthcare to direct more of their focus and presence towards the aspects of their work where they feel they can add the most value or find greater engagement.
Hope and curiosity in the face of the new
Reflecting on the conversation, I felt a sense of excitement. What felt particularly hopeful was the manager's clear openness to exploration. His willingness to genuinely experiment with AI – to play, tinker, learn, and see what emerges – and his curiosity about co-creating something new without a predetermined outcome creates a vital space.
It’s a space where we can move beyond partnering with technology just for predictable efficiencies and start exploring how it might help us collaborate more deeply, navigate complexity more effectively, and perhaps even foster a greater sense of shared ownership in the changes we seek to make.
It’s early days for this specific AI application within the larger transformation, and for our collective understanding of AI’s role. But conversations like this fuel my curiosity and optimism about the path ahead.