How I Lead Design

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My AI Native UX Workflow Approach

I’ve evolved my traditional design approach into an AI-enhanced process. The fundamentals that have served me throughout my career remain, but I now use AI to accelerate ideation, synthesis, exploration, and iteration. Tools such as Notion, Zoom, Claude, GPT, Perplexity, and Gemini help me work more efficiently, explore more possibilities earlier, and reduce the time from idea to validated product direction.

AI Speeds Up My Task What I Lead
Interview transcript synthesis Choosing what questions matter
Pattern clustering across sessions Reading emotional subtext
Sentiment trend analysis Deciding what’s signal vs. noise
Desk research at scale Defining the research frame

AI Speeds Up My Task What I Lead
Generating opportunity hypotheses Selecting the right problem
Drafting HMW statements Setting success criteria
Mapping competitive patterns Navigating org politics
Synthesizing stakeholder inputs Aligning product and business

AI Speeds Up My Task What I Lead
Rapid concept variations Selecting the right problem
Copy and microcopy drafts Setting success criteria
Component scaffolding Navigating org politics
Accessibility checks Aligning product and business

AI Speeds Up My Task What I Lead
Rapid concept variations Taste and curation
Copy and microcopy drafts System coherence decisions
Component scaffolding Emotional resonance judgment
Accessibility checks Which variant is actually right

AI Speeds Up My Task What I Lead
Anomaly detection in funnels Deciding what metrics matter
NPS and CSAT theme extraction Explaining the why behind data
Session replay summarisation Advocating for the user in reviews
Automated metric reporting Closing the loop to research

My Design Experience Operating (DXO) Model

Throughout my career, I’ve brought cross-functional teams together around a shared North Star, creating the structure, clarity, and decision-making needed to move work forward. This isn’t a design process—it’s an operating model. Design craft is an essential part of the engine, but it’s only one part of how teams align, make decisions, and deliver outcomes.

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JTBD I establish why the work matters before we start designing—what business outcome we’re trying to change, why it matters now, and what’s at risk if we get it wrong or deliver too late. If we can’t answer those questions clearly, we’re not ready to design the solution.
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I define how the work gets done before the team starts building—who owns which decisions, what capabilities we need, what we build versus buy, and how design, product, and engineering work together around the problem.
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Research > Concepts > Build > Deploy > Feedback > Iterate > Build: This is where the craft happens—understanding the problem, exploring possibilities, building solutions, and learning from what happens in the real world. It’s essential work, but it’s the engine inside the larger model, not the model itself.
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I translate the work into what leaders need to know: where we’re making progress, where risk is emerging, and where a decision, alignment, or support is needed to keep the work moving forward.

People & Organization

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The Structure
  • I Managed-UX Integration (MUXI) pattern
  • I create a Dual-Relevance Framework — deep daily collaboration with PMs, BU leads, and engineering, anchored to an executive sponsor
  • As a Player-Coach — I actively upskill juniors and raise design literacy/maturity
  • Teams I worked on have focused on dedicated Pod/balance team  setups — Lead Designer, Engineer, Design System Specialist, and Site Reliability

Process & Frameworks

02

The Workflows
  • As with traditional Continuous discovery, AI enhances discovery  — formal, repeatable user research and validation sprints using AI to accelerate task analysis
  • Spec-Driven Development — functional requirements defined first, mapped to engineering capabilities through the use of AI capabilities
  • I utilize Governance — cross-functional alignment workshops and executive briefings tied to UX metrics