Create your survey

Create your survey

Create your survey

How to use AI to analyze responses from employee survey about diversity and inclusion

Adam Sabla - Image Avatar

Adam Sabla

·

Aug 20, 2025

Create your survey

This article will give you tips on how to analyze responses from an employee survey about diversity and inclusion. Whether you’re tackling dozens or thousands of replies, I’ll show you how to turn survey response analysis into real insights using AI and smart prompts.

Choosing the right tools for analyzing survey responses

Your approach (and tools) for survey analysis depends on the form of your employee diversity and inclusion data. Here’s what I mean:

  • Quantitative data: If you’re counting how many employees picked a particular option (like “I feel included: Yes or No”), then you can handle that with a spreadsheet. Tools like Excel or Google Sheets are perfect for tallying up numbers, building quick charts, and tracking trends over time.

  • Qualitative data: Most employee surveys about diversity and inclusion include open-text questions (“Tell us about a time you felt excluded,” or follow-ups like “Why did you answer that way?”). These qualitative responses are where the gold lies, but manually reading hundreds of answers isn’t realistic. This is where AI tools come into play, making it possible to analyze nuanced feedback at scale.

There are two approaches for tooling when dealing with qualitative responses:

ChatGPT or similar GPT tool for AI analysis

With exported survey responses, you can paste qualitative data into ChatGPT or any other GPT-powered chat interface.


It’s flexible—you can ask follow-ups or request summaries in natural language, just like chatting with a colleague.

But honestly, it gets messy fast. Exporting and prepping data for ChatGPT takes time. If you have a large file, the tool may hit its context limit. You’ll have to do a lot of copy-pasting and track what you’ve analyzed manually.


Your privacy and data confidentiality are also considerations—especially important with sensitive diversity and inclusion topics.


All-in-one tool like Specific

Tools designed for AI survey response analysis (like Specific) solve these pain points by letting you both create the survey and analyze responses in one place.

When collecting responses, Specific uses conversational AI to ask follow-up questions automatically. This means you get deeper, higher-quality answers—which is especially important in diversity and inclusion surveys where surface-level answers don’t cut it. For more on follow-ups, check out how AI follow-up questions work.

AI-powered analysis in Specific instantly summarizes responses, finds core themes, and highlights actionable insights—no spreadsheet wrangling required. You can interact with the data conversationally, just like in ChatGPT, but with the added ability to filter, segment, and collaborate on results with your team.

You’re not locked in, either. If you prefer to build your own survey from scratch, you have options—check out this AI survey generator or start directly with a preset for employee diversity and inclusion surveys.

Useful prompts that you can use for analyzing employee diversity and inclusion survey responses

Let’s talk about AI prompts. A good prompt helps you (and your AI tool) uncover the big picture, as well as zoom in on what matters most in employee survey analysis.


Prompt for core ideas: This is a versatile prompt for extracting the main themes from a large set of open-ended employee responses. For diversity and inclusion surveys, it’s great for seeing what comes up most.

Your task is to extract core ideas in bold (4-5 words per core idea) + up to 2 sentence long explainer.

Output requirements:

- Avoid unnecessary details

- Specify how many people mentioned specific core idea (use numbers, not words), most mentioned on top

- no suggestions

- no indications

Example output:

1. **Core idea text:** explainer text

2. **Core idea text:** explainer text

3. **Core idea text:** explainer text

You’ll get a prioritized list of core topics—excellent for reporting back to management or identifying what to work on next.


Better prompts with more context: Give the AI specifics about your survey—your goals, audience, and concerns—and you’ll get sharper insights. For example:

You are analyzing a confidential 2024 employee survey on diversity and inclusion, with 150 responses. The survey aims to discover the barriers ethnic minority groups face at our company and provide actionable steps for leadership. Stick to issues related to hiring, promotions, and internal communication.

The extra detail will guide AI to focus on what matters to you.


Dive deeper: Once you find a theme, ask: "Tell me more about XYZ (core idea)." The AI will surface direct quotes or anecdotes behind the theme—essential context for diversity and inclusion action planning.

Prompt for specific topic: Want to check if a certain subject (like “work-from-home”) came up in the employee responses?

Did anyone talk about work-from-home? Include quotes.

With so much discussion recently about return-to-office policies—especially post-pandemic—it’s key to see who’s mention this and what they say. According to a 2024 article, return-to-office moves are disproportionately affecting women, minorities, and those with disabilities, despite remote work’s benefits for equity and productivity [1].


Prompt for pain points and challenges: If your leadership wants to know what employees struggle with, try:

Analyze the survey responses and list the most common pain points, frustrations, or challenges mentioned. Summarize each, and note any patterns or frequency of occurrence.

Prompt for personas: Understanding employee “personas” or groups with similar experiences can drive tailored D&I initiatives:

Based on the survey responses, identify and describe a list of distinct personas—similar to how “personas” are used in product management. For each persona, summarize their key characteristics, motivations, goals, and any relevant quotes or patterns observed.

Prompt for Motivations & Drivers:

From the survey conversations, extract the primary motivations, desires, or reasons participants express for their behaviors or choices. Group similar motivations together and provide supporting evidence from the data.

Prompt for Sentiment Analysis:

Assess the overall sentiment expressed in the survey responses (e.g., positive, negative, neutral). Highlight key phrases or feedback that contribute to each sentiment category.

Prompt for Suggestions & Ideas:

Identify and list all suggestions, ideas, or requests provided by survey participants. Organize them by topic or frequency, and include direct quotes where relevant.

These prompts help structure your AI analysis, making sense of even the most open-ended employee feedback. For more on crafting survey questions and prompts, see our guide on the best questions for employee diversity and inclusion surveys.

How Specific breaks down qualitative data by question type

Specific automatically adapts its AI analysis based on the type of survey question:


  • Open-ended questions (with or without follow-ups): The AI summarizes all responses and their related follow-up conversations, surfacing core ideas and actionable insights.

  • Multiple choice with follow-ups: For each chosen option (like “Management is diverse”—Yes/No), Specific generates a separate summary of follow-up responses for that option, making it easy to spot patterns by group.

  • NPS-style questions: For employee net promoter questions, the AI summarizes feedback by segment—detractors, passives, promoters—giving you a focused view on what each group is saying.

You can do the same using ChatGPT or similar tools—it just takes more copy-pasting and configuration. If you want to create an NPS-specific survey, check out this employee NPS template for diversity and inclusion surveys.

Working with context limits in AI analysis tools

Every AI chat tool, including ChatGPT and Specific, has a context limit—which means it can only take in so much data at a time. Large employee diversity and inclusion surveys can quickly hit these limits, especially if you have hundreds of respondents and follow-ups.


  • Filtering: Slice your data by selecting only the conversations that include replies to certain questions or specific choices. This way, AI only analyzes the relevant subset of your data, letting you focus on, say, ethnic minority employees or those who mentioned “promotion.” This filtering maximizes the insight you get within context limits.

  • Cropping: Only send responses to a particular question into the AI for analysis. For example, focus only on the “Describe a time you felt included” question, leaving out the rest.

These strategies keep things running smoothly and help you surface targeted insights without overwhelming the AI. Specific has these controls built in, but they’re easy to replicate in any structured workflow.


Collaborative features for analyzing employee survey responses

Cross-team collaboration often turns into chaos when analyzing employee feedback—especially on sensitive topics like diversity and inclusion. Merging hundreds of open-ended responses, tracking who asked what, and making sure each department’s concerns get addressed is a core challenge for diversity initiatives.

In Specific, you can analyze survey data just by chatting with AI. Each analysis can happen in its own chat window, allowing you (and your teammates) to run side-by-side deep dives—by department, by leadership, or even by initiative.

Multiple chats, each with their own filters, make it easy to compare or hand off work. Every chat shows who started it, and you can see exactly who said what. This transparency clears up confusion and lets you attribute findings and comments directly—a big plus for accountability in employee diversity and inclusion work.

Whenever colleagues drop analysis or comments in AI chat, avatars & names mark every message. These features ensure smooth collaboration and clear audit trails—vital when your survey data underpins serious HR or compliance actions.

For more practical tips on designing and launching employee diversity and inclusion surveys, check out the how-to guide on creating an employee diversity and inclusion survey.

Create your employee survey about diversity and inclusion now

Start uncovering what really matters to your team—create a survey with built-in AI analysis so you can act on diversity and inclusion insights without waiting on manual reporting. No more spreadsheet mess, just actionable feedback—designed for people, delivered instantly.

Create your survey

Try it out. It's fun!

Sources

  1. Reuters. Hedge fund employees give sector lowest diversity and inclusion score in four years - survey (2024)

  2. Financial Times. Why diverse teams boost creativity and company performance (2024)

  3. Time. “No More Mr. Nice Boss”—the post-pandemic shift in workplace empathy (2024)

Adam Sabla - Image Avatar

Adam Sabla

Adam Sabla is an entrepreneur with experience building startups that serve over 1M customers, including Disney, Netflix, and BBC, with a strong passion for automation.

Adam Sabla

Adam Sabla is an entrepreneur with experience building startups that serve over 1M customers, including Disney, Netflix, and BBC, with a strong passion for automation.

Adam Sabla

Adam Sabla is an entrepreneur with experience building startups that serve over 1M customers, including Disney, Netflix, and BBC, with a strong passion for automation.