Designing a Scalable AI Assistants Framework For GenAI
Role:
Service, UX Designer & Researcher
Tools:
Figma, Miro
Our agency needed a consistent, scalable way to design AI assistants.
Mission
Build a GenAI framework roadmap, templates, and paradigms that accelerates design, aligns stakeholders, and ensures robust, user-centered platforms.
Symptom
- Fragmented design approaches
- Clients unclear on AI assistant value
- PMOs struggling with scoping
Root Cause
No structured methodology for AI assistant design.
Impacts
- Slower delivery
- Missed features
- Low trust in scalability
Create a framework that guides designers step by step—from scoping and personas to features, paradigms, and principles while giving clients and PMOs clarity.
Provided a roadmap
Added clear roadmap
problem → persona → AI values → features.
Outcome
Repeatable, guided workflow.
Created Templates
Created feature templates:
core, optional, advanced.
Outcome
Covered gaps, future-ready design.
Defined Principles
Defined design principles & UI paradigms.
Outcome
Educated designers and clients for AI design
Designed the skeleton
Built high-fidelity skeleton types with their interactions
Outcome
Faster approval, smoother execution and avoid repartition
1
designed
Artifacts
Roadmap, personas, feature lists
2
Features
Library
Memory, prompts, handoff, explainability
3
Designed
Paradigms
Chat, inline, proactive
4
Defined
Principles
Transparency, trust, scalability, tone
Business
Faster AI projects, clearer scope
Customers
Clarity, stronger trust in solutions
Operations
Unified workflow, better PMO scoping
Standardizing the process freed designers to focus on creativity while reducing risk.
Next step: add metrics to measure assistant performance post-launch.