Organization and resources
Capitalize on project decisions, retrieve relevant lessons learned, and prepare the transfer of technical knowledge without replacing professional judgment.
PI Project studies how targeted uses of artificial intelligence and data analysis can draw additional operational value from those foundations
The starting point
The Aero Excellence certification leads industrial sites to clarify their processes, responsibilities, indicators, standards, and control mechanisms. This effort primarily serves an objective of industrial control.
Once these foundations are genuinely in place, they also produce a context usable by artificial intelligence and by data analysis. Histories become comparable, deviations can be linked to their causes, and decisions can rely on better-structured information.
Areas of application
The examples below concern the operational excellence part of the framework, structured into five sections. They are not part of the framework itself: they show what the foundations put in place in each section can make possible once data and practices have reached sufficient stability.
Capitalize on project decisions, retrieve relevant lessons learned, and prepare the transfer of technical knowledge without replacing professional judgment.
Identify drift, reconcile demand, capacity, and technical data, then qualify situations that require a planning decision.
Consolidate performance signals, track the cascade of requirements, and surface supplier risks to review first.
Link non-conformities, changes, routings, inspections, and history to assist impact analysis, root-cause investigation, and file preparation.
Monitor the consistency of standards and indicators, detect drift, and retrieve the factual elements needed for an improvement action.
When the process, the data, or the responsibility remain unstable, the priority is to clarify them. Artificial intelligence does not fix an organization that does not yet produce comparable facts.
The offer, step by step
Each stage produces an actionable decision. Moving to the next stage depends on the results obtained, not on a general promise about the capabilities of artificial intelligence.
Selection principle
Business value, data availability, ability to control, and compatibility with deployment constraints are examined together.
Describe the decision or task concerned, establish the reference situation, inventory the available data, and define what will need to be demonstrated.
Test the mechanism on a limited corpus, make its errors visible, and verify that the result can be checked by the people responsible for the process.
Integrate the system on a bounded scope, compare its results to the initial situation, and track useful effects as well as new control workloads.
Deploy the validated use case, ensure traceability of versions and decisions, monitor the quality produced, and organize the pathway back to human expertise.
Work already carried out
Decades of archives structured and made usable, with human validation at every step.
Discover more R2COld catalogs made searchable and cross-checked against stock data to surface inconsistencies.
Discover more DOCUMENT ENGINEERINGExtraction, structuring, and cross-referencing of old technical documents, with human supervision at every step.
Discover moreFirst discussion
The first discussion is used to determine whether the case has a sufficient industrial base, a plausible AI mechanism, and a verifiable result.
Aero Excellence is a program independent of PI Project. This offer does not constitute an Aero Excellence assessment or support toward labeling. The framework is cited as an existing industrial reference from which additional uses can be studied. Official Aero Excellence site.