Poorly structured asset master data is one of the most common barriers to effective asset management. Inconsistent naming conventions, duplicate assets, and vague descriptions make it difficult for maintenance teams to find the right equipment, analyse performance, and trust the data in their systems.

In this session, industry expert Leo Brooks shares a practical real-world example of how AI was used to analyse and restructure asset records, breaking down unstructured descriptions and generating consistent naming conventions across thousands of assets. By standardising asset data at scale, the organisation was able to turn messy records into structured information that teams could search, analyse, and rely on.

Leo will walk through how the approach worked in practice, what challenges emerged along the way, and how improving asset naming and structure can unlock better maintenance insights, reporting, and long-term asset management decisions.

Key Learnings

• Why inconsistent asset naming creates hidden challenges for maintenance and asset management teams
• How AI can analyse unstructured asset descriptions and support large-scale asset data standardisation
• What clean, structured asset data enables for maintenance planning, reporting, and reliability analysis