In your view, what strengths do we bring to an innovation process?
We humans are good at reading between the lines. We don’t just perceive what is said, but also how something is said. We can be empathetic, put ourselves in others’ shoes, and recognize meanings that aren’t explicitly stated.
In interviews with potential users, needs, desires, or areas of tension often emerge that the interviewees themselves are not yet fully aware of and therefore do not directly articulate. These are often expressed indirectly: through nuances, contradictions, casual remarks, or emotional reactions. With the appropriate experience and methodological training, we can perceive these signals, interpret them, and use them to develop a deeper understanding of needs, motivations, and areas of tension.
At the beginning of an innovation process, the goal is to identify and categorize the future needs of users and other relevant stakeholders. This leads to insights that refine the actual problem statement for a new product, service, or business model. These insights later form the basis for developing relevant ideas, designing prototypes, and testing solutions. If underlying needs are overlooked, innovation often remains close to the status quo.
And what strengths does AI bring to the table?
We’re certainly still in the early stages here, and we’re far from having exhausted the possibilities. I particularly value AI when there’s a complex dataset from interviews with users and relevant stakeholders, and the goal is to systematically identify recurring themes, connections, areas of tension, and potential patterns. At the same time, the most valuable insights rarely emerge from patterns alone.
What matters most is how we interpret these patterns: What needs become apparent? What areas of tension emerge? What insights can be derived from them that might later serve as inspiration for new solutions?
In my approach, AI is particularly valuable when the team has already developed an initial interpretation of its own. In such cases, it can help reveal additional patterns, offer alternative interpretations, and examine the team's interpretation through an additional perspective. It’s important to note that AI does not provide objective truth, but rather a perspective shaped by prompts that must be critically reflected upon and contextualized.
In workshops and project support, how do you help teams stay attuned to those signals and nuances that must not be lost, even in the age of AI?
Marc Stussak: When we have results from interviews with users, we first work through the material ourselves as a team. We identify statements that seem relevant, surprising, or contradictory, and then interpret what lies behind them: needs, motivations, areas of tension, or unspoken expectations.
This step is crucial because innovation doesn’t arise from a single statement in an interview, but rather from translating observations into insights. Only when we understand which need or area of tension lies behind an observation can it become a relevant source of inspiration for new solutions. AI can then be used in a targeted manner as an additional level of reflection. It analyzes the same dataset based on carefully formulated prompts and can help
reveal additional patterns, explore alternative interpretations, or challenge known biases within the team.
We then compare the human analysis with the AI-driven perspective side by side. We examine which insights align, which aspects we may have overlooked, and where differing interpretations arise. It is precisely these differences that often lead to the most exciting discussions.
This does not create an either/or situation between human and machine perspectives, but rather a deliberate comparison of different viewpoints. The teams strengthen their ability to observe, interpret, and make judgments. At the same time, they use AI to reveal blind spots, reflect on their own assumptions, and build a more solid path from insights to potentially innovative solutions.
Thank you very much for the interview.
