design leadership in the age of AI
design leadership in the age of AI
Synthesizing Research at Scale
Synthesizing Research at Scale
As AI tools evolve, so does our opportunity to work smarter—not just faster. This piece will explore how you’ve integrated AI into your workflow to accelerate research synthesis and insight generation, while keeping the human touch at the center.
August 18, 2025
As design leaders, we're asked to wear many hats—visionary, strategist, coach, translator of ambiguity. But one of the most powerful (and often invisible) aspects of our work is how we uncover and shape the why behind what we build. That means doing the work of research—and more importantly, synthesis.
For years, synthesizing user research meant sticky notes, hours of combing through transcripts, and workshop marathons. And while I still believe in the power of post-its and face-to-face mapping, the rise of AI tools has offered us a new lens: one that doesn’t replace human insight, but accelerates how we reach it.
At International Motors, I’ve led the integration of AI-assisted research methods within the Commercial Sales Dealer Experience team, transforming how we move from data to decision. Instead of getting buried in notes or paralyzed by volume, we use AI to sort, cluster, and illuminate patterns early—giving us a strategic head start on what matters most. While my work began in one product area, the methods and outcomes have influenced how research designers across the company think about scaling insight.
But here’s the nuance: it’s not about speed alone. It’s about creating space for deeper thinking.
When AI helps you surface signal from noise in minutes instead of hours, you’re not shortcutting the process—you’re enriching it. You’re giving your team more room to test assumptions, explore edge cases, and co-create with stakeholders.
As design leaders, we're asked to wear many hats—visionary, strategist, coach, translator of ambiguity. But one of the most powerful (and often invisible) aspects of our work is how we uncover and shape the why behind what we build. That means doing the work of research—and more importantly, synthesis.
For years, synthesizing user research meant sticky notes, hours of combing through transcripts, and workshop marathons. And while I still believe in the power of post-its and face-to-face mapping, the rise of AI tools has offered us a new lens: one that doesn’t replace human insight, but accelerates how we reach it.
At International Motors, I’ve led the integration of AI-assisted research methods within the Commercial Sales Dealer Experience team, transforming how we move from data to decision. Instead of getting buried in notes or paralyzed by volume, we use AI to sort, cluster, and illuminate patterns early—giving us a strategic head start on what matters most. While my work began in one product area, the methods and outcomes have influenced how research designers across the company think about scaling insight.
But here’s the nuance: it’s not about speed alone. It’s about creating space for deeper thinking.
When AI helps you surface signal from noise in minutes instead of hours, you’re not shortcutting the process—you’re enriching it. You’re giving your team more room to test assumptions, explore edge cases, and co-create with stakeholders.
As design leaders, we're asked to wear many hats—visionary, strategist, coach, translator of ambiguity. But one of the most powerful (and often invisible) aspects of our work is how we uncover and shape the why behind what we build. That means doing the work of research—and more importantly, synthesis.
For years, synthesizing user research meant sticky notes, hours of combing through transcripts, and workshop marathons. And while I still believe in the power of post-its and face-to-face mapping, the rise of AI tools has offered us a new lens: one that doesn’t replace human insight, but accelerates how we reach it.
At International Motors, I’ve led the integration of AI-assisted research methods within the Commercial Sales Dealer Experience team, transforming how we move from data to decision. Instead of getting buried in notes or paralyzed by volume, we use AI to sort, cluster, and illuminate patterns early—giving us a strategic head start on what matters most. While my work began in one product area, the methods and outcomes have influenced how research designers across the company think about scaling insight.
But here’s the nuance: it’s not about speed alone. It’s about creating space for deeper thinking.
When AI helps you surface signal from noise in minutes instead of hours, you’re not shortcutting the process—you’re enriching it. You’re giving your team more room to test assumptions, explore edge cases, and co-create with stakeholders.
The Method
Transcription and tagging: Interview audio files are transcribed instantly using MS Teams’ built-in transcription. (Tools like Otter or Descript can also be used in similar workflows.) We then layer in tags by intent or theme.
Pattern clustering: With AI models trained on our research vocabulary, we group responses into insight clusters—often revealing themes we hadn’t anticipated.
Insight drafting: AI helps generate first-draft “insight cards,” which we then review, refine, and validate collaboratively.
Hypothesis framing: From these insights, we co-develop strategic hypotheses to test—ensuring research directly informs roadmap planning.
The Method
Transcription and tagging: Interview audio files are transcribed instantly using MS Teams’ built-in transcription. (Tools like Otter or Descript can also be used in similar workflows.) We then layer in tags by intent or theme.
Pattern clustering: With AI models trained on our research vocabulary, we group responses into insight clusters—often revealing themes we hadn’t anticipated.
Insight drafting: AI helps generate first-draft “insight cards,” which we then review, refine, and validate collaboratively.
Hypothesis framing: From these insights, we co-develop strategic hypotheses to test—ensuring research directly informs roadmap planning.
The Method
Transcription and tagging: Interview audio files are transcribed instantly using MS Teams’ built-in transcription. (Tools like Otter or Descript can also be used in similar workflows.) We then layer in tags by intent or theme.
Pattern clustering: With AI models trained on our research vocabulary, we group responses into insight clusters—often revealing themes we hadn’t anticipated.
Insight drafting: AI helps generate first-draft “insight cards,” which we then review, refine, and validate collaboratively.
Hypothesis framing: From these insights, we co-develop strategic hypotheses to test—ensuring research directly informs roadmap planning.
Here’s how I approach AI in synthesis
Here’s how I approach AI in synthesis
The result?
Faster synthesis.
Smarter iteration.
Deeper alignment.
But none of this works in a vacuum. AI isn’t a magic wand—it’s a tool we must wield with care. I always ask my teams to review, question, and humanize what comes out of the model. Because design, at its core, is about people—not just patterns.
Why It Matters
As design leaders, we’re not just responsible for producing great work—we’re responsible for creating the conditions in which great work can emerge. Integrating AI into our research flow is one of those conditions. It helps us scale insight without sacrificing integrity. It supports cross-functional trust. And it keeps us focused on what users actually need, not just what stakeholders assume.
As the tools evolve, so will we.
Why It Matters
As design leaders, we’re not just responsible for producing great work—we’re responsible for creating the conditions in which great work can emerge. Integrating AI into our research flow is one of those conditions. It helps us scale insight without sacrificing integrity. It supports cross-functional trust. And it keeps us focused on what users actually need, not just what stakeholders assume.
As the tools evolve, so will we.
Why It Matters
As design leaders, we’re not just responsible for producing great work—we’re responsible for creating the conditions in which great work can emerge. Integrating AI into our research flow is one of those conditions. It helps us scale insight without sacrificing integrity. It supports cross-functional trust. And it keeps us focused on what users actually need, not just what stakeholders assume.
As the tools evolve, so will we.
The heart of design leadership remains unchanged: listen closely, interpret wisely, and lead with clarity.
The heart of design leadership remains unchanged: listen closely, interpret wisely, and lead with clarity.
The heart of design leadership remains unchanged: listen closely, interpret wisely, and lead with clarity.
Building Teams that Build Each Other
My Approach to Mentorship
Building Teams that Build Each Other
My Approach to Mentorship
Building Teams that Build Each Other
My Approach to Mentorship