Predictive maintenance does not start with a model

The 2026 aviation plans across the services make predictive sustainment the baseline expectation rather than an experiment. That is the right direction and it has produced a wave of pilots that will not survive their first renewal.

The reason is consistent. A predictive model needs a reliable failure history, and most maintenance records were written to close a work order, not to support inference. Cause codes are entered under time pressure, free text carries the real explanation, and the same failure is recorded three ways across three shifts.

Cleaning that up is not glamorous and it does not demo well. It is also the entire difference between a model that holds its accuracy through a deployment cycle and one that degrades quietly until somebody stops trusting the output.

Where we have seen this work, the sequence was the same: fix the recording practice, backfill what could be recovered, establish a baseline with simple statistics, and only then introduce a model, with a retraining schedule wired to actual completions.

The payoff is real. Fleet availability improvements in the low single digits sound modest until you convert them into aircraft on the line, and they compound because the instrumentation keeps working after the engagement ends.