Moving a UX team to AI-native delivery
Static UX/UI handoffs were slowing delivery and forcing engineers to pixel-match design intent. I'm leading my team's shift to an AI-native, front-end-integrated workflow — pairing Figma Make for iterative design with AWS Kiro for build, pushed through engineering's own CI/CD into a UX-specific sandbox for review. It's already cut design revision cycles by 30%+.
🔒 Some specifics are generalized to respect confidentiality. Happy to walk through the details in conversation.
The opportunity
My team designs for 8+ enterprise identity products, and like most teams, we delivered UX as static handoffs — wireframes and high-fidelity mockups that engineering then had to interpret and pixel-match. That handoff was a bottleneck: slow, dependent on developers to carry design fidelity, and expensive to iterate on for complex features. AI-native tooling opened a door — but only if someone built the foundation to walk through it safely.
The challenge
Adopting AI tools isn't the hard part; integrating them into how real teams ship is. To make an AI-native workflow stick across multiple product teams, I had to:
- Understand each team's existing development pipeline and CI/CD.
- Introduce new tooling without disrupting engineering's standard flow.
- Keep AI-generated UX consistent with our design system.
- Give engineering a trustworthy way to review AI-built front-end code.
None of that works without doing the groundwork first.
How I'm leading it
So I built the foundation:
- Secured access to AWS Kiro for myself and my team, and became a leading internal adopter.
- Convened engineering leaders to understand their pipelines — learning GitLab, AWS, and each team's CI/CD from the people who run them.
- Learned each team's workflow, then defined a strategy and adjusted it as I went.
- Set up UX sandbox servers integrated into each team's existing CI/CD — leveraging pipelines they already trust, but adding a new UX-specific sandbox endpoint where AI-built code can be reviewed by engineering.
- Introduced Figma Make for iterative, stakeholder-facing design on complex features.
- Authored design-system steering so the code we generate is scalable, on-brand, and consistent.
- Mirrored the dev process — clean branches, Jira tickets, merging our branch into the dev branch — so UX plugs into engineering's world rather than running parallel to it.
The result is a new loop that runs from design all the way to reviewable code:
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Design
Figma Make
Iterative design and stakeholder review on complex features, until the feature is defined.
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Build
AWS Kiro
Builds the defined feature against our design-system steering — scalable, on-brand UX as code.
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Pipeline
CI/CD → GitLab
Pushed through engineering's existing pipeline into a UX branch.
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Review
UX sandbox endpoint
Engineering reviews the front-end code and connects it to the backend.
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Ship
Merge to dev
Clean branches and Jira tickets, mirroring the dev process.
Because the design intent already arrives as code, engineering no longer has to pixel-match — and the Figma Make files feed Kiro better inputs, which means better code reaching developers.
Early results
It's working, and it's still maturing. So far:
- Design revision cycles are down 30%+.
- Three designers have adopted the workflow, with broader rollout in progress.
- Developer time is reduced — engineering receives cleaner, design-system-aligned front-end code instead of static mockups to rebuild.
We're still refining the handoff to make it seamless — but the direction is set, and the foundation is in place.
What it shows
Innovation only happens once the foundation exists. The AI tools were available to anyone — the leverage came from understanding the pipelines, earning engineering's trust, and building the steering and sandboxes that let a team adopt them safely.