How it started

The motivation for this project was to help HR teams quickly and accurately analyze and understand employee feedback from eNPS surveys.

In fast-paced workplaces, understanding employee feedback is essential for maintaining engagement and satisfaction. To address this, we developed an AI-powered tool to analyze eNPS (Employee Net Promoter Score) survey comments.

The tool summarizes key topics, and identifies sentiment, allowing HR teams to take meaningful action.

The Challenge

HR departments often face challenges with the overwhelming volume of comments collected from employee surveys. For example, a company with 200 employees can receive thousands of comments, making it time-consuming to read through them all and delaying the identification of critical issues.

Our challenge was to simplify this process and deliver clear insights — removing the manual workload. This led us to ask: How might we simplify the analysis of survey comments data from eNPS surveys?

The process

1. Discover:
   Through user interviews and observations, we gained valuable insights into the challenges faced by HR teams, particularly the overwhelming task of analyzing large volumes of survey comments. These insights helped us understand the real pain points and prioritize the problem.

2. Define:
   We synthesized our findings to clearly define the problem: HR professionals needed a faster, more efficient way to analyze and interpret large sets of qualitative feedback while minimizing manual effort.

3. Ideate & Design:
   We brainstormed various solutions, exploring how AI could automate and smooth the analysis process. The focus was to create a tool that could quickly summarize key topics and analyze sentiments to surface insights. We also worked on designing a user-friendly interface to ensure the tool would be accessible and intuitive for HR teams.

4. Prototype & Testing:
   I designed initial prototypes of the AI-powered tool, prioritizing its ability to read and summarize comments. During this phase, I collaborated closely with developers to understand how Carol (our AI tool) works and what capabilities could be used or enhanced. This collaboration also marked the beginning of training Carol, teaching her how to read and analyze survey comments.

Collaboration is a keyword in my process, I involved the Product Manager and developers since the first wireframes — making sure everything we designed was technically viable but also supplied the users’ needs.

Image below description: Early wireframes shared with the team to openly discuss some options and if funcionalities were technically viable.

The solution:
Using AI to simplify and analyze survey comments

The AI reads through all the comments, identifies sentiments and recurring topics. This automation significantly reduces the time required for data analysis and helps surface trends that might otherwise go unnoticed.

Image below description:
Image 1: The tool analyzes survey comments, identifies sentiment, and summarizes key topics.
Image 2: It also highlights related topics, enabling HR to explore deeper insights.

Using IA to identify topics and sentiments

Carol, our AI tool, analyzes survey comments to uncover key topics and sentiments. It scans for recurring themes, such as benefits, workplace culture, or leadership, while detecting the emotional tone (positive, negative, or neutral). This allows HR teams to quickly understand employee feedback and act on it.

By combining topic detection with sentiment analysis, Carol provides a clear overview of concerns and satisfaction areas, helping HR teams make data-driven decisions.

Listing Topics and Breaking Down by Sentiments

Our AI tool organizes feedback into:

• Key topics: The most discussed areas, such as benefits or work-life balance.

• Sentiment per topic: Whether employees feel positively, negatively, or neutrally about each.

• Overall sentiment: A summary of the general mood across all comments.

This structured approach enables HR teams to quickly identify areas for improvement and take meaningful action.

Designing for Accessibility: Optimizing Charts for Colour Blindness

Ensuring that insights are clear and accessible to all users was a key consideration in our design process. The colours used in the sentiment graphs were carefully tested for different types of colour vision deficiencies using Stark, including:

• Deuteranopia: Red-green color blindness

• Protanomaly: Reduced sensitivity to red light

• Tritanomaly: Reduced sensitivity to blue light

• Protanopia: Complete red colour blindness

By testing with these conditions, we ensured that the data remained distinguishable and easy to interpret for everyone. This commitment to accessibility enhances the usability of our tool, allowing HR teams to make informed decisions without visual barriers.