A utilities provider had a large inventory of ageing assets and data spread across separate systems. Call-out and investigation costs increased due to failures that appeared random. Consolidating data and utilising AI prediction in risk-based maintenance reduced costs, improved stability, and automated regulatory reporting.
Challenge
Ageing assets and fragmented data drive up infrastructure replacement and third-party investigation costs.
- Correlated failures trigger excessive call-outs and diagnosis costs.
- Regulatory reporting remains labour intensive.
- Modernisation priorities are unclear.
Guardrails
Work within established systems to maintain operational continuity.
- Dispatch depends on a constrained system that cannot be replaced.
- Reporting data is fragmented and inconsistent.
Approach
AI finds patterns in fault reports and data to directly identify root causes.
- Rules and AI aggregate faults and initiate call-outs.
- Dashboards streamline regulatory reporting.
- Agents operate on a common platform.
Impact
Faster recovery and proactive maintenance lower overall cost.
- Shorter recovery times and lower contractor spend.
- Faster recurring and ad hoc reporting.
- Equipment age and fault risk guide proactive renewal strategy.