Epic Systems

Epic Systems

Zero -> AI design system used by 100K+ clinicians and 350+ orgs.

Enterprise AI

B2B

Securing Pilots

Disclaimer: Due to IP restrictions, design artifacts cannot be shared.

Epic, the largest EHR provider in the U.S., needed a way to make AI safe, consistent, and adoption-ready across 80+ apps.

I co-built Epic’s first AI design system from scratch, unifying scattered AI efforts into a trusted foundation now used by 80K+ clinicians and 330+ organizations.

The Results

The system became Epic’s foundation for all AI experiences:

  • Used by 100K+ clinicians across 350+ orgs

  • Launched for Epic’s largest annual conference (40K+ attendees)

  • Endorsed by Epic’s C-suite as a “beautiful, scalable solution”

  • Established validated standards still guiding future AI work

"Working with Josh was a fantastic experience. He was highly responsive to feedback and played a key role in shaping our ideas into a compelling narrative and an enhanced user experience around AI.

I especially appreciated his initiative in researching the best solutions and his creativity in exploring new directions."

Nick Marzotto

Head of AI Customer Success, Epic

The Process

Step 1

Define the foundation

Audited 30+ in-progress AI features to uncover adoption blockers: lack of trust, unclear ownership, and no shared UX standards.

Step 1

Define the foundation

Audited 30+ in-progress AI features to uncover adoption blockers: lack of trust, unclear ownership, and no shared UX standards.

Step 1

Define the foundation

Audited 30+ in-progress AI features to uncover adoption blockers: lack of trust, unclear ownership, and no shared UX standards.

Step 2

Establish visual identity

Created a consistent AI identity with a new icon system, visual language, and the “Bloom” symbol as the universal AI marker.

Step 2

Establish visual identity

Created a consistent AI identity with a new icon system, visual language, and the “Bloom” symbol as the universal AI marker.

Step 2

Establish visual identity

Created a consistent AI identity with a new icon system, visual language, and the “Bloom” symbol as the universal AI marker.

Step 3

Create reusable patterns

Defined four core workflows (summarization, automation, content transformation, drafted text) and built reusable components.

Step 3

Create reusable patterns

Defined four core workflows (summarization, automation, content transformation, drafted text) and built reusable components.

Step 3

Create reusable patterns

Defined four core workflows (summarization, automation, content transformation, drafted text) and built reusable components.

Step 4

Scale and validate

Rolled out across 80+ apps and tested continuously with clinicians to ensure safety, clarity, and adoption.

Step 4

Scale and validate

Rolled out across 80+ apps and tested continuously with clinicians to ensure safety, clarity, and adoption.

Step 4

Scale and validate

Rolled out across 80+ apps and tested continuously with clinicians to ensure safety, clarity, and adoption.

Solving Hard Problems at Scale

These were some of the UX problems solved, where design directly impacted trust, adoption, and safety.

The AI design system

Before: Scattered teams, inconsistent layouts, lost ideas. After: A unified framework and shared AI language used across 80+ apps.

The AI design system

Before: Scattered teams, inconsistent layouts, lost ideas. After: A unified framework and shared AI language used across 80+ apps.

The AI design system

Before: Scattered teams, inconsistent layouts, lost ideas. After: A unified framework and shared AI language used across 80+ apps.

Trust and explainability

Before: AI outputs lacked context or reliability. After: Designed patterns for confidence scores, transparency, and control, so clinicians could trust and act confidently.

Trust and explainability

Before: AI outputs lacked context or reliability. After: Designed patterns for confidence scores, transparency, and control, so clinicians could trust and act confidently.

Trust and explainability

Before: AI outputs lacked context or reliability. After: Designed patterns for confidence scores, transparency, and control, so clinicians could trust and act confidently.

Visual identity + brand

Before: AI had no recognizable brand or “face.” After: Co-designed a enterprise brand and style system for AI that made outputs instantly recognizable and interpretable.

Visual identity + brand

Before: AI had no recognizable brand or “face.” After: Co-designed a enterprise brand and style system for AI that made outputs instantly recognizable and interpretable.

Visual identity + brand

Before: AI had no recognizable brand or “face.” After: Co-designed a enterprise brand and style system for AI that made outputs instantly recognizable and interpretable.

Scalability

Before: Every AI feature was a one-off pilot. After: Shifted from “features” to “systems,” enabling faster, safer scale with trust built in.

Scalability

Before: Every AI feature was a one-off pilot. After: Shifted from “features” to “systems,” enabling faster, safer scale with trust built in.

Scalability

Before: Every AI feature was a one-off pilot. After: Shifted from “features” to “systems,” enabling faster, safer scale with trust built in.

Broader Scope of Work

Beyond design, I helped define Epic’s broader AI direction:

Partnered with leadership to shape AI strategy

Built pitch decks and roadmaps with partners (e.g. Microsoft, Mayo Clinic)

Advised 80+ internal teams as an “AI design consultant”

Delivered executive presentations to evangelize AI design

Built design systems, Figma component libraries, and internal guidelines

Defined future vision roadmaps for AI’s future in healthcare

This role blended strategy and execution, driving alignment across teams and leadership.

Outcomes and Impact

The unified AI design system reshaped how 80+ clinical apps delivered AI. Teams could now scale safely through shared components, consistent identity, and built-in safeguards, reducing duplication and accelerating development.

The system influenced Epic’s broader AI strategy, partner presentations, and long-term vision, proving that strong UX can drive both adoption and enterprise direction at scale.

Lessons for Founders

The same patterns apply to AI startups building their first or next product:

Think in systems, not features: Reusable patterns and safeguards enable scale from 1 to 100.

Build trust into the workflow: Explainability and user control are levers for strong adoption.

Position for impact: A strong design system builds credibility, partnerships, and strategic clarity.

This project showed how UX can turn fragmented AI pilots into systems users trust—setting the foundation for scalable, safe AI adoption.

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