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Rebuilding How Design Works: Leading an AI-Native Practice

ROLE VP OF DESIGN, RESEARCH, CONTENT · REMOTE
YEARS 2024–PRESENT

I retrained an entire design org to prototype in working code, automated the research pipeline, and raised the quality bar while shipping faster, turning AI from a tool into a way of working.

Stori’s design team supported a multi-product financial platform serving millions of customers across LATAM, and the roadmap was growing faster than the team could. Rather than hire I chose to rebuild the practice itself so each IC operated with more leverage, without letting craft slide. The risk was real, AI makes it cheap to ship mediocre work quickly. The practice had to raise quality and speed together, or it would fail at both.

I led the transformation personally, ahead of any mandate, by showing not telling. I retooled my own workflow first, brought working examples back to the team, and shared early wins and learnings. Adoption was uneven, so I set the expectation of exploration and learning over output at first — with clear goals: eliminate low-value work, increase iteration speed, and raise ownership of shipped UX quality. The practice stuck the same way any craft standard sticks: training, shared playbooks, and the same quality gates every shipped flow goes through.

What changed
Designers who ship code. Designers who think propose automations.

Trained the entire design team on Cursor to produce front-end code, moving them closer to controlling final quality and increasing our iteration speed. Designers now validate flows as working prototypes before engineering handoff.

Cursor agent building a Tap2pay onboarding prototype
AI Powered Design Skills folder of custom design review agents
25%
Flows prototyped in code
Research at machine speed, insight at human depth.

Automated participant recruiting and research synthesis, and centralized customer insights in an AI notebook the whole company queries. Weekly research became sustainable for a lean team.

n8n automation workflow for onboarding a new user
NotebookLM notebook synthesizing customer research
Quality systems that keep pace.

Wrapped the new velocity in Core Flow Reviews, DoD, and UX audits, so speed had a quality gate, not a quality cost.

75%
Design-to-dev reduction
8
Squad adoption
Designing AI, not just with it.

Led qual/quant research into customer AI readiness and built the framework for proactive vs. reactive AI entry points grounded in trust signals.

AI sets the floor, not the ceiling. I delegate production, synthesis, and exploration breadth to AI; judgment, taste, and knowing what shouldn’t ship stay human. The designer’s job is shifting from making screens to defining quality and directing systems that make them — and teams that learn this now will outrun teams that wait.

I’ve built this way since before the current wave: at Foureyes (2016–19) I designed AI-driven sales recommendations around transparency, explainability, and user control, principles that now define how I lead teams building agentic products.

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