Build internally, hire consultants, do nothing, or deploy a system that compounds with every deployment. Here’s how each option plays out.
Four paths. Three cost you time. One compounds in your favor.
 
|
Internal (your engineers) | Consultants | Not done | |
|---|---|---|---|---|
| FMEA for 1 system | Typical: engineer review of AI-generated baseline | Typical: 100-300 engineer hours spread across months | Typical: dedicated project over several weeks plus workshops | Skipped |
| Calendar time | Typical: 1-3 weeks for scoped systems | Often 3-6 months part-time | Often 6-10 weeks | Never |
| How it stays current | ✓ Built to support ongoing updates as work orders, events, and source documents change | ✕ Usually updated manually, infrequently | ✕ Usually static unless re-engaged | Not maintained |
| After delivery | ✓ Searchable, reusable working model | Static spreadsheet/document | Static deliverable | Reactive firefighting |
| Resource impact | Engineers review and approve | Engineers build it themselves | Engineers still support workshops/review | Hidden reactive cost |
| Methodology | Structured around IEC 60812 and common FMEA frameworks such as AIAG-VDA and SAE J1739 | Varies by team/site | Usually strong methodology and facilitation, but output is project-based and update cycles are expensive | None |
| Personalization | ✓ Built from your data, your systems, your history | Best context. They know the equipment. Quality varies and it’s rarely written down well. | Fresh perspective, but less equipment-specific depth | N/A |
| Traceability | ✓ Every row linked to source paragraph. Change flagging on revision. | Spreadsheet-based. Weak audit trail. | Better documented, but not linked to source data | None |
| Knowledge retention | ✓ Stored, queryable, reusable | ✕ Mostly tribal knowledge | ✕ Locked in deliverables | Lost over time |
| Survives AI model changes | ✓ Knowledge stored as domain data, not model weights. Models upgrade, your corrections transfer forward. | Tied to whatever tool or model version was used | Static deliverable. No AI layer to upgrade. | N/A |
| Scalability | Expandable across systems/sites. Supports multilingual teams. | Limited by bandwidth | Limited by budget | No scale |
| Commercial model | Predictable software/project spend | Internal opportunity cost | Recurring engagement cost | Cost shows up as failures, downtime, and audit pain |
Generic AI can generate FMEA-shaped text from an uploaded manual. Here is what it cannot do.
See comparison against ChatGPT.
Send us a sample document. In 30 minutes we run it through the pipeline and show you what we find versus a manual process. No commitment.