ENTERPRISE AI DESIGN SYSTEMS TEAM
Designing SAP AI Trust Framework
ROLE
Senior UX Designer
EXPERTISE
AI-powered UX, Trust patterns
IMPACT
Delivered AI framework to be adopted by over multiple LOBs
Goal Design an AI Trust Framework for SAP’s Joule assistant and embedded LOB apps, so users
Know when AI is involved
See how AI impacts content
Understand why AI arrives at certain results
Timeline
February – August 2025 (8‑week sprint + prior exploratory sprints leveraged for research)
Background
Enterprise workflows lacked clear AI cues. This opacity eroded trust, slowed adoption, and risked errors in high‑stakes scenarios. Our guidelines establish SAP‑branded AI indicators, context‑aware summons, spotlighting, and explainability across products.
Details of the step-by-step approach taken during the project, including research, planning, and design guidelines.
Research & Scoping
Synthesized 11 internal/external AI trust studies
Defined user needs: discoverability, visibility, context, transparency, consistency, empowerment
Beta Explorations (March–April)
Sketched initial indicator systems and context‑aware handoff flows
Pattern Sprints (April- July)
Rapid prototyping of 7 trust patterns:
AI Notice (General / Localized / Global)
AI Spotlight (inline visual highlights)
AI Explainability (concise reasoning overlays)
Content taxonomy (curated, created, predicted)
User Testing & Stakeholder Reviews (July- August)
Validated frequency, placement, language through usability sessions
Co‑designed iconography and lifecycle conventions
Technical Consults
Aligned with platform constraints; optimized performance
Delivered interactive Vibe‑Code prototype for rapid feedback
Guideline Delivery
Published pattern library and adoption playbook for LOB teams