Case Study
Helping Product, Design, and Engineering Shape Work Together
How I led a shift towards earlier, cross-functional work shaping, improving delivery predictability and increasing On-Time Delivery from ~25% to ~85% across a distributed design organisation.
Role
UX Operations Lead / Program Manager
Company
GE Vernova, GridOS UX
Focus
Product Operations · Work Shaping · Cross-functional Collaboration
Outcome
OTD ~25% → ~85%
Context
Delivery predictability was the visible problem
GridOS UX supported multiple product programmes across Product, Design, and Engineering. The work sat in a complex domain, where product decisions often depended on specialised user workflows, technical constraints, and SME input.
I found that the delivery problem usually started before delivery. Work often entered the team before scope, ownership, feasibility, or the user workflow were clear enough to support realistic commitments.
The Problem
Work was entering delivery before it was ready
Poorly shaped work
Work Requests often arrived too large, too vague, or too late to estimate confidently.
Sequential handoffs
Product, Design, Engineering, and SMEs joined at different stages, creating context loss and late questions.
Limited visibility
Capacity constraints, priorities, and spillover were not visible early enough to adjust plans.
Approach
Leading the shift towards earlier decisions
I reframed Work Requests as the outcome of a discussion rather than the starting point of work, and introduced a clearer shaping step before delivery.
I brought Product Managers, Designers, Engineers, and SMEs into the same conversation before work entered delivery. I facilitated these discussions to clarify the user problem, workflow complexity, technical constraints, reusable patterns, scope boundaries, ownership, and priorities.
This gave teams a repeatable way to identify risks earlier, split oversized requests into smaller pieces, and make more realistic delivery commitments.
The goal was not more process. The goal was better decisions before design and development began.
Before
- Product idea
- Work Request
- Design
- Questions
- Rework
After
- User workflow
- Joint discussion with Product, Design, Engineering, and SME
- Work Request
- Design
- Implementation
Operational Improvements
Making the operating constraints visible
Moving decisions upstream only worked when teams could see the constraints they were making decisions within. To make earlier decisions actionable, I also introduced more structured capacity reviews, improved spillover tracking, and created clearer visibility of workload constraints across programmes and releases.
Reducing Context Switching
I advocated for programme-based micro-teams, reducing the number of product areas supported by individual designers wherever possible. This improved ownership, strengthened domain expertise, and reduced the overhead of switching between unrelated programmes.
Making Capacity Visible
I established a more evidence-based view of capacity, giving Product and UX leads a clearer basis for negotiating priorities and delivery commitments.
Outcomes
Improving predictability through better work shaping
The operating model I introduced made delivery commitments easier to make, discuss, and adjust. As the new ways of working took hold, On-Time Delivery increased from approximately 25% to 85%.
- I helped establish earlier UX involvement before Product created Work Requests.
- I brought Engineering and SMEs into shaping conversations earlier.
- Teams adopted a more consistent basis for realistic commitments.
- I made capacity trade-offs visible across programmes and releases.
- Product, Design, and Engineering developed a more collaborative way of working.
Reflection
Most delivery problems start before delivery
My most useful contribution was not adding process for its own sake. It was creating the conditions for teams to make key decisions earlier, before delivery constraints made those decisions expensive.
By redesigning how work was shaped before delivery, I helped reduce ambiguity, improve visibility, and create better conditions for predictable outcomes.
I reframed delivery predictability as an upstream product operations problem, then put the operating system in place to address it.