AI-Accelerated UX /
Product Strategy
AI amplifies strong product and design fundamentals, it doesn’t replace them.
Role: Player-coach, led strategy while designing flows, prototypes, validation scripts, and hands-on AI workflows used by product, design, and engineering.
Moving Fast Without Direction
Teams moved fast, but none of the work was grounded in real user insight, validation, or measurable outcomes:
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A Big 4 firm I supported was facing the same challenges I’ve seen across many organizations:​
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Product Owners jumped to solutions without understanding real user problems
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No research muscle or ResearchOps foundation
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Designers produced impressive prototypes, but none validated with users
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Agile meant “deliver faster,” not “learn faster”
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AI features were bolted on without trust, transparency, or clarity
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Analytics existed, but metrics weren’t defined or used
PRODUCT OWNER
We're delivering features, but I can't tell if any of them actually mattered.
PRODUCT DESIGN
I can generate prototypes all day… but I rarely know which are "right".
END USER
Stuff keeps changing, but none of it fixes the things that slow me down every day.
This wasn’t a technology problem, it was a product practice gap.
I stepped in as a strategist and hands-on designer to rebuild the foundation while modeling the work for the team.​
We Weren’t Doing the Hard Work First
Teams were jumping into delivery without grounding in user problems.
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Before fixing the AI methods and designs, we had to fix the fundamentals. I worked directly with product owners and designers, writing hypotheses, mapping flows, facilitating quick interviews, and building early wireframes to shift the team from output-driven to learning-driven.​​​
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Rebuilt ResearchOps from scratch, templates, scripts, tagging, and rapid-learning cycles.
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Ran hands-on discovery sessions with POs and designers to clarify real user problems.
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Redesigned key workflows in low-fi and mid-fi wireframes to reset the team’s direction.
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Modeled Lean UX practices inside ongoing sprints so the team could watch and adopt.
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AI as an Accelerator — Not a Shortcut
Once the fundamentals were in place, I used AI to accelerate the work, not replace it. AI helped generate concepts, surface insights, and speed up research and prototyping, but every output still required hands-on design, human judgment, and real user validation.
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AI-assisted early concepts: I created first-pass flows, screens, and interaction variants to explore multiple directions quickly.
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AI-powered ResearchOps: built scripts, personas, interview guides, and synthesis frameworks backed by real user behavior / studies.
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Rapid prototyping: combined AI mockups with manual refinements in Figma to produce testable flows in hours, not days.
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Design → DevOps workflow: generated structured specifications and component mappings for engineering using AI-assisted documentation.
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AI dashboards: built measurement models that tied UX outcomes directly to product KPIs.
So What Happened Next?
Once the foundation was set, AI became a force multiplier around a AI UX Framework. We learned faster, validated more reliably, and delivered designs grounded in evidence—not opinion which drove measurable outcomes.
40%
Faster learning cycles
(Discovery to Validation)
60%
Improved solution quality
(Validated before build)
55%
Higher product adoption
(Increased user trust in AI)
