AI Assistant for Reports

To empower clients to meet their own analytics needs, the business set out to design an AI-driven report authoring experience that reduces manual support demands and improves overall client satisfaction. This initiative aimed to simplify complex reporting tasks through intuitive AI assistance, giving clients greater control and confidence with their reports.

ROLE

  • Product design lead: Design direction and strategy

  • Guidance for designers, including a content designer and a researcher

TEAM

  • 2 product designers

  • 1 content designer

  • 1 user researcher

  • 1 director of product management

  • 1 engineering

TIMELINE

1 quarter

Problem

Clients struggled to self-service access to their analytics around their employee experience, and over-reliance on client teams caused inefficiencies. High level pain points included:

  • Analytics were scattered across
multiple platforms

  • Non-tech savvy users struggled with manually creating reports and dashboards 

  • Clients’ ad-hoc requests resulted in delays, adding to client frustration

Solution

How can AI help clients with different technical skills get their analytics faster and easier, while decreasing the need for client team support?

🎯

Narrow down the scope

Focus on report capabilities: find, modify, create

🤖

GenAI chat experience

Intuitive conversational experience, regardless of level of data expertise

Prototype

Process

Discovery

I worked with the PM director and researcher to compile key questions about the clients’ use of analytics:

  • How do users currently use analytics

  • What drives users’ data needs?

  • Where are areas of friction?

Primary research methods consisted of:

  • Client surveys: Gather volume of responses to inform directional themes

  • Interviews with clients and client teams: Understand details of their processes, goals, and pain points

Snapshot of interview theming in Miro

Key insights

🧑‍💻

Two user types:

• Tactical: Use analytics for daily, weekly, or monthly tasks

• Strategic: Use analytics to guide long-term plans

📆

Multiple use cases for analytics

• Recurring status meetings

• Targeting for campaigns

• Ad-hoc requests from leadership

🔢

Heavy report usage

Most users were tactical, and they used reports more often than dashboards

🤓

Varied level of expertise

Some users can pull their own reports, while others require client team support

😖

Backlogged client team requests

As client requests piled up, client teams struggled with delivering requests on time

User flow

Based on the key insights, I partnered with the engineering lead to map out the user flow for the 3 main paths:

  • Find a report

  • Edit an existing report

  • Create a new report

Design iterations

Entry point

Choosing the AI assistant’s entry point was crucial, as it needed to be visually prominent, while setting user expectations on the specific reporting capabilities of the chat.

Chat window size

The chat window needed to scale to accommodate future functionality, including dashboards, report previews, and data visualizations. In collaboration with the engineering lead, we decided to design a scalable chat window upfront, rather than redesigning it in later phases.

Patterns for AI-generated output

Design iterations for indicating AI-generated output focused on clarity and transparency. The pattern required distinguishing AI-generated content from standard chat responses, helping users immediately recognize when content had been created or modified by AI.

Guided and conversational AI

As the AI assistant had functionality limited to reports, it was important to clearly indicate when users had finite paths to choose from, and where users could chat freely.

Impact

🎉

AI assistant set to roll out to 350+ clients across 3 domains (Leaves, Health, Wealth)

🤖

Set the foundation for AI-powered 
design patterns and chat voice and tone

📊

Increased client self-service and reduced ad-hoc client team requests

🤝

Positive feedback from alpha and beta clients, rebuilding client trust

Challenges & learnings

  • Focus on outcomes, not features: Although AI can support countless analytics processes, the team focused efforts on the priority use case of reports to deliver value where it mattered most

  • Structure vs. free-form conversation: While the AI assistant supported conversational interactions, it was important to set user expectations around finite choices. Users needed to understand when they were navigating a limited set of options versus engaging in open-ended conversation with the assistant

  • Limited client access for user feedback: The project faced challenges in recruiting client end users for feedback. I partnered with the Director of User Research and the Client Communications team to design and execute targeted recruitment efforts.