Client
A large, multi-region mining organisation seeking one AI-enabled predictive maintenance platform across its operations.
Challenge
The client faced a crowded vendor market, with more marketing than evidence about real-world performance. They needed a structured, evidence-based selection process and a smooth implementation that would not disrupt existing operations.
Objectives
- Identify and validate realistic business requirements for AI-enabled predictive maintenance
- Cut through vendor claims and ensure measurable value from AI capabilities
- Integrate with existing asset management systems
- Minimise risk and preserve prior technology investments and intellectual property
Our Approach
Future Asset Management provided end-to-end support. Key steps included:
- Understanding the data first: We mapped which tasks existed on each equipment class, how they linked to failure modes, and how both related to the inspection sheets used in the field. That mapping became the use-case set every vendor was tested against.
- Market scanning: We shortlisted vendors on functional and technical fit.
- Evaluation workshops: We ran use-case alignment sessions with key stakeholders.
- Risk and integration assessments: We confirmed compatibility with APM, CMMS and ERP systems, and assessed AI risk.
- Deployment planning: We set clear implementation milestones and governance.
An evidence-based approach, tested against the client’s own use cases, gave the client the confidence to make a data-informed decision.
Results
- Roughly 10x return on the capital investment in year one
- A best-in-class AI-enabled predictive maintenance platform selected
- Integrated with the existing APM, CMMS and ERP systems
- A shift from pilot projects to enterprise-wide scale
Related service: Predictive Maintenance Deployment · Talk to us
