This article will give you tips on how to analyze responses from an employee survey about trust in leadership using AI-powered tools and proven approaches for both quantitative and qualitative data.
Choosing the best tools for survey response analysis
When you review survey results, the approach and tooling depend on the structure of your data and question types. Some data is easy to quantify; others need advanced tools for deeper analysis:
Quantitative data: Numbers, ratings, and multiple-choice selections (like "What percentage of employees trust leadership?") are easy to count and visualize with tools such as Excel or Google Sheets. Standard charts and pivot tables will quickly show trends, such as if only 21% of U.S. employees strongly trust organizational leadership—a number that's been declining in recent years. [1]
Qualitative data: Open-ended responses and follow-ups—where participants share why they feel the way they do or suggest improvements—are much harder to analyze manually. You simply can't read through hundreds of nuanced comments and find patterns without help. This is where AI analysis becomes essential.
There are two approaches for tooling when dealing with qualitative responses:
ChatGPT or similar GPT tool for AI analysis
Flexible and accessible: You can export survey data—such as employee narratives on trust in leadership—and paste them into ChatGPT (or another GPT-based large language model) to start a conversation about your results.
Drawbacks: This workflow is not especially convenient. It requires manual data prep and cleaning, and you may bump into limitations around prompt size or lack of structure. You'll often need to guide AI step-by-step, and it can be tricky to reference individual survey questions or filter responses based on demographics or user segments.
All-in-one tool like Specific
Designed for this job: A platform like Specific combines survey collection and AI-powered analysis under one roof. When employees respond, the system can automatically ask relevant follow-up questions, increasing the depth and quality of insights. See more about how this works here.
Instant, AI-driven insight: After data comes in, Specific instantly summarizes open-ended responses, sorts main themes, turns answers into digestible key points, and creates actionable recommendations—without endless spreadsheets or manual sorting.
Conversational analytics: Just like in ChatGPT, you talk directly to AI about your results, but with richer built-in filtering, segmentation, and organizational features. Manage what data the AI sees in each conversation, keep chats focused, and segment by NPS, department, or any survey logic for deeper understanding.
For more about AI analysis in action, check out the overview of AI survey response analysis features.
Useful prompts that you can use for analyzing employee trust in leadership survey responses
AI tools are only as powerful as your prompts. To harness GPT’s power in making sense of employee feedback on trust in leadership, here are some proven prompts I’ve used and recommend. Adapt these for your tool of choice:
Prompt for core ideas: If you want a big-picture summary of main themes—great for spotting factors behind low trust or leadership wins—use this:
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
Prompt for better results—give context: AI always performs better with more background. For example, let the AI know you’re analyzing a survey focused on trust in leadership at a mid-sized U.S. company, and describe your main concerns (e.g., transparency, communication). Here’s a way to do that:
You are analyzing responses from our employee survey about trust in company leadership. The company is 500 people in the fintech space, hybrid work, and we've noticed trust scores are dropping quarter over quarter. We want to understand the core drivers for this trend and any actionable issues raised by employees.
Prompt for follow-up on a key idea: After you surface core themes, dive deeper:
Tell me more about XYZ (core idea)
Prompt for specific topics: To validate or explore if employees mention a hot topic (such as “remote work” or “executive transparency”), try:
Did anyone talk about XYZ? Include quotes.
Prompt for pain points and challenges: This one is fundamental for trust in leadership topics—what really frustrates staff?
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 sentiment: Use this to quickly gauge overall positive/negative/neutral feelings towards leadership:
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 and ideas: This is often overlooked but extremely actionable:
Identify and list all suggestions, ideas, or requests provided by survey participants. Organize them by topic or frequency, and include direct quotes where relevant.
Prompt for unmet needs & opportunities: If you want to discover gaps, blind spots, or quick wins around leadership trust:
Examine the survey responses to uncover any unmet needs, gaps, or opportunities for improvement as highlighted by respondents.
Want to see more tips for building and analyzing these kinds of surveys? Explore our practical guide on how to create employee surveys about trust in leadership or check out question recommendations for your survey here.
How Specific analyzes qualitative data based on question type
The way AI tools like Specific tackle qualitative employee survey responses depends on the setup of each question:
Open-ended questions (with or without follow-ups): You’ll see a summary for all main responses to the initial question, as well as any associated follow-ups (such as clarifications or deeper probes into why employees do or don’t trust leadership). You get both a thematic summary and notable individual comments.
Choices with follow-ups: If you offer multiple-choice questions with optional “other—please explain” or tailored follow-ups, analysis is grouped by answer. For example, each trust driver option gets its own summary of explanatory feedback, making it easy to visualize what’s pushing scores up or down.
NPS (Net Promoter Score): If you use NPS (e.g., “How likely are you to recommend the leadership team?”) with open-text follow-ups, summaries are automatically grouped: detractors, passives, and promoters all get distinct thematic summaries about their trust (or lack thereof) in your leaders.
You can replicate this with ChatGPT, but it takes more setup—splitting data by answer/segment, copying into different prompts, and manually tracking which comments belong to which category. Specific automates this for you, saving hours and ensuring clean, structured insights for each part of your survey.
Solving context limit problems in AI analysis
One issue with AI-driven survey analysis is context size limit—what you send to AI has to fit within the model’s working memory. For large employee surveys on trust in leadership, where you may have hundreds or thousands of detailed responses, that’s a real headache—but it’s manageable with the right approach.
Here’s how we address the challenge in Specific (and you can do similar things manually):
Filtering: You can filter survey conversations, only sending to AI the responses where users replied to a particular question or picked a specific choice. For example, segment out only detractors or those citing “lack of transparency.” This focusses analysis, stays within limits, and sharpens your results.
Cropping: Instead of sending your whole survey, you select certain questions (say, all about executive integrity) and analyze those in depth. Cropping streamlines data and avoids overwhelming AI—and helps you drill into micro-topics, not just general sentiment.
Both of these approaches are built right into Specific, but you can export-filter and chunk manually for use in other tools like ChatGPT—just with a bit more work.
Collaborative features for analyzing employee survey responses
Cross-team analysis is a headache: Employee surveys about trust in leadership are highly sensitive, and everyone—HR, leadership, managers, and sometimes even external consultants—wants input. Too often, collaboration means email chains, messy spreadsheets, or endless meetings to interpret what employees actually said.
Chat-based analysis in Specific: In Specific, you collaborate with colleagues simply by chatting with AI about the data. Each chat session can be filtered by question, department, NPS score, or respondent attribute. That means every team member can create their own threads—focused on issues that matter most to them.
Clear ownership and transparency: If you spin up three separate chats—one on communication, one on executive decision-making, one filtering for detractors—each shows who started it and which filters are active. This makes sharing findings and agreeing on next steps much easier.
Multi-user awareness: When you and a teammate are working together in the AI chat, you see avatars that indicate who made each request or note. This keeps the analysis process organized, traceable, and truly collaborative—no confusion over “who asked what” or why the AI is focusing on a specific insight.
Want to launch your own trust in leadership survey and analyze it as a team? Try building a survey from scratch with our AI-powered survey generator.
Create your employee survey about trust in leadership now
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