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💡 How to Measure the Impact of Didask AI

Measure the impact of Didask AI using user feedback, usage statistics, and indirect effects (less need for support, more autonomy). These indicators allow you to evaluate the added value and estimate the ROI of your deployment.

Written by Océane
Updated over a month ago

Measuring the impact of Didask AI allows you to justify the investment and optimize its use. Although impact metrics are still being developed, you already have indicators available to assess the value provided by Didask AI.

📊 Available satisfaction indicators

Real-time user feedback

Users can evaluate each AI response via a rating system:

  • 👍 Thumbs up: Useful and relevant response

  • 👎 Thumbs down: Inadequate or inaccurate response

These evaluations are aggregated in a dashboard allowing you to:

  • Measure the overall satisfaction rate of responses

  • Identify the types of questions that generate the most dissatisfaction

This info is available in the Didask AI section > Feedback tab.

Engagement analysis

Usage statistics give you clues about valuable users find your training:

  • Repeat users: Frequent returns indicate perceived usefulness

  • Messages per conversation: Long exchanges suggest positive engagement

  • Growth in usage: Accelerating use demonstrates added value.

These statistics are available in the Didask AI section > AI Statistics tab.

📊 Indirect impact indicators

Reduction in support workload

Monitor changes in requests made to trainers and support teams:

  • Reduced number of repetitive questions

  • Fewer interruptions during training sessions

  • Gradual empowerment of learners

Improved learning experience

Look for signs of improvement:

  • Increased training completion rates

  • Fewer dropouts during training sessions

  • Positive feedback from learners about their experience

📊 Estimated ROI calculation

Time saved for trainers

Simple estimate:

  • Number of questions handled by AI × Average trainer response time

  • Example: 1,000 questions × 5 minutes = 83 hours saved per month

Improved productivity

Factors to consider:

  • Reduced time spent by employees searching for information

  • Faster skill development

  • Fewer errors thanks to better understanding

✨ Measurement tips

💡 Baseline: Document the situation before deployment (support time, learner satisfaction) to measure progress comparatively

💡 Qualitative Feedback: Supplement quantitative data with interviews with users and trainers

💡 Regular monitoring: Establish a monthly measurement schedule to detect trends quickly

💡 Communication: Share the first positive indicators to maintain and encourage team engagement


Keywords: AI Didask, coach, learning assistant, monitoring, impact, ROI


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