How do I improve heatmaps using AI Suggestions?

This article explains how administrators and editors can use AI Suggestions to optimise existing templates based on real service cases, resulting in faster fault resolution, fewer clicks, and more accurate probabilities.

Written By Matthias Musenbrock

Last updated About 1 month ago

What is the benefit of AI learning?

AI Suggestions improve templates by learning from real service cases instead of relying only on initial assumptions.

This leads to:

  • Faster fault resolution, because the most relevant causes are prioritised earlier

  • Fewer clicks for service technicians, as unlikely paths are deprioritised

  • Correction of initial frequencies, which are often defined before real field data is available

Over time, templates increasingly reflect actual service behaviour in the field.

How does AI optimisation work?

The AI compares existing template frequencies with patterns found in logbook entries.

As a result, the AI:

  • Suggests adjusted frequencies between causes and symptoms

  • Shows how probabilities would change after optimisation

  • Makes all changes transparent before they are applied

No changes are applied automatically.

How do I perform the optimisation?

  1. Open the Knowledge Hub

  2. Select the relevant template

  3. Open the AI Suggestions section

  4. Review the suggested frequency changes

  5. Apply the optimisation if the suggestion is plausible

The system always displays the current frequency and the suggested frequency for comparison.

How can I exclude service cases from optimisation?

Administrators and Editors can control which data is used for AI optimisation.

Service cases can be excluded if they are e.g.:

  • Outdated

  • Incomplete

  • Not representative for diagnostics

Excluding cases ensures that optimisation is based only on relevant and high-quality data.

Important notes

  • AI Suggestions do not create new causes, symptoms, or solutions

  • AI Suggestions only optimise existing frequencies

  • Optimisation is always a manual decision

  • Recommendations should be reviewed regularly once sufficient new service cases are available

FAQ

Are AI Suggestions applied automatically?
No. All suggestions must be reviewed and manually applied by an administrator or editor.

Do AI Suggestions change the structure of a template?
No. Only frequencies between existing elements are optimised.

How often should optimisation be performed?
Optimisation should be performed regularly once enough new real service cases are available.