What Industrial Consultants Won't Tell You

The most common thing I see in industrial AI programmes is a company that has skipped Layer 1 and gone straight to Layer 2. Layer 1 is a unified, governed data foundation - a reliable, timestamped, single version of operational truth that every function reads from. Layer 2 is AI, machine learning, and predictive analytics. The two are not interchangeable. You cannot have Layer 2 without Layer 1.

Vendors don't tell you this because they sell Layer 2 products. System integrators don't tell you this because Layer 1 is less glamorous and harder to pitch at board level. And so organisations buy an AI demand planning tool and train it on siloed, inconsistently formatted, politically adjusted data - and then spend 18 months wondering why the forecast accuracy has not improved.

The sequencing is non-negotiable: master data rationalisation, then integration, then analytics, then AI. Every organisation that has built a sustainable AI capability in manufacturing or supply chain followed this sequence. Every organisation that skipped it has a failed AI project somewhere in their portfolio.

Every PdM vendor will show you a demonstration where a model predicts a bearing failure three weeks in advance. What they won't show you is the 18 months of labelled failure history the model was trained on, the data engineering required to get clean sensor feeds, and the CMMS integration that turns a prediction into a work order.

In practice, a PdM programme on a new asset group - one that does not have structured historical failure data - typically requires 6–12 months of data collection before a reliable supervised model is possible. The first phase of any honest PdM programme is not modelling. It is sensor deployment, data pipeline construction, and CMMS integration. The model comes third.

This doesn't mean PdM isn't worth pursuing. It is - typically 15–25% reduction in unplanned downtime once the programme matures. But the organisations that get there are the ones that planned for the real timeline, not the vendor's pilot timeline.

The third thing most industrial consultants will not say out loud is this: the goal of a data programme is not to make more decisions faster. It is to make better decisions in the decisions that matter. There is a meaningful difference.

I have seen organisations deploy 40-metric dashboards that nobody opens, because the signal-to-noise ratio is so low that the dashboard requires a data analyst to interpret it before an operations manager can use it. The analytics investment produced more data, not better decisions.

The programmes that deliver sustained operational value focus on three to five high-value decision points - replenishment, maintenance scheduling, shift planning - and instrument those specifically. Not everything worth measuring is worth optimising. Clarity about which decisions you are trying to improve, and how the data will change those decisions, is the discipline that separates programmes that compound from programmes that stall.