AI can add practical value around AS9102 today, but mostly as an assistive layer on top of existing FAIs, not as an autonomous decision-maker. What is realistic depends heavily on how your AS9102 data is captured (PDF vs structured fields), how consistent ballooning and characteristic IDs are, and how integrated your PLM/MES/QMS landscape is.
1. Searchable, normalized access to historical FAIs
The most achievable use case is treating AI as an interface to your AS9102 history, especially where records are scattered across Net-Inspect, MES, QMS, shared drives, and ERP.
- Indexing AS9102 forms, ballooned drawings, and related inspection reports to make them text-searchable.
- Normalizing basic metadata (part number, revision, supplier, work center, program, date, disposition) so you can filter and query consistently across systems.
- Using AI-assisted search (natural language queries) to quickly find similar parts, prior FAIs on the same feature set, or previous dispositions for a given characteristic.
This is usually the first step because it does not change the underlying quality process; it just reduces time spent hunting through legacy data.
2. Characteristic and ballooning assistance
With reasonably clean drawing and FAI data, AI can help with some of the heavy lifting around characteristics.
- Draft ballooning support: Proposing initial characteristic lists from 2D drawings or 3D models, which a quality engineer then reviews and finalizes.
- Characteristic mapping: Suggesting mappings between drawing characteristics and AS9102 Form 3 entries, helping catch omissions or mismatches.
- Cross-part characteristic reuse: Identifying when a new FAI is effectively a variant of an older part with similar features, so prior balloons and inspection plans can be reused or adapted.
These uses still require human review and formal approval. In regulated environments, AI outputs should be treated as draft artifacts that enter normal document control and signoff workflows.
3. Pattern analysis on nonconformances and key characteristics
If AS9102 results are tied to NCRs, CAPA, or yield data, AI can help surface patterns that are hard to see in spreadsheets.
- Identifying characteristics that drive a disproportionate share of FAIs that fail or require concessions.
- Highlighting suppliers, machines, tools, or work centers that correlate with repeated FAI findings on specific features.
- Spotting revision-change effects, such as a design update that increases the likelihood of FAI issues for certain dimensions or materials.
This is realistic when your AS9102 data includes structured links to part numbers, revisions, NC records, and supplier or routing information. Without that linkage, you are limited to more superficial text mining.
4. AI-assisted FAI preparation and review workflows
Another concrete use case is making FAI preparation and review faster, not changing criteria.
- Pre-populating forms: Pulling part, BOM, routing, and drawing metadata from PLM/ERP into AS9102 forms to reduce manual data entry.
- Consistency checks: Flagging obvious issues such as missing mandatory fields, mismatched revisions, or inconsistent units of measure before formal review.
- Cross-document comparison: Comparing current and prior FAIs to ensure that planned characteristics, methods, and gages are consistent with similar parts when they should be, and highlighting unexplained deviations.
Most of this can be implemented with a mix of rules and AI models. In all cases, human approvers retain accountability and must explicitly sign off within your existing QMS processes.
5. Natural-language reporting and audit support
AS9102 data often becomes critical evidence in AS9100 audits and customer reviews. AI can help produce more coherent views without changing underlying records.
- Generating narrative summaries of FAI status for a program, cell, or supplier based on existing structured and unstructured records.
- Answering audit-style questions such as “Show FAIs for part X across revisions and summarize major findings and concessions” using indexed data.
- Preparing draft responses to customer FAI inquiries, referencing the correct forms, revisions, and linked nonconformances for a quality lead to review and finalize.
This relies on robust access control, especially if data is ITAR- or export-controlled, and should be deployed with clear boundaries on which repositories the AI layer can see.
6. Supplier FAI support and comparative analysis
Where you collect AS9102 packages from multiple suppliers, AI can help with incoming FAI triage and trend monitoring.
- Normalizing supplier AS9102 submissions into a common structure (units, naming, basic characteristic groupings) to enable comparison.
- Highlighting which suppliers struggle with particular feature types (e.g. tight bores, complex GD&T, special processes) during FAI.
- Assistive checks on supplier-submitted data such as missing signatures, mismatched part numbers, or obvious misalignments between drawings and Form 3 content.
This does not replace supplier qualification, source inspection, or traditional scorecards; it simply gives quality and supply chain teams faster visibility into FAI-related risk.
7. What is not realistic today
There are several AI ideas that are attractive on paper but usually unrealistic or unsafe in current aerospace environments:
- Autonomous acceptance of FAIs: Having AI approve or reject FAIs without human review is generally misaligned with AS9100 expectations, customer requirements, and internal quality policies.
- Automated tolerance or criteria changes: AI proposing or implementing tolerance changes directly from FAI data without formal engineering, MRB, and change-control involvement is not acceptable.
- Replacing inspection planning: AI can suggest, but cannot replace, qualified quality engineers for decisions on sampling plans, gages, or inspection strategies.
- “Plug-and-play” AI across all plants: Given site-to-site differences in data structure, system landscape, and process maturity, there is no universal AS9102 AI solution that works out of the box at scale.
Dependencies and data prerequisites
Realistic AI use depends on several practical factors:
- Data structure: If AS9102 lives only as scanned PDFs with inconsistent naming, the first step is OCR and basic structuring. Expect significant data preparation effort.
- System integration: Linking AS9102 records to PLM, MES, QMS, and ERP (part, revision, route, supplier, NC) is critical for meaningful analysis.
- Validation and change control: Any AI-assisted workflow that touches production or quality decisions must be validated, documented, and managed under formal change control, particularly where customers or regulators could rely on its outputs.
- Security and export controls: If AS9102 data includes controlled technical information, AI deployment must respect ITAR/export rules, data residency, and vendor security posture.
Coexistence with brownfield systems
In most aerospace environments, AS9102 data is spread across Net-Inspect or similar portals, legacy MES, QMS, shared drives, and email. Replacing these systems outright is rarely practical due to qualification burden, integration complexity, and downtime risk.
Realistic AI strategies usually look like:
- Adding an AI-enabled indexing and analytics layer on top of existing FAI repositories.
- Integrating at the data and API level rather than trying to replace validated MES/QMS platforms.
- Focusing first on read-only, assistive capabilities (search, summarization, pattern detection), then carefully piloting AI-assisted authoring or checks in narrow, well-controlled areas.
This approach respects long equipment lifecycles and avoids triggering full revalidation of core systems wherever possible.
