Client
A multinational mining company with operations across multiple continents, seeking to accelerate the development of AI predictive maintenance algorithms.
Challenge
Algorithm development was siloed, with no common method and little buy-in from the people who had to use the results. This reduced the effectiveness of predictive maintenance algorithms and made it difficult to scale improvements globally.
Objectives
- Review existing work practices and stakeholder engagement
- Overcome organisational resistance to change
- Design and adopt agile ways of working
- Select and deploy supporting digital tools
Our Approach
Future Asset Management led a structured transformation. Key steps included:
- Stakeholder alignment workshops: We broke down silos and built cross-functional buy-in.
- A shared backlog: We redesigned development around a common backlog of equipment, failure modes and data requirements, so every algorithm started from the same definitions and data checks.
- Digital enablement: We introduced fit-for-purpose tools and visual dashboards to track progress.
- Coaching and capability uplift: We coached work rhythms and routines until the team was self-sufficient.
- Performance tracking: We put metrics in place to monitor adoption and impact.
Our focus on practical implementation and individual empowerment drove sustained behavioural change.
Results
- A 10x improvement in the rate of algorithm development
- Faster alignment between stakeholders and developers
- New ways of working and digital tools embedded, and a method that scaled across continents
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