Introducing Comment Copilot's AI-Powered Insight Generation

Employee feedback captured through listening contains important data-driven insights that can transform organizations — if you can extract them quickly enough. While traditional Natural Language Processing (NLP) can help tag and categorize comments, understanding the full story hidden within open text feedback has remained a time-intensive manual process — until now.

The Current State of Employee Listening 

Organizations are collecting more employee feedback than ever before through a variety of listening channels. While quantitative scores reveal how employees feel, it's often the written comments that explain why they feel that way. These comments are an invaluable window into the employee experience, but analyzing thousands of responses creates significant challenges for HR teams who need both broad patterns and demographic-specific understanding.

The Traditional Approach: Theme Analysis 

Theme analysis powered by Natural Language Processing (NLP) is typically what’s utilized to make sense of employee comments. This approach tags feedback into predetermined categories, tracks theme distribution, and groups similar comments together to identify patterns. While valuable, theme analysis has limitations.

Specifically, this approach:

• requires extensive manual reading for deeper understanding,
• takes significant time to analyze by demographic group,
• creates cognitive fatigue from processing large volumes of data,
• makes cross-department comparison challenging, and
• doesn't provide a clear narrative of what employees are saying.

The time required to overcome these limitations not only delays action, but also risks missing important links between numerical scores and the explanatory power of comments, especially when trying to understand variations across different employee populations.

Enter Comment Copilot: The Next Evolution 

Comment Copilot represents a significant advance in comment analysis, powered by generative AI rather than just NLP. Unlike traditional theme analysis that provides categorical tags, Comment Copilot:

• creates comprehensive written summaries of feedback,
• generates immediate insights without manual review,
• enables instant demographic filtering, and
• delivers specific, actionable recommendations.

How Comment Copilot Works

Every Comment Copilot analysis provides four AI-generated views of the feedback:

1. A comprehensive overview of main themes
2. Top 5 positive aspects needing recognition
3. Top 5 challenges requiring attention
4. Top 5 specific suggestions for improvement

This structured approach ensures organizations get both the big picture and actionable details. The system's demographic filtering capabilities allow instant analysis by:

• department,
• location,
• role, and
• any available demographic segment.

Consider this common scenario: low inter-team communication scores in the accounting department. The traditional approach would require manual filtering of accounting employee comments, hours spent reading individual feedback, and manual identification of themes to create a summary narrative of the department’s collective sentiment.

By way of streamlining the process, Comment Copilot provides:

• instant analysis of accounting feedback,
• immediate understanding of specific challenges,
• auto-generated improvement suggestions, and
• a quick comparison with other departments.

Comment Copilot in Action at Children’s Nebraska

Leading Great Plains healthcare provider Children's Nebraska recently put Comment Copilot to a rigorous test while analyzing their 2024 engagement survey results. With more than 60% of team members providing written feedback, they faced thousands of comments requiring analysis. "Our focus this year has been on decreasing time to action and making sure that the actions we take in response to our team member feedback is more meaningful," explained Michelle Harrison, a senior organizational development consultant at Children's Nebraska.

Initially, Harrison assembled a team of 13-14 culture strategists to review all comments and identify core themes. The manual process was intensive, consuming 150-200 hours of staff time. Beyond the time investment, the emotional toll was significant. "People on my team described it as really emotionally draining," Harrison noted. "When you care about an organization and what's happening, what experiences your team members and employees are having, and things aren't going great, it's hard to hear that sometimes."

After completing their manual analysis, Harrison ran the same comments through Comment Copilot as an experiment. The results were striking. "I was amazed at the similarity between what our panel of 14 experts identified as the core themes and messages and what Comment Copilot came up within just a few seconds. It was astonishing.” The AI analysis matched Harrison’s team’s manual findings so closely that "90% of the words in those sentences we kept."

Looking ahead, Children's Nebraska plans to integrate Comment Copilot into its feedback analysis process. Harrison explained, "I fully expect that it's going to decrease our time taking action on survey results and increase the well-being of our culture strategists. Next year we're going to probably start with Comment Copilot when we look at our written comments. The sorting process and the categorizing are going to be a lot easier." The organization is also exploring using Comment Copilot for lifecycle surveys and analyzing custom-curated themes beyond the standard taxonomy.

Integration with Existing Analytics 

Comment Copilot complements rather than replaces existing NLP analysis capabilities. This combined approach enables both broad and deep analysis.

Enterprise-Grade Security at Every Level 

Security wasn't an afterthought in Comment Copilot's design — it's woven into the platform's DNA. The system employs sophisticated Named Entity Recognition (NER) technology to automatically detect and remove names that could identify individuals while preserving the essential meaning of their feedback. 

Comment data moves through encrypted channels with strict retention policies, ensuring feedback never trains AI models or leaves secure environments. Perceptyx leverages OpenAI’s generative AI for advanced language analysis, while access controls at both the application and user levels give organizations precise control over who can use these powerful features.

Delivering Value Beyond Time Savings 

While reducing analysis time from hours to minutes is impressive, Comment Copilot's true value lies in its strategic impact. By providing deep context behind quantitative scores, it helps leaders understand not just what is happening but why and what to do about it — enabling appropriate action when and where it’s needed most.

The system reveals patterns across demographic groups that might otherwise remain hidden, enabling more targeted and effective interventions. These insights drive faster, more informed action — instead of waiting weeks to understand feedback trends, teams can immediately begin developing responses to employee concerns. 

This quick-turn analysis capability transforms how organizations allocate resources, ensuring efforts focus on the most pressing needs identified through employee feedback. Rather than taking a one-size-fits-all approach, organizations can develop specialized solutions for specific departments, locations, or employee groups based on their unique challenges and opportunities.

Get Started with Comment Copilot

Comment Copilot is available to all Analytics Studio users at no additional cost. Existing customers can contact us to enable the feature and request a demo of the demographic filtering capabilities.

Prospective customers interested in learning more about our approach to employee listening and action, including advanced analytics, should schedule a meeting with a member of our team.