Leading when the playbook is still being written.
In the AI era, I anchor design leadership in knowing what’s worth doing, doing it well, and creating the conditions to do it differently.
Thirteen years ago, I sat around a table at IBM answering simple math and logic questions to help train Watson, two years after it beat Ken Jennings at Jeopardy. The awe and the low-grade dread in that room are the same feelings I watch teams move through today.
AI is not new. What’s new is the scale, speed, and accessibility of what it can do. The core role of design and research is the same as it’s always been: create experiences people love. What’s changing is how we get there.
No one has the playbook for this yet. That’s the kind of ground I’ve always done my best work on: getting close to what’s emerging and building what the team needs before the shape of it is obvious.
AI expands what’s possible. When we can move this quickly through product development, human taste and discernment is my focus.
Clarity
Know what’s worth doing.
I lead teams by defining clear goals to rally around so they have the agency and direction to solve, prototype, and explore.
Rigor
Do it well.
I set the vision for the division of labor to stay accountable for the integrity and quality of what we ship, watching for inaccuracies, inconsistencies, bias, and shifts in the source material.
Invention
Dare to do it differently.
I create an environment for my teams to be curious about the whole problem and willing to work past where their role usually stops.
What it looks like in practice
For me, AI leadership is more than adopting a set of tools. It changes how the team works, how we develop people, what we expect from our roles, and where design can have an impact. None of what follows came from a manual. We’ve been figuring it out in the work.
We remove design as a bottleneck.
Not everything needs to route through a designer. We’ve been building systems that let product and engineering partners create flows on their own, with designers reviewing near the end. That frees designers for the problems where their judgment and creativity matter most.
At Visa, I came up with a tool that let sales change the logos, colors, and content on a card-benefit prototype themselves, and the team built it. Before, every variation meant briefing design and waiting on the changes. Now sales could tailor options and share them with customers in the moment instead of waiting days, which kept deals moving while design shifted its time to harder product problems.
We work in parallel and take more ownership.
AI lets people follow an idea further. Designers can prototype, run lightweight research, write code, and get closer to implementation. Researchers can build and visualize what they’re learning as the work develops. Fewer things need to be handed off before the team can keep moving.
For an internal hackathon, I challenged the team to throw out the usual playbook and work differently. Our researcher used AI to gather and synthesize individual feedback, then validated the themes herself. At the same time, our designer folded those learnings into a design system she was building with Claude. Research, design, and product learned and built together.
That kind of parallel work extends well beyond a hackathon. One designer and one researcher on my team have supported two to three large projects for different teams at once, with AI helping across research, synthesis, exploration, prototyping, and production. The result is greater ownership, fewer handoffs, and more capacity for the work that needs human judgment.
We make experimentation part of the job.
AI changes too quickly for learning to happen once a year in a training, so we’ve built it into how we work. In Monday standups, someone shares something new: a tool, a feature, a use case, or something that didn’t work. We run learning sessions, attend trainings, share what we’re finding, and sometimes challenge ourselves to build entirely with a new tool.
When something proves useful, we make it repeatable. The team builds AI fluency through the work itself, and keeps expanding what it can do.
We redefine what good looks like.
As the work changes, leadership has to change with it. I don’t measure AI fluency by how many tools someone knows. I look for curiosity, judgment, adaptability, and the ability to use new capabilities to solve problems in better ways.
That shapes how I develop people and build teams. I want designers and researchers who are strong in their discipline and curious beyond it. I encourage people to experiment outside the usual boundaries of their roles, then coach them on where that creates value and where deeper expertise still matters.
It also shapes what I recognize as strong performance. I care about whether someone made a better decision, expanded what the team could accomplish, created something people actually used, or found a smarter way to solve the problem. AI fluency becomes part of how we grow talent and judge impact, without confusing speed or volume with quality.
You bring a lot of warmth into conversations and critiques, showing trust, and being super approachable. These qualities tend to trickle down and in turn have directly influenced our team’s working culture.— Vineet Boyle · UX Designer, Skip-Level Report
I learn a lot from your leadership, the way you communicate, and the way you approach your work… You have a very positive impact on me.— Jisoo Shoon · Staff UX Designer, Direct Report
I don’t pretend to know exactly where this is going. It’s impossible to keep up with every new tool, and the pressure to “do more with less” is real. Leadership in this moment means being honest about that.
If AI gives people more capacity, I don’t want to fill every hour it frees up with more work. I want some of that capacity to go toward deeper thinking, stronger relationships, experimentation, learning, and ideas we never had time to pursue. That’s the opportunity I’m most interested in.
Thirteen years after that afternoon with Watson, I still feel some of the same awe and uncertainty. The technology will keep changing. Our job is to decide what deserves our attention, hold a high bar for what we make, and give people room to invent what comes next.
Clarity. Rigor. Invention.