Predictive maintenance without the hype

Most 'AI for assets' programmes stall because they start from the model, not the decision. The useful question is narrow: which failure mode, detected how early, changes which maintenance action?

Answer that, and the data and model requirements fall out naturally — often simpler than the vendor demo suggested.

Data readiness first

A two-week data-readiness review usually reveals whether the signals exist, whether they're trustworthy, and whether anyone will act on an alert. That review saves far more than it costs.

Keep it auditable

Models drift. Without documentation, drift monitoring and a human in the loop, a predictive programme becomes a liability. Governance is not bureaucracy — it's what keeps the system trustworthy at year three.