UX and Design Leadership · AI Transformation

I build and scale design organizations, and bring the AI fluency design leadership increasingly needs.

Nineteen years leading UX and design. I treat design as an operating discipline, not only a craft, building the teams, standards, and practices that make good work repeatable at enterprise scale.

19 yrs

Leading UX and design

2 → 12

Scaled a design discipline, hired every seat

Org-wide

Design system adopted across every product team

Exp → prod

AI into compliance-critical workflows

Selected Work

Global Retail Pharmacy · Principal UX/UI Design Manager

Scaled a design discipline from 2 to 12 and built the operating model behind it.

Situation: A global retail-pharmacy business was standing up an enterprise pharmacy-software transformation across multiple product domains. There was no visual-design discipline operating at scale, no shared system, and no consistent way for apps built by separate teams to look or behave like one product.
What I owned: I ran the entire build of the discipline from 2 designers to 12: all interviewing, hiring, and onboarding. More than headcount, I wrote the function's operating model, the team vision and standards, the hiring practices, the onboarding program, the design-review process, and a senior-designer model that placed art-direction leads inside each domain to cut bottlenecks. I set the shared visual standards, branding, accessibility bar, and naming conventions, and I led the visual-design side of the enterprise design system in partnership with UX and development.
What changed: Separate domain apps became one coherent system adopted across every product team, governed by a design system with common vocabulary and a repeatable review process. The team ran as a function, not a queue: designers were mentored up, domains had their own leads, and the standards outlasted any single person. This is my proof I can drive adoption org-wide and build a team that sustains it.

Design system foundations: color tokens, type scale, spacing, grid, and core components, governed across every product team. — The foundations of the design system: shared tokens, components, and the governance model every product team built against. *(Illustrative — representative of the work, not actual client deliverables, which are under confidentiality.)*

Enterprise Safety Science · UX Manager

Took AI from experiment to production in document-heavy, compliance-critical workflows.

Situation: Product teams held experimental AI capabilities but had no path from feasibility to trustworthy, adopted tools in work where experts certify products and cannot simply trust a model.
What I owned: Partnered with an experimental AI team and three product groups to move AI from feasibility through RICE-prioritized roadmap, UX design, and rollout. Set the responsible-AI interaction standards: human-in-the-loop confirmation, confidence scoring, source-file traceability, and AI output labeling.
What changed: AI capabilities moving into production across three product teams, with standards that let expert users verify every result against the original references regardless of confidence score. Responsible AI built as shipped controls, not a policy document.

One current-state experience map synthesized from prior studies, giving every team a shared, org-wide view and a common backlog. *(Illustrative — representative of the work, not actual client deliverables, which are under confidentiality.)*

Compliance-critical detail available on request

UX Research Function · UX Manager

Built the evaluation-driven AI practice an eight-person research function adopted as its standard.

Situation: A UX research function using AI ad hoc, with no shared standard, no quality bar, and no repeatable method behind its deliverables. Every person was starting from a blank prompt, and hard-won learnings evaporated the moment a chat window closed.
What I owned: Designed the practice: reusable skill files that encode complex instructions once and run many times, treated as living artifacts and updated from captured learnings so the agents get sharper over time instead of resetting. I built a gold-standard evaluation loop (an SME-vetted gold standard, the model graded against it on a 100-point scale, prompts iterated until they reliably score 90 or higher on the first pass), engineered the practice to carry learnings across the context-window cliff so knowledge compounds rather than restarts, and held the team to disciplined token economy: fewer redirects and revisions per task, the right tool matched to the job. I also had the agents learn beyond our own inputs, pulling from industry literature and live sources for comparative analysis of UX patterns.
What changed: Ad-hoc tool use became the eight-person function's documented, improving standard: a defensible first-pass quality bar, less downstream manual editing, and a body of practice that keeps compounding instead of living in one person's head. A system built to be run many times, and to get better each time.

The evaluation loop: model output scored against an SME-vetted gold standard, iterated until it reliably passes 90 on the first attempt. *(Illustrative — representative of the work, not actual client deliverables, which are under confidentiality.)*

Enterprise Safety Science · UX Lead, AI Initiatives

Led the UX work that turned experimental AI into designed, trustworthy tools ready for delivery.

Situation: An internal AI lab could prove models worked but had no path from a raw capability to a tool that experts certifying products would trust and adopt. Early builds validated the engine, not the workflow, and exposing them to users too soon was an adoption and research-validity risk.
What I owned: Named the single UX point of contact across the lab's AI use cases, coordinating research and design against team capacity. Across three initiatives (a document smart-lookup, an automated test-report generator, and an environmental-product-declaration tool) I set the responsible-AI interaction standards (human-in-the-loop confirmation, confidence scoring, source-file traceability, and output labeling) and wrote a reusable AI-Lab UX playbook and a research-to-product handoff template so the approach could repeat. I also defined how the work should be measured, the success metrics and baselines, so it could be judged on outcomes rather than opinion. My designers led the hands-on research and prototypes; I led the standards, the stakeholder alignment, and the operating-model guidance.
What changed: Each initiative moved from experiment through user research, tested prototypes, and RICE-prioritized backlog to a clean handoff to the delivery teams building them. The responsible-AI standards became the verification model behind the tools, so experts could check every result against its source. Later implementation surfaced exactly the data and workflow issues the research had flagged, which is why the caution was built in from the start.

The responsible-AI interaction standard: confidence scoring, source traceability, output labeling, and human verification against the original. *(Illustrative — representative of the work, not actual client deliverables, which are under confidentiality.)*

Rollout in progress; compliance-critical detail available on request

Approach

AI augments experts, it does not replace them

In compliance-critical work the model informs the expert and never overrides them. Every output stays verifiable against its source. That is a conclusion earned after proof, not an opening slogan.

Adoption is a leadership problem, not a tooling one

A standard only matters if people keep using it. The real work is training, change management, and a practice that outlives the launch. I have driven this in a design system and in AI, in two different mediums.

Design is an operating discipline

Hiring, mentoring, design-maturity standards, and repeatable process are what turn a group of designers into a function the rest of the organization can rely on.

In their words

Leaders and peers I have partnered with

He exemplifies what you want in a UX leader. He delivers thoughtful, effective experiences and elevates the team around him. His ability to balance strategic thinking with hands-on execution makes him ideal for a senior leadership role in UX.

Michael HenrySenior Director, Transformation and Innovation

I take messy, multi-layered ideas and watched him turn them into clean visuals that made leadership decisions not just easier, but better. Few people have shaped how I think about design and user experience more than he has.

Andrew MargolisProduct Manager

As a technical leader on the software team, what I valued was the usability, technical feasibility, and quality of his design work together. A natural leader, and a strong asset to any organization looking for a UX leader.

Phil DanielsEnterprise Architect

He took competing views on a new internal tool and built a proposal that created excitement and confidence around it. His approach shifted the team from apprehension to genuine enthusiasm.

Erik LeiderConformity Assessment Program Principal

People I have led

He takes full ownership and fosters a team environment of open communication. He proactively takes initiative in leadership, project planning, personnel hiring, and critical design decisions.

James DamonteSenior UX Designer

He embodies what exceptional leadership looks like. He provides clear direction without micromanaging, creates space for us to voice concerns, and advocates for our work-life balance. Working with him has been instrumental in my growth.

Sarah LaiUX Designer

He understands each designer's unique strengths and communicates in ways that connect personally. He spots where I can improve and turns it into an opportunity to grow. His feedback is specific and actionable.

Alex MoltaAssociate UX Designer

He drives clarity and structure when the team faces ambiguous situations, and creates space for teammates to share ideas and improve how we work. He mentored me up as a designer.

Erik GamradtAssociate UX Designer

About

I have spent nineteen years building and scaling design organizations, driving enterprise-wide adoption of standardized systems, and turning senior-leadership strategy into daily operating practice.

Most recently I led a UX function through its AI transformation, building the prompt-engineering and evaluation methodology that became the team's standard way of working, and partnering with product teams to take AI from experiment into live, compliance-critical workflows. My value is judgment and leadership applied to AI, not technical implementation. Based in the Greater Chicago Area.

The visuals here are illustrative. My enterprise design work is covered by client confidentiality, and I am glad to walk through it in detail during interviews.