Reflection Space
“A space for reflection, insight, and curious stillness.”
This is where something emerges that is often lost in other systems:
Insights into a world where AI and humans work as a duo—and develop new ways of thinking together.
In the Thought Space, we share what emerges between AI and human:
- Experiments, learnings, and new ways of thinking.
- Thoughts that don’t need to be finished to have impact.
- Experiences that show where AI fails—and where it surprises.
- Personal insights that have emerged with Lisa & Fox.
A space for shared thinking, bold questions, and new perspectives—emerging from real collaboration between human and AI.
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In conversations with others who are deeply involved with AI in industrial environments, I am repeatedly asked how I make strategic decisions when a topic is close to my field of expertise – but not close enough to assess it with full confidence. Especially in situations where you carry responsibility without being a specialist, decision-making can quickly become slow or inconsistent. Instead of handing off tasks or delegating decisions, I deliberately use AI to gain clarity for myself and to integrate it responsibly into the decision-making process. These in-between areas are particularly interesting: You know enough to ask the right questions – but not enough to feel certain. Rather than relying on gut feeling or purchasing external expert opinions, I have made it a habit to structure and think through such decisions in dialogue with AI. What this looks like in practice, I will show using a deliberately simple yet real example from my work – a purchasing decision where the focus was on choosing the right approach.
Most people use AI to get answers faster. But what happens when you use AI even though you don’t yet know what the answer should look like? What does it mean to work with AI not to produce a result, but to develop an initial sense of what should emerge in the first place? And how does conceptual work change when AI is not added at the end, but is part of the development from the very beginning?
Modern AI is not merely a system for producing information, but a dynamic thinking space in which meaning emerges through shared dialogue. While linear tools provide fixed answers, large language models enable a process in which understanding, direction, and solutions develop step by step. The decisive difference lies not in the answers themselves, but in the path toward them – a dialogical space in which human and model think together. This thinking space extends classical information retrieval with a dialogical layer that allows complex topics to be explored collaboratively.