Client
A large mining organisation was seeking to adopt AI-enabled predictive maintenance tools.
Challenge
The client faced a crowded vendor landscape, with limited transparency and clarity around real-world application versus marketing hype. They needed a trusted partner to lead a structured and impartial selection process and ensure a smooth and seamless selection and implementation without disrupting existing operations.
Objectives
- Identify and validate realistic business requirements for AI enabled predictive maintenance
- Cut through vendor hype and ensure measurable value from AI capabilities
- Ensure seamless integration with existing Asset Management software platforms
- Minimise risk and preserve prior technology investments and developed intellectual property
Our Approach
Future Asset Management provided end-to-end support. Key steps included:
- Market scanning: We shortlisted vendors based on functional and technical fit.
- Evaluation workshops: We facilitated use-case alignment sessions with key stakeholders.
- Risk and integration assessments: We ensured compatibility with APM, CMMS and ERP systems, along with AI risk assessment.
- Deployment planning: We developed clear implementation milestones and governance structures.
Our unbiased and vendor-neutral approach empowered the client to make confident, data-informed decisions.
Results
- Selected a best-in-class AI-enabled predictive maintenance platform
- Achieved seamless integration with the existing technology stack
- Reduced implementation risk and accelerated time to value
- Enabled a strategic shift from pilot projects to enterprise-wide scalability
Future Asset Management 