AI Automation Pilot Tracker
Log each observed case, monitor review effort and failure signals, preserve a structured evidence trail, and transfer measured results directly to the Pilot Scorecard.
- 📝 Case-level log
- 📊 Live evidence dashboard
- 💾 Local autosave
- 📥 CSV and JSON export
- ↗ Scorecard handoff
Define one measurable pilot
Set the workflow boundary, baseline, observation denominators, costs, and operating thresholds before logging cases.
Active Pilot
Pilot evidence workspace
Live evidence dashboard
Descriptive aggregates from the records currently saved in this browser.
Operational logging progress0 of 0
Evidence and risk signals
Log an observed case
Enter measured case-level data. A short anonymous reference is optional.
Pilot evidence log
Search and filter the local record. Edit or delete individual rows when a correction is required.
| Date | Reference | Outcome | Human time | Cycle | Signals | Incident | Notes | Actions |
|---|
No cases logged yetAdd the first observed pilot case above.
Back up and use the evidence
JSON preserves the editable tracker. CSV provides a portable evidence report. Scorecard handoff sends calculated aggregates through the URL without uploading the case log.
Before using the Scorecard: verify denominators, baseline comparability, review records, full costs, incident status, and sample suitability. The handoff does not mark any Scorecard control or evidence checkbox.
Measurement Workflow
Turn pilot activity into reviewable evidence
The tracker separates observations from interpretation and keeps operational targets distinct from claims about statistical validity or deployment approval.
Define
Freeze the bounded workflow, baseline, denominators, costs, planned period, and thresholds.
Observe
Record every included case with the same outcome and failure definitions.
Review
Inspect corrections, residual errors, exceptions, fallback, failures, incidents, and reviewer notes.
Decide
Export the evidence and use the Scorecard for a transparent Scale, Improve, Pause, or Stop review.
NIST describes measurement as a traceable basis for management decisions and emphasizes documented testing, evaluation, validation, and verification processes. Choose metrics and methods appropriate to the actual context. See the NIST AI Metrology Center and AI RMF Measure Playbook.
FAQ
Pilot Tracker questions
Record human handling and review time, cycle time, the acceptance or correction outcome, verified post-review errors, exceptions, fallback, technical failures, incidents, and a short non-sensitive note when useful.
No. The active pilot is stored in local browser storage and is not submitted to a server by this page. Export a JSON backup because browser data can be cleared, blocked, or lost.
Yes, when local storage remains available for the same browser and site origin. Use JSON export and import to create a portable backup.
The tracker aggregates the current entries and opens the Pilot Scorecard with baseline, pilot, quality, reliability, cost, coverage, adoption, and incident fields prefilled. Verify all values and confirm controls and evidence yourself.
No. The planned case count is an operational logging target only. Sample sufficiency and statistical methods depend on the workflow, population, variability, risk, decision, and study design.