Predictive Maintenance AI | Industrial Reliability Diagnostics

Controlled beta module

Asset reliability intelligence that starts with work-order evidence, not sensor hype.

Predictive Maintenance AI inside AI2COE analyzes work-order history, failure patterns, asset classes, maintenance backlog, and reliability signals to identify downtime risk and maintenance optimization opportunities.

Readiness note: Predictive Maintenance AI is designed as a controlled beta product module. It is ready for structured discovery and pilot design, while PartsCleanse AI remains the production anchor product today.

Primary buyers

VP Operations, Reliability Director, Maintenance Manager, Asset Integrity Lead, Plant Manager, COO

Inputs required

CMMS/EAM work orders, asset register, failure codes, downtime logs, maintenance cost, criticality, site, area, equipment class, and optional sensor/condition records.

Industries covered

Oil & GasMiningManufacturingFood & BeveragePharmaceuticalUtilitiesData CentersAviation MRO / AirlinesHealthcare SystemsRail, Metro & TransitTelecom Network OperatorsPorts, Marine Terminals & ShippingAerospace & Defense Maintenance DepotsWarehousing, Distribution Centers & 3PLCommercial Fleet, Trucking & LogisticsConstruction & Heavy Equipment FleetsHigher Education & Multi-Campus FacilitiesHospitality, Resorts & Gaming

Executive outcomes

What the module must prove before any transformation spend.

Outcome

Bad actor identification

Rank assets by repeat failure frequency, downtime exposure, emergency work, cost concentration, and maintenance backlog pattern.

Outcome

Failure-mode intelligence

Group noisy work-order language into recurring failure modes so reliability teams can distinguish symptoms from systemic causes.

Outcome

Maintenance strategy segmentation

Separate run-to-failure candidates, preventive maintenance candidates, predictive candidates, and engineering redesign candidates.

Outcome

Downtime risk prioritization

Translate maintenance patterns into executive risk language: uptime exposure, production interruption risk, cost avoidance, and planning quality.

Diagnostic workflow

Built in the AI2COE sequence: diagnose, quantify, prioritize, govern.

Ingest maintenance evidence

Upload work-order and asset extracts from SAP PM, Maximo, Oracle, Hexagon EAM, Infor, or CMMS spreadsheets.

Normalize asset and failure language

Clean free-text work orders, action descriptions, failure codes, equipment classes, and location hierarchies.

Score reliability exposure

Rank assets and failure themes by recurrence, cost, downtime, criticality, emergency work, and planning variance.

Govern next actions

Route findings to reliability, maintenance, production, and finance owners before changing PM strategy.

Board-level metrics

Metric	Interpretation
Unplanned downtime exposure	Hours, events, and operational value at risk
Emergency work ratio	Break-in work as a percentage of total maintenance demand
Repeat failure concentration	Assets driving recurring maintenance load
Maintenance spend leakage	Cost tied to preventable repeat events
Planning quality	Reactive vs planned work, backlog aging, and schedule compliance signals
Critical asset risk	High-criticality assets with rising failure or cost signatures

Pilot demand capture

Tell us where this module should attack value first.

This form routes directly into the AI2COE lead system. Use it to capture sector, buyer, and operational pain before building dataset-specific pilots.

How this becomes real product

Dataset contractDefine exact ERP/EAM/procurement extracts and minimum required columns

Truth setCollect known events, savings cases, bad actors, or price variance examples for validation

Model governanceRank findings, show confidence, expose assumptions, and require owner review

Pilot scorecardMeasure value, false positives, adoption friction, and buyer willingness to pay

FAQ

Executive product questions.

Does Predictive Maintenance AI require live sensors?

No. The controlled beta starts with work-order, asset, failure, downtime, and maintenance cost history. Sensor data can improve maturity later, but a valuable diagnostic can begin from CMMS/EAM data.

How is this different from a generic predictive maintenance platform?

It is diagnostic-first. The first output is not a black-box prediction; it is a governed reliability evidence pack showing bad actors, repeat failure patterns, downtime exposure, and owner-review actions.

What systems can provide the input?

SAP PM, IBM Maximo, Oracle, Hexagon EAM, Infor, and site-level CMMS exports can all provide usable work-order and asset extracts.

What is the first buyer outcome?

A prioritized reliability backlog: which assets and failure patterns deserve engineering review, preventive-maintenance redesign, predictive monitoring, or procurement action.