Introducing AI works best when each stage has a question, evidence and acceptance criteria. Existing alarms and operator observations provide context for evaluating whether a forecast adds useful information.
Review the current alarms
List trigger conditions, priorities, recipients and unresolved events. Identify repeated notifications and missing responsibility. Confirm how operators verify a signal and record the outcome of an investigation.
Add context and traceable history
Connect alerts to equipment, process stage, measurement quality and operating mode. Link maintenance and laboratory records where relevant. Make event times consistent before comparing the warning with the actual incident.
Pilot one predictive question
Choose an event with enough evidence to check results. Run in observation mode and retain operator reviews. Track false alerts, missed events and useful warning time rather than reporting only an overall accuracy figure.
Expand after review and handover
Agree acceptance criteria, support responsibilities and model review triggers. Document data sources, configuration and remaining limitations. Equipment or process changes should trigger an assessment of whether previous results still apply.
Records to check
| Record group | Evidence to review |
|---|---|
| Alarm review | Triggers, ownership and verification |
| Data preparation | History, quality and event context |
| AI pilot | Measured results and operator review |
Prepare an assessment with X-IRIS
Bring the alarm list, event records, available history and the intended response to one priority risk. Review the implementation roadmap and request an assessment to agree a scope that can be tested.
