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📊 Understanding the AI Dashboard: Structure, Features & Use Cases

The AI Dashboard provides in-depth insights into survey data through modern AI analysis. This article explains the structure, key features, and how they support your decision-making processes.


🔍 Structure & filter options

The dashboard is divided into three main pages, each of which can be filtered by organisational unit — e.g. by team or department.

📝 For anonymous surveys, teams are only displayed when at least 6 responses are available.

If additional profile attributes are available (e.g. age, gender, tenure), you can also use these for filtering — however only for quantitative data.


📈 Overview & AI analysis

Here you will find a summary of the survey results with in-depth AI analysis:

  • Basic information — Survey title and response rate.

  • Sentiment analysis 🧠 — Uses AI to detect the emotional tone of text responses.

  • Most mentioned topics — Groups text responses into topic clusters and shows the most common themes along with sentiment.

  • Answers — Lists all questions with responses, summaries, and number of submissions.

  • Correlation analysis — Shows the three strongest correlations between scale questions, including an interpretation of statistical relevance.


🚦 Pain points & measures

From insight to action:

  • Pain points ⚠️ — Problem areas sorted by importance (high, medium, low).

  • Recommended measures — Concrete suggestions for resolution, divided into short-, medium-, and long-term actions.

  • Suggested follow-up questions — New questions for upcoming surveys to explore identified topics in more depth.


🧾 Management summary

A compact overview for leaders covering key topics, sentiment, and recommendations for action for quick orientation.

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