First Gen-AI Support Assistant in AutoCAD
CASE STUDY 3
ROLES I PLAYED
Lead Interaction Designer, Conversation Designer
DELIVERABLES
Competitive Analysis, Brainstorming Workshops, Early Concepts, Wireframes, Prototypes, User stories
TEAM
AutoCAD Product Team (Product Management, Engineering Design), Customer Success Stakeholders, Technical Support Agents, Autodesk Assistant Team (Product Management, Engineering, Quality Assurance, Data Science, Analytics, Content Strategy, Program Management,Visual Design, Research, UX Designer)
DURATION
5 months
Context
Autodesk’s flagship product, AutoCAD, is a 2D and 3D computer-aided design (CAD) software that is trusted and used by millions across the world. When it comes to troubleshooting an issue in the product:
AutoCAD users attempt self-service by leaving the product context to access resources like Google, AutoCAD Support site, Product Help pages, and the Forum Community for troubleshooting issues with their product.
They submit hundreds of technical product support cases every month but have to wait to hear back
We know from industry research that customers expect fast answers so they can get back to work quickly.
Problem Statement
How might we help AutoCAD customers find answers to their product questions and contact support all in one convenient location so they can get back to work quickly.
Approach
The plan was to leverage the Universal Help module, an extensible, modular help platform, that uses entity-based search to present relevant articles for a user’s issue.
Launch Beta: Update the module to be more chat-like and present top links for their product troubleshooting questions and connect to live chat agents. The Beta version of the bot was added to product help pages that product could users could access from within product.
Learn Fast: Conduct user testing and monitor interaction patterns to tweak and improve the bot’s responses and user experience over time as users adopted it within AutoCAD and other related products.
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Update to Generative AI: The Beta version was successful in that it helped us learn and tweak the experience and accuracy of its search results, but the engagement was low as the bot was hard to find. So I partnered with the lead Researcher and Product Manager to craft a vision that convinced AutoCAD team to embed the chatbot with the product canvas thus making it available at their fingertips. In early 2024 we later rebranded the bot as the Autodesk Assistant and The last iteration of this evolution I worked on involved evolving it to new RAG technologies to develop the first Generative Q&A chatbot that speaks “AutoCAD”.
BRAND MISMATCH: Universal Help was visually aligned with the Autodesk web branding guidelines and did not fit the AutoCAD design system that millions of customers are familiar with.
Beta Version
Access via Product Documentation
There was concern that embedding it directly into the canvas would take up valuable real estate so we started with a floating icon in the product documentation pages where some users start their help journey.
Indirect Access
Concern that a floating icon in the product canvas would take up valuable real estate so access to the bot was only via product help pages.
Indirect Access
Concern that a floating icon in the product canvas would take up valuable real estate so access to the bot was only via product help pages.
Indirect Access
Concern that a floating icon in the product canvas would take up valuable real estate so access to the bot was only via product help pages.
Indirect Access
Concern that a floating icon in the product canvas would take up valuable real estate so access to the bot was only via product help pages.
Discovery
I facilitated workshops with stakeholders from sales and e-store organizations to align on top customer intents. I also helped label 200 sample inputs for the data science team to train the AI routing models.
Design
Actions
Identify top sales intents
Lead with topic selection
Pass context during agent handoff
Build UX Library of components
Collect user feedback
Fallback options
Human agent always an option
Guide support/education inquiries out of sales path
Fallback to phone when chat is unavailable
Scope Change
New LLM + RAG for self-service
Assumed no UX needed
Conversational FAQs added to flow
Designed guardrails to minimize risk
Designed appropriate responses for conversation repair (error handling)
Limited number of turns
Early Results
Next Steps
Deep-dive into conversation logs
Include generative answers
Conversational memory
Improve formatting of answers
Topics or open-input or both
Revisit turn counts
Replace phone number with call-scheduling