The Art of Asking: How Inquiry Skills Bridge Qualitative Research and AI Innovation
During a recent conversation with a graduate student weighing her next career steps, I found myself explaining how different expertise can be translated across fields. As we discussed various skills and how to frame them during job searches, I shared my own realization that I had spent years developing a skill I hadn't recognized: unstructured data analysis. This ability to extract meaning from complex, non-standardized information has suddenly become a valuable capability in today's AI-driven landscape.
Looking back, I realize that my strongest skill wasn't just finding patterns in unstructured data—it was knowing what questions to ask in the first place. Whether in qualitative research or working with AI today, framing the right inquiry creates the foundation for meaningful discovery. The ability to craft precise, nuanced questions that reveal hidden insights is a skill that transcends methodologies and technologies.
Recognizing the Power of Strategic Questions
For years, I collected and synthesized unstructured data from qualitative research, thinking of this skill simply as "thematic identification." In the early part of my career, we lacked today's technology for developing code frames using keywords. The process was manual and methodical, and at times tedious, but my background blending engineering and English literature equipped me perfectly for this work. This foundation in analytical pattern recognition has served me in multiple industries.
It wasn't until I attended a University of Michigan Penny Stamps presentation by Giorgia Lupi, on her Dear Data book tour, that I had my aha moment. Lupi displayed images of her preliminary research for "Data Items: A Fashion Landscape at The Museum of Modern Art”—showing hand-coded “transcripts” with meticulous annotations: color-coded highlights, handwritten keywords in margins, connecting arrows mapping relationships between concepts, and carefully bracketed themes.
Photo from Giorgia Lupi’s website.
This resonated deeply with my own experience. For years, I had filled printer paper margins similarly—using different colored pens to mark recurring themes, circling key phrases, drawing connection lines between related concepts, and flagging findings with Post-It notes. I recognized that these techniques for transforming chaotic qualitative data into structured insights—represented a sophisticated form of data analysis that I had been doing all along without fully appreciating its significance.
Why Unstructured Data Matters More Than Ever in the AI Age
What's fascinating is how history has come full circle. While the business world spent decades focusing on structured data analysis—information neatly organized in databases and spreadsheets—we're now experiencing a renaissance of unstructured data's importance.
Generative AI, the technology revolutionizing countless industries today, operates primarily on unstructured data—text, images, audio, and video that don't fit into traditional rows and columns. The very skills that felt somewhat undervalued in the era of big data and quantitative analytics are now at the forefront of innovation. The ability to identify patterns, extract themes, and create meaning from unstructured information is precisely what powers today's AI systems and helps humans make sense of AI outputs.
What's more, unstructured data analysis drives innovation in ways structured data often cannot.
Where structured data excels at answering known questions, unstructured data exploration reveals questions we didn't know to ask.
It uncovers unexpected connections between seemingly unrelated concepts.
It identifies emerging trends before they're quantifiable.
It helps organizations think differently about their challenges and opportunities..
This divergent thinking—seeing patterns where others see chaos—is the catalyst for breakthrough innovations.
My experience manually coding qualitative data suddenly feels less like a historical artifact and more like foundational training for the current technological revolution—a revolution driven by those who can navigate the vast ocean of unstructured information and extract meaningful insights that traditional analytical approaches might miss.
From Historical Archives to Innovation Frameworks
The value of these approaches extends beyond technology. Later in my career, I had the opportunity to meet with the curriculum team at The Henry Ford in Dearborn, Michigan. What they accomplished at the museum still amazes me: they had taken the same analytical techniques I'd been using and applied them to systematically categorize their vast collection of historical artifacts, personal accounts, and archival records spanning 300 years of innovation history.
What's remarkable is how this meticulous unstructured data analysis—the careful coding of themes across thousands of primary sources from the Archive of American Innovation®—ultimately transformed into something entirely different: a comprehensive educational framework for developing entrepreneurial thinking. Their Model i curriculum, which they describe as "a universal language for teaching and learning about innovation," emerged directly from this structured analysis of unstructured historical materials. They had essentially distilled the patterns of innovative thinking across centuries into teachable competencies that could foster the next generation of entrepreneurs and innovators.
This transformation—from rigorous analysis of historical artifacts to actionable educational methodology—demonstrated to me the profound potential of unstructured data synthesis to generate entirely new forms of value. I was struck by how the methodologies for structuring unstructured information remained remarkably similar across such diverse applications—suggesting their fundamental value transcends any single domain.
From Research Questions to AI Prompts: Why Inquiry Skills Matter More Than Ever
The same underlying principles that enabled The Henry Ford to transform historical artifacts into an innovation curriculum now play a critical role in how we interact with artificial intelligence. Just as the museum staff manually extracted patterns from diverse historical sources to create structured frameworks, today's AI resources require human guidance to transform vast amounts of unstructured information into meaningful insights. My experience with unstructured data analysis affords me a happy advantage when working with AI technologies. The combination of research platforms like Dovetail and AI assistants like Claude has transformed my qualitative synthesis process, making it considerably more efficient. While the time savings are valuable, the true benefit lies in how this combination of technological capability and human insight generates more meaningful, business-relevant conclusions than either could produce independently.
The parallels between writing effective research questions and crafting productive AI prompts are striking—both require precision, context-setting, and an understanding of how to guide exploration without constraining discovery.
For example, when synthesizing patient interview transcripts for a healthcare client, I could use AI to quickly identify recurring themes, but then apply cross-domain expertise to recognize patterns the AI misses—such as subtle connections between patient experience and adherence behaviors. AI won't replace the need for human expertise; rather, this expertise determines where and how to deploy AI tools most effectively. Even the most sophisticated AI platforms need guidance because they lack the human ability to identify and frame conceptual problems, understand nuanced contexts, and apply practical experience from multiple domains.
Throughout my career, I've watched technology evolve from early programming languages to relational databases to cloud computing and now to AI. Through all these changes, the fundamental skill of making meaning from unstructured information has remained not just valuable but essential as we navigate increasingly complex data environments.
Your Turn: What Superpowers Are You Hiding?
Most of my work involves developing bespoke analytical frameworks for new discovery work or novel thought leadership avenues. The synergy between unstructured data synthesis and cross-domain expertise has proven particularly powerful for this sort of innovation. When I can draw parallels between real estate buyer experiences and retail customer journeys, or apply place-making techniques to community development challenges, I'm not just transferring knowledge—I'm creating entirely new insights that couldn't emerge from any single discipline.
To uncover your own hidden expertise, consider these questions:
How do you naturally organize complex information? Do you create visual maps, develop categories, look for patterns across seemingly unrelated areas? These approaches indicate sophisticated unstructured data skills that can provide the foundation for innovative thinking.
When have you successfully transferred methodologies between different fields? These cross-pollination moments often reveal unique analytical frameworks that can generate breakthrough insights.
How does your background influence how you work with AI tools? Your approach to formulating prompts and contextualizing results likely draws on domain knowledge you've accumulated throughout your career.
How do you formulate questions that lead to unexpected insights? Your approach to inquiry—whether in research, problem-solving, or now in AI prompting—might be your most transferable skill.
What patterns do you recognize that others miss? Your unique perspective might help you see connections or themes that could provide valuable insights in the age of AI.
I'd love to hear about your own discoveries. Connect with me to continue the conversation. As we navigate this new frontier where unstructured data drives innovation, recognizing these hidden capabilities might reveal your own unexpected edge.