CULTURE AMP

AI Feedback Assistant

How might we help managers spend less time analyzing data and more time driving impactful change?

Project Details

Role:
Lead Product Designer
Timeline:
6 Months
Deliverables:
Low-Fidelity Designs, High-Fidelity Designs, Interactive Prototype, Information Architecture, JTBD Matrix, User Research Playback Artifacts
Tools:
Figma, Miro, Maze, Protopie, Google Vertex AI
Managers shouldn’t have to wrestle with data to drive impact. Too often, they get stuck analyzing and digging for insights instead of making decisions that matter. I focused on removing that friction—turning complex data into clear, actionable insights so managers can spend less time digging and more time leading.

My focus was on making data work for managers—not the other way around. Too often, they spend valuable time piecing together insights when they could be driving real change. To solve this, I started by talking to managers, watching how they work, and identifying the moments where data slows them down. I then organized and led a multi-team, multi-functional design sprint to solidify our POV and identify a design direction.

From there, I collaborateed with engineers, data scientists, and my product counterparts to create solutions that surfaced key insights faster, automated repetitive tasks, and made complex information easier to act on for a busy manager during a performance review.

Through prototyping and testing, I refined the experience to ensure it felt effortless and intuitive. Success meant managers spend less time sifting through numbers and more time making decisions that truly move the needle for their direct reports.

PROCESS

The process of designing the AI Feedback Assistant involved a deep dive into understanding the challenges managers face with data analysis and designing solutions to help midigate those pain points. Our solution was comprised of four key pillars.

1. Surface Key Themes

We heard from managers that identifying key themes an summarizing their direct reports performance in those areas was a huge part of their process for constructing reviews so we set out to eliminate that work and present the insights to them automatically.

2. Identify Growth Opportunities

Performance conversations are not only about highlighting successes and failures but also discussing growth opportunities. We used machine-learning to identify potential areas of growth for a direct report so managers can start those conversations with them.

3. Connect to Supporting Sources

Building trust was a core pillar of our AI work. By connecting the AI-generated insights to real and tangible evidence provided by a human we were able to connect the dots between the artificial and the actual.

4. Analyze and Highlight Issues

Once a manager was connected to the supporting source we used our LLM to highlight potential areas where the feedback provided differed from people-science points of view so the manager could be aware of biases such as gender, recency, similarity, etc.

"This will provide our managers with so much more insight and save them from having to dig for this information themselves."

Results

We tested our AI solution with managers, direct reports and HR leaders from among existing customers.

The testing phase revealed significant improvements in efficiency and satisfaction among managers. They reported spending less time on data analysis and more on strategic decision-making, which positively impacted their teams' performance.

Additionally, managers noted a marked increase in their ability to focus on leadership tasks, as the AI solution streamlined their workflow. This shift allowed them to allocate more time to mentoring their teams and fostering a culture of growth and innovation.

HR leaders were pleased with the reduction in cognitive load for their managers and were quick to begin creating enablement materials to help teach their managers how to properly utilize the AI assistant.