A maturity model for moving from scattered LMD records to inspection-linked analytics, validated AI decision support, and closed-loop development.
Preliminary decision-support only. Final feasibility depends on base material, geometry, service conditions, inspection requirements, and expert review.
The maturity question is not which model to use first. It is whether process, material, geometry, and inspection data are connected.
Start with missing-information checks, risk routing, and evidence planning before any closed-loop claims.
Useful AI maturity depends on validation, escalation rules, drift monitoring, and expert review.
Maturity stages
Maturity signal: Knowledge lives in people, screenshots, PDFs, and machine-side notes.
AI capability: AI can only summarize generic guidance.
Next move: Start collecting consistent RFQ, material, parameter, photo, and inspection records.
Maturity signal: Machine logs, photos, drawings, and inspection results exist but are not reliably linked.
AI capability: AI can help with search, checklists, and missing-information prompts.
Next move: Create job IDs that connect RFQ, CAD/path, parameters, feedstock, and inspection evidence.
Maturity signal: Jobs have linked material, geometry, parameters, feedstock batches, and operator notes.
AI capability: AI can support offline analytics and risk summaries.
Next move: Connect process signals to measured inspection outcomes and comparable build families.
Maturity signal: Monitoring data, parameter changes, inspection results, and defect labels can be analyzed together.
AI capability: AI can provide anomaly triage, evidence routing, and repairability decision support.
Next move: Validate model behavior against known outcomes and define escalation rules.
Maturity signal: AI outputs are tested, bounded, logged, reviewed, and connected to expert decisions.
AI capability: AI can support RFQ review, process monitoring interpretation, and inspection planning.
Next move: Build governance for model updates, drift checks, and closed-loop experiments.
Maturity signal: Validated feedback loops connect process, inspection, and outcome data across repeated jobs.
AI capability: AI may support controlled closed-loop development under engineering governance.
Next move: Treat deployment as a qualification program, not a software toggle.
Related resources
The maturity model explains the organizational path. The readiness score checks the current data foundations. The evidence ladder keeps AI outputs connected to physical validation.
Framework path
