How I lead design teams

What I believe

1. Fewer handoffs, more pairing. The traditional design-to-development pipeline duplicates work and loses decisions at every step. I bring design, business and engineering together early, and keep them together.
2. Design systems are infrastructure. Tokens and components that both Figma and code trust let a team move fast without drifting.
3. AI is a craft tool, so hold it to a craft standard. Test it on real work, measure accuracy, write down the rules, then teach it.
4. Listen before fixing. I start with what stakeholders actually experience, then decide what's a process fix, what's tooling and what's automation.
5. Teach everything. I taught design and front-end development at the college level, and I still run knowledge-transfer sessions so the whole team levels up together.

How design should work in an AI-native org

AI is changing when design, product and engineering work together, not just how fast. I've been advocating for bringing UX into AI-assisted delivery teams from the start, rather than treating AI as another engineering-only process.

The long-term shift I'm working toward is moving away from a design → dev pipeline and toward paired development grounded in a shared design system. That directly targets the problems most teams already feel: duplicated work, information lost at handoffs, and decisions made in silos.

The question isn't "can AI make it faster?" It's "can AI make it faster and keep it right?"

Where should a department invest in AI?

Teams usually start with a long list of disconnected "we should build an agent for that" ideas. I built a discovery method that starts from the work instead:

• Analyze real work patterns and run short, AI-assisted interviews with employees.
• Turn findings into standardized opportunity records that can be clustered, de-duplicated, scored and prioritized.
• Build in governance from day one: evidence, human review and participant validation.
• End with a small portfolio of high-value pilots, not AI for AI's sake.

A marketing leader adopted the approach for team-level pain-point analysis and asked me to help scale it across the department.

Bringing the whole team along

• AI enablement: tracked my team's completion of the company AI training program and scheduled dedicated sessions.
• Knowledge transfer: taught developers and designers how Figma components connect to code and how AI workflows can generate pages from shared libraries.
• Responsible rollout: tracked which AI tools and connectors were security-approved, and kept estimates clearly separate from official commitments.
• Adoption: coordinated my team's access to a new AI audience-persona testing platform.

Operations that give designers their time back

I build the automations myself: a weekly portfolio status report generated from Asana, automated stakeholder sign-off tasks, a Figma QA report that checks accessibility and component usage, and a Claude-powered assistant that answers project-status questions from Teams (prototype).

See how a stakeholder survey reshaped our design reviews →