worksheet

Predictive Maintenance AI Pilot Planning & Evaluation Worksheet

An interactive, saveable worksheet to scope, plan, and evaluate predictive maintenance AI pilots. Captures objectives, data readiness, labeling needs, measurable success criteria, timeline, governance checklist, and operational actionability so teams can run focused, measurable pilots and avoid common AI pitfalls.

Interactive Tool

Predictive Maintenance AI Pilot Planning & Evaluation Worksheet

Use this worksheet to scope, plan, and evaluate a predictive maintenance AI pilot. Capture objectives, data readiness, success metrics, timeline, governance and the operational actions you'll take on pilot outputs. Save responses to iterate and compare pilots.

Describe the specific failure mode, downtime pattern, or maintenance cost you want to reduce and why it matters.
Concrete, measurable objectives (e.g., reduce unplanned downtime by X%, reduce time-to-detect by Y hours).
List equipment types, asset IDs, locations, or process lines included in the pilot. Note why these were chosen.
Select the best-fit scope for this pilot.
Describe sensors, telemetry, CMMS logs, inspection records, operator notes, images, and timestamps. Include sampling rates and storage locations.
How many days of historical data are available for the selected assets?
Estimate completeness, consistency, synchronization, and missing-value rates.
1.0 10.0
If the model needs supervised labels (failure/no-failure), indicate whether labels exist or need creation.
If labels are needed, describe who will produce them, volume required, and estimated time/cost.
Any known transformations, aggregations, or derived features required (e.g., rolling means, FFT, temperature compensation).
Choose a primary measurable outcome the organization cares about.
Current baseline (e.g., current monthly downtime hours, current precision). Include units.
Target improvement (absolute or percent) to consider pilot successful.
Operations' tolerance for false alerts before they become burdensome.
Tolerance for missed true failure events.
Estimate avoided downtime, cost savings, safety improvements, or production gains if target achieved.
Choose how your organization will act on model outputs during the pilot.
1.0 10.0
YYYY-MM-DD
Typical pilots run 8–16 weeks but adapt to data needs.
How many pieces of equipment will be monitored?
Describe required integrations (CMMS, MES, historian, dashboarding) and constraints.
Person accountable for decisions and pilot progress.
List teams (Maintenance, Reliability, IT, Data Science, Operations) and their roles.
Who owns the data and approvals?
List any rules affecting data use or model actions.
Estimate hours, roles, cloud or compute needs, tools, and budget.
List main risks (data bias, alert fatigue, integration failures) and planned mitigations.
Specific measurable criteria that determine whether to scale, iterate, or stop.
Check off governance items completed before starting the pilot.
Add links to data samples, diagrams, dashboards, or analysis.
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