Designing AI Agents Experience for Omnichannel Response Automation
Neudesic an IBM Company
Research, UX UI
Why
The insurance and legal industries face significant challenges in dispute management, where traditional manual processes create bottlenecks, inaccuracies, and compliance risks.
How
Custom software solution where Agentic transforms the landscape by deploying autonomous AI agents, automating research, data verification, and response drafting. By integrating Semantic Kernel for contextual reasoning and retrieval-augmented generation (RAG), the system ensures accuracy while maintaining human oversight.
System Metrics
↓
(%)
Dispute processing time
↓
(%)
Error rate in responses
↓
(%)
Customer satisfaction
↓
(%)
Operational cost savings
My Role
As a Product Designer, I led end-to-end UX strategy - from research and workflow mapping to prototyping and testing. I designed the human-in-the-loop interface that enables dispute adjusters to efficiently review and modify AI-generated recommendations, balancing automation with human oversight.
My work focused on simplifying complex agentic workflows into intuitive dashboard, optimizing the feedback loop between users and AI.
With insights from the research, a new workflow redefined the division of interaction between AI and humans. Instead of full automation, I designed a collaborative process where AI agents act as a research assistant. Data and AI team identified critical handoff points where human oversight was non-negotiable, such as high-value disputes or ambiguous cases, ensuring AI enhanced rather than replaced expertise.
Envisioning The Workflow,
0 to 1
Early sketches and rapid prototyping (AI draft vs. human edits) explored different ways to visualize AI’s contributions. The winning approach was a dynamic workspace where users saw AI-generated drafts alongside the supporting evidence, with the ability to accept, modify, or request deeper analysis.
Looking forward, the architecture is designed for adaptability across multiple sectors including healthcare claims and financial disputes, with potential to expand into contract analysis and litigation support.
Final Design
Unlike simple chatbots, this agentic approach creates a truly collaborative workflow where AI handles data-intensive tasks and humans focus on client's experience. The platform features intuitive interfaces that guide users through the review process, provide complete audit trails, and incorporate continuous learning from user feedback.
Why
The insurance and legal industries face significant challenges in dispute management, where traditional manual processes create bottlenecks, inaccuracies, and compliance risks.
How
Software solution where Agentic transforms the landscape by deploying autonomous AI agents, automating research, data verification, and response drafting. By integrating Semantic Kernel for contextual reasoning and retrieval-augmented generation (RAG), the system ensures accuracy while maintaining human oversight.
System Metrics
↓
(%)
Dispute processing time
↓
(%)
Error rate in responses
↓
(%)
Customer satisfaction
↓
(%)
Operational cost savings
My Role
As a Product Designer, I led end-to-end UX strategy - from research and workflow mapping to prototyping and testing. I designed the human-in-the-loop interface that enables dispute adjusters to efficiently review and modify AI-generated recommendations, balancing automation with human oversight.
My work focused on simplifying complex agentic workflows into intuitive dashboard, optimizing the feedback loop between users and AI.
Process
With insights from the research, a new workflow redefined the division of interaction between AI and humans. Instead of full automation, I designed a collaborative process where AI agents act as a research assistant. Data and AI team identified critical handoff points where human oversight was non-negotiable, such as high-value disputes or ambiguous cases, ensuring AI enhanced rather than replaced expertise.
Envisioning The Workflow,
0 to 1
Early sketches and rapid prototyping (AI draft vs. human edits) explored different ways to visualize AI’s contributions. The winning approach was a dynamic workspace where users saw AI-generated drafts alongside the supporting evidence, with the ability to accept, modify, or request deeper analysis.
Key Features
AI Transparency
Every recommendation includes clickable references to policy clauses and prior cases, so adjusters understand the reasoning behind suggestions.
One-Click Adjustments
Instead of rewriting from scratch, user can tweak AI drafts with smart editing tools that maintain formatting and compliance.
Exception Handling
For edge cases, user can flag issues for further AI review or escalate to colleagues, keeping workflows flexible.
Looking forward, the architecture is designed for adaptability across multiple sectors including healthcare claims and financial disputes, with potential to expand into contract analysis and litigation support.
Final Design
Unlike simple chatbots, this agentic approach creates a truly collaborative workflow where AI handles data-intensive tasks and humans focus on client's experience. The platform features intuitive interfaces that guide users through the review process, provide complete audit trails, and incorporate continuous learning from user feedback.