How communicators can get the most out of qualitative feedback

Mike Prokopeak is director of Communications Leadership Council learning, community and content.

Most communicators know that qualitative feedback matters. The challenge is scale. They haven’t had the tools to use it effectively … until now. 

In a recent learning session hosted by Ragan’s Communications Leadership Council, Integral CEO Ethan McCarty said AI is making it practical to analyze large volumes of employee and customer comments, transcripts and open-ended survey responses without defaulting to a handful of cherry-picked quotes. 

The shift, he said, is from anecdote to insight. 

“Qualitative tells you the why,” McCarty said. “What’s behind the behavior—what people are making of their experience.” 

Moving beyond confirming your priors 

Most communications teams already understand the difference between quantitative and qualitative research. One tells you what’s happening; the other tells you why. 

But qualitative data has often played a supporting role. Teams collect verbatim comments, then pull a few representative quotes to reinforce a broader narrative. It’s a familiar approach shaped by time constraints more than strategy. 

Reading and coding thousands of comments is slow, expensive and often impractical. McCarty argues that AI changes that equation. 

With the right inputs, communicators can now analyze large datasets of open-text feedback and identify patterns, themes and tensions in a fraction of the time. What once required significant research resources can now happen much faster and more systematically. 

From listening to strategy 

For communicators, the value goes beyond efficiency to improved decision-making. 

Qualitative analysis is especially useful during moments of change such as reorganizations, leadership shifts or broader transformations when communicators need to understand how messages are actually landing.  

Quantitative data will often signal a problem while qualitative feedback explains it. 

“If you want to understand why it’s happening, you need that qualitative data,” McCarty said. 

That distinction matters when trying to understand what’s driving confusion and resistance and identifying which messages are resonating. Without that layer, teams risk solving the wrong problem. 

A practical framework for teams 

McCarty outlined a straightforward workflow communicators can apply with tools they likely already have access to: 

• Gather the right inputs: Start with verbatim feedback such as survey comments, focus group transcripts or interview notes, ensuring you have permission to use the data.  

• Structure the data where possible: Clean, organized datasets, especially those tied to demographic or organizational metadata, produce more useful insights.  

• Use targeted prompts: Ask specific, strategic questions. For example: “How are employees experiencing organizational change?” rather than “What are people saying?”  

• Review and refine outputs: Treat the first response as a draft. Adjust prompts, re-run analyses and test different angles to sharpen the findings.  

• Apply human judgment: Use your understanding of the organization and audience to interpret results and determine what matters for strategy.  

Structured survey data, McCarty said, can be particularly valuable because it allows communicators to compare responses across targeted segments, not just in aggregate. 

The guardrails still matter 

Using AI may make qualitative analysis easier, but it doesn’t remove risk.  

“AI doesn’t magically solve the hard parts,” McCarty said, noting that privacy, permissions and human judgment still matter. Here are a few items to bear in mind: 

• Confirm data ownership and permissions. 

• Ensure privacy and anonymization.  

• Use approved, secure tools.  

• Apply human oversight to catch errors or weak assumptions.  

Large language models can misinterpret context or overgeneralize. The role of the human communicator in the process should remain central to the process, McCarty said. 

A shift in how teams listen 

For communications teams under pressure to move faster and show results, the ability to analyze qualitative feedback at scale is a meaningful shift. It turns listening into something more actionable and potentially more credible. 

Done right, it also addresses a common gap: customers and employees alike are often asked for input but don’t see it reflected back clearly or quickly. 

AI doesn’t solve that on its own. But it makes it much easier to understand what people are actually saying and respond in a way that reflects it.