AI chatbot for Internal knowledge

Healthcare

Employees across Optum frequently struggle to find accurate, up-to-date answers to commonly asked internal questions related to tools, programs, and operational workflows. Existing knowledge repositories like Confluence or static documents are fragmented, hard to navigate, and often require SME intervention for even basic queries.

Client

United Healthcare

Services

Visual Design
UI & UX Design

Conversational Design

Industries

Healthcare

Insurance

Date

Jan 2025

Context

To streamline onboarding for new employees, reduce reliance on SMEs, and centralize organizational knowledge, Optum needed a way to leverage existing AI endpoints—such as Copilot—while ensuring that the UI and conversational experience were tailored specifically for Optum employees.

The problem

Employees across Optum frequently struggle to find accurate, up-to-date answers to commonly asked internal questions related to tools, programs, and operational workflows. Existing knowledge repositories like Confluence or static documents are fragmented, hard to navigate, and often require SME intervention for even basic queries.

My role

I was the sole product designer on this project, which meant juggling a bunch of roles—from managing stakeholders to working closely with 2+ frontend developers.

Who are the user's

Optum employees across departments and roles who rely on the chatbot to quickly find accurate answers to internal tools, workflows, and process-related queries.

Setting the Stage

before we proceed with exploring options or ideating solutions, we had to get answers to few questions

Why are employees currently struggling to find answers?

Why are existing tools (e.g., Confluence, Copilot) not working well?

Why is it important to solve this problem now?

Who are the most frequent askers of internal questions?

Who are the current information gatekeepers (SMEs, team leads, support teams)?

Who benefits most from instant answers (new joiners, Ops teams, etc.)?

When are delays in finding answers most costly?

What types of questions will the chatbot answer (FAQs, links, SOPs, systems)?

What content sources will it pull from (Confluence, SharePoint, internal wiki)?

What level of personalization is needed (role-based, team-based, geo-specific)?

What success metrics will define its value (time saved, SME load, satisfaction)?

Asking the Experts

Why do we need a chatbot instead of a central repository of documents

What are the most common type of questions employees struggle to get answers?

Which team or department would you think benefit the most from this?

What is the level of personalization you need like role based , team based etc?

How do you wish to roll out this to the team?

What kind of Voice or Tone should Chatbot have to build trust?

Are there any Key KPI’s you want to track?

Responsibilites, Needs & Paint Points

Thinking through the Structure

Final design

UI were created in Figma using United Healthcare built-in design system features to ensure consistency and scalability. We incorporated ready-to-use Lottie animations to enhance micro-interactions and leveraged accessibility plugins to align with WCAG compliance standards. As we began presenting the designs, we iterated continuously to reflect stakeholder feedback and ensure alignment with business priorities.

Tone of Voice

One of the most overlooked aspects of chatbot design is tone of voice. It’s easy to focus on functionality—getting the chatbot to return the right answer, connect to the right source, or solve a ticket. But what often goes unnoticed is how the answer is delivered. Through this project, I learned that tone isn’t just “how it sounds”—it’s how the experience feels to the user.

The Future

Looking ahead, we see this chatbot not just as a support tool—but as a core part of how knowledge is shared, trust is built, and productivity is empowered at scale.