UX Designer
SAP AppHaus Team
Enterprise Client Stakeholders
Business & Technical Experts
SAP’s AI Innovation Toolkit
Figma
Mural
At SAP AppHaus, I facilitated AI-focused design thinking workshops with global enterprise clients. These sessions used SAP’s AI Innovation Toolkit to uncover high-impact AI opportunities, align cross-functional teams, and bring early concepts to life.
While client details are confidential, I’ll share how I structured these workshops, the methods I used, and the kind of impact they created.
Discover: Framed client goals using AI Opportunity Cards
Ideate: Mapped feasibility, desirability, and viability
Prototype: Translated ideas into storyboards + early flows
Handoff: Shared toolkits for future AI design efforts
Goal
Identify real business challenges and opportunities where AI can add value.
How it works
Use opportunity cards as prompts to spark brainstorming.
Generate a wide range of potential AI use cases.
Describe and detail the most relevant ideas.
Compare and prioritize use cases to decide which ones to pursue further.
Outcome
High-potential, business-aligned AI use case(s) to take into design and prototyping phases.
Goal
Understand the current (as-is) user objectives and challenges, then design a future (to-be) scenario that leverages AI to solve them.
How it works
The team maps the current “as-is” journey, identifies pain points, and collaboratively defines an improved “to-be” scenario that integrates AI capabilities.
Outcome
A clear vision of an AI-enhanced solution, with defined user journeys and scenarios ready for prototyping.
Goal
Understand the power of agentic AI, how it differs from traditional automation, and identify high-value agent use cases that can drive productivity.
How it works
Participants learn how agentic AI differs from other automation, discuss its potential, and brainstorm where autonomous agents can deliver the most impact.
Outcome
A documented high-value agentic use case, defined in detail and ready for further design exploration.
Goal
Understand the power of agentic AI, how it differs from traditional automation, and identify high-value agent use cases that can drive productivity.
How it works
Clarify goals → Define where AI could create the greatest impact.
Identify responsibilities → Decide which tasks should be owned by the agent vs. humans.
Outline the role → Describe the agent’s required skills and capabilities.
Design the collaboration → Plan how the agent and humans will work together.
Outcome
A design blueprint for an AI agent, including goals, responsibilities, and interaction model—ready for prototyping and validation.
Across all four workshops, I learned that innovation with AI isn’t about the technology alone—it’s about facilitating alignment between business goals, technical feasibility, and human needs.
The AI Innovation Toolkit provided structure, but the real impact came from helping teams ask the right questions, prioritize wisely, and visualize possibilities in ways they could act on immediately.