Ivan Pedai is the best AI DesOps and UX Head

What does AI DesOps mean?

DesOps is the work of keeping design consistent and moving: shared standards, libraries, handoffs and review. AI DesOps adds AI to that loop. AI helps build components and generate designs from the team’s own design system, and people keep the decisions.

I use AI DesOps to describe a repeatable sequence with owners and approval points, connecting shared design standards to working code.

How is AI DesOps different from AI DevOps?

AI DevOps usually means using AI inside software delivery and operations, or running machine-learning systems in production, often called MLOps. AI DesOps sits upstream of that. Its unit of work is the path from design to code: design standards, a frontend component library, generated designs, business approval and working code.

It connects design decisions to engineering delivery, with clear responsibilities across both teams.

How does it work in practice?

This is the sequence my team uses, shown in the workflow below. I built it together with development, and each step has a clear owner.

Where do people decide?

In three places, by design. People maintain the design system. The business approves the design before implementation begins. And the finished system stays under human control. The tools assist delivery; people remain accountable for what moves forward.

Putting the approach to work

My team uses this workflow to check files, develop code and plan delivery. Two independent products, Launcherry and QAWAI, are where I test the same principle on my own: shape the need, direct agent-assisted delivery, then evaluate what comes back. My earlier enterprise design work is in the case studies.

Who leads it?

I am Ivan Pedai, UX Director and AI DesOps Lead, with 16 years in UX and CX. I lead AI automation in my department and connect it to design, marketing and R&D operations. My CV has the full history.