Build the data asset before the AI layer
AI can improve analysis and decision support, but it depends on identifiers, relationships, history, evidence and provenance that make operational records interpretable.
10 August 2026
AI makes it easier to extract patterns, summarise history and assist with decisions. It does not remove the need for a coherent operational record.
If asset identifiers are inconsistent, observations are disconnected from work, photographs have no context and decision history is missing, an AI layer inherits those weaknesses.
Useful analysis depends on structure
The underlying data asset should make basic questions answerable:
- What asset or process did this relate to?
- What happened?
- What evidence was available?
- Who assessed it?
- What decision was made?
- What action followed?
- What changed afterwards?
The more reliably those relationships are captured, the more useful later analytical techniques become.
Data model first, AI second
This is not an argument against AI.
It is an argument for building the operational record so that AI has something trustworthy to work with.