Changing how design becomes delivery

Problem

AI was changing how products could be designed and built, without a clear model for where UX fit. In senior leadership meetings, the need for UX itself was being questioned. In leading a global team of 11 UX designers, it was critical to define how our expertise could contribute as execution changed, as well as prepare the team to put that direction into practice.

Our existing process also separated design from implementation. Prototypes crossed a handoff that required Engineering to recreate the experience in code. There was an opportunity to rethink both how we brought experiences to life and how Product, UX, and Engineering worked together.

Process

The transformation began with a deliberate investment in learning. I protected four hours each week for team workshops and experimentation alongside active project work, giving designers time to explore unfamiliar tools and apply what they learned to practical challenges.

We coauthored an executive whitepaper, developed under my direction, that evaluated AI across the UX lifecycle to help us understand where it could contribute. Practical projects gave the team opportunities to test new approaches, demonstrate their capabilities to executives, and build confidence through experience. Coaching and shared learning helped turn individual discoveries into capabilities the team could use together.

Making that progress useful beyond UX required translating our learning across disciplines. I worked with the Product organization to share what we were discovering and how it could inform their ways of working. I met with Engineering leadership to build support for a closer partnership, connecting our experimentation to opportunities for improving how teams designed and delivered together.

As those relationships developed, we partnered with Engineering to connect prompt-driven design to usable front-end code. Together, we created a plugin that both Product and UX could use, then ran pilots to test it. We refined the approach so generated experiences used real components and code Engineering could build upon. This required setting direction amid uncertainty, building agreement across disciplines, and giving people room to help shape the solution.

Solution

We established a more connected way of working in which UX contributed usable front-end code, with Engineering’s technical expertise informing the prompts and components behind it. The shared plugin supported Product and UX, while closer collaboration made the transition from prototype to development more useful to Engineering.

Design intent could now carry forward directly into implementation. UX and Engineering worked together through the transition, reducing the need to rebuild what had already been designed and giving designers greater ownership of the implemented experience. Sharing our learning with Product and aligning with Engineering leadership helped extend the approach beyond the design team.

The guiding principle remained consistent: AI executes under the direction of people whose expertise determines what we build and the quality it needs to meet.

Outcome

Beyond faster prototyping, the work created a richer Engineering partnership and reduced time spent recreating designs. Those practical results helped change senior leadership’s mindset: UX moved from having its relevance questioned to being seen as a vital partner in shaping how the organization worked with AI.

My team was ahead of the curve within our organization and across Oracle’s broader UX community. Team members grew more confident, took on increasingly complex work, and demonstrated their expertise to senior leaders. They went on to help lead AI-enabled practices across Oracle’s 1,500-person UX community, while our learning informed conversations and practices within Product and Development.

The impact extended beyond a faster process. We helped establish a new understanding of UX’s value through the expertise, collaboration, and direction the team brought to organizational change.