Palantir and Predictive Maintenance in Defence Assets

Predictive maintenance is the most oversold idea in defence analytics and one of the few with a genuinely strong business case. Both of those things are true, and the gap between them is almost entirely about whether the data exists.

On a modern platform with dense sensor coverage, recorded flight or sailing parameters and a decade of failure history, predicting component degradation is achievable and the savings in availability are real. On a thirty-year-old platform where the maintenance record is a scanned paper form and there is no telemetry at all, no model will help, and the first useful project is digitising the record.

Establish the data position before the business case

The sequence that wastes money is to approve a programme on projected savings, then discover that the failure history is incomplete, inconsistently coded, or held by the original equipment manufacturer under a contract that does not oblige them to share it. This is not an edge case. It is the normal state of affairs.

A short, honest data assessment ahead of commitment costs very little and reshapes the programme. It usually reveals that one fleet has excellent data and three do not, which is a far better starting point than a uniform enterprise ambition that will fail unevenly.

Failure history is the scarce resource

Models learn from failures, and well-maintained fleets do not fail often. That is a genuine statistical difficulty: the events you are trying to predict are rare, and the records of the ones that occurred are frequently written as free text by an engineer at three in the morning.

There are reasonable mitigations. Physics-based models can supplement sparse data where the degradation mechanism is understood. Pooling records across operators of the same type helps, where commercial arrangements allow it. Extracting structure from free-text maintenance narratives is a legitimate use of language models and often the fastest route to a usable dataset.

What does not work is treating the shortage of failure data as a problem to be solved later. It is the constraint that determines what the model can do.

A prediction nobody can act on

Several programmes have produced accurate predictions that changed nothing, and the reason is organisational. Replacing a component before it fails means consuming a spare that is not yet due, booking maintenance capacity against a schedule built on planned intervals, and occasionally removing a serviceable part. Each of those conflicts with how availability contracts and maintenance budgets are structured.

If the support contract pays for activity rather than availability, early intervention is commercially unattractive to the supplier. If spares are allocated against forecast consumption, an unscheduled draw creates a shortage elsewhere. These are solvable, but they are solved in contracts and in planning processes rather than in the model, and they need to be addressed in parallel with the analytics rather than after it.

Where the platform genuinely contributes

The useful contribution is not the algorithm, which is rarely exotic. It is holding the asset, its configuration state, its history, its parts, the available spares and the engineering capacity in one model, so that a prediction arrives attached to everything needed to act on it, and so that the lineage of the prediction can be shown to whoever has to authorise the work.

Configuration state deserves particular emphasis. Two aircraft of the same type with different modification standards are different objects for maintenance purposes, and models that ignore configuration produce predictions that are quietly wrong for part of the fleet.

Monitoring the model, not just the asset

A model trained on one operating pattern degrades when the pattern changes, and in defence the pattern changes with operational tempo, basing and climate. Without routine comparison of prediction against outcome, performance declines invisibly. Any deployment plan that does not include who checks the model and how often is incomplete.

AIC works on the integration and application layer around sustainment data, including the connections into maintenance and inventory systems that were never designed to be read by anything else. Our PAL.NET SDK gives .NET teams typed access to Foundry and their own ontology.