# Dragos Tudor

Dragos Tudor writes about FMEA and risk management in manufacturing, with a focus on how AI can help engineers maintain and improve failure analyses while keeping engineering judgment and approval with people.

- Profile: https://b2binfluencer.marketing/creators/dragos-tudor/
- LinkedIn: https://www.linkedin.com/in/tudordragos
- LinkedIn followers: 10,315
- Topics: artificial-intelligence, risk-management
- Based on 14 dated posts, latest 2026-10-05

## Summary

They explain how AI can help FMEA work while engineers keep judgment and approvals.

- Best for: A medical device manufacturer considering a talk or workshop on AI in FMEA.
- Not for: A consumer personal finance app seeking short-form video placements and published rates.
- Check first: Confirm partnership availability, rates, audience details and disclosure practices.

### Takeaways

- AI should take over FMEA’s tedious work, not the judgment
- Copy-paste FMEA management breaks as product families grow
- An FMEA database can be obsolete almost as soon as it is finished

## Main ideas

- **AI should take over FMEA’s tedious work, not the judgment**: Dragos Tudor argues that automation threatens the parts of FMEA nobody enjoys, not the analysis itself. AI can replace memory-based brainstorming, copy-paste from old analyses, and meetings where engineers debate whether something is a 6 or a 7. But engineers still decide whether a risk is acceptable for a particular product, process, or patient. The product description draws the same boundary: nothing changes without review, and nothing publishes without approval.
- **Copy-paste FMEA management breaks as product families grow**: Dragos Tudor argues that copying a master DFMEA for each product variant lets the analysis drift out of control. A field fix in one variant’s DFMEA never reaches the master, so the next variant inherits the same gap. When variants are consolidated, their lessons can be retired with them. The problem is structural, not a matter of discipline: copy-paste works for a handful of variants but fails well before hundreds. The master stops being the master and becomes the oldest copy.
- **An FMEA database can be obsolete almost as soon as it is finished**: Dragos Tudor calls the completed FMEA pack a false finish line. Design changes, new field failures, supplier material changes, and staff turnover make the analysis stale; nobody opens it again until the next audit. FMEA must be maintained as a current risk record, not produced as a deliverable. The cost of building it is wasted if it is not kept alive.
- **Generic chatbot output shifts FMEA work around instead of saving it**: Dragos Tudor is skeptical of using a general chatbot as a shortcut. Its plausible-looking table is largely generic, with guessed severity ratings, controls that do not exist in the plant, and shallow cause chains. Engineers spend hours correcting rows, adding real failure modes from memory, and deleting nonsense. The sharper question: if close-enough output suffices, is FMEA being treated as risk management at all—or just as a document produced because someone asked for it?
- **The proposed fix: link every variant to a living master, with engineers approving every change**: Dragos Tudor’s solution keeps variants connected to a shared master instead of copied from it. AI ingests existing master and application FMEAs in any format, flags gaps, maps them to the AIAG-VDA structure, and runs what-if questions about new failure modes or skipped detection controls against the FMEA, control plan, and DVP&R. A closed-loop option traces a non-conformance or customer complaint to the relevant FMEA row and proposes an update. Engineers review changes before approval, while data and IP stay in the customer’s environment. The post ties this to traceability requirements for a PPAP submission.

## Work to read

- [Is AI killing FMEA?](https://www.linkedin.com/posts/tudordragos_fmea-dfmea-pfmea-activity-7505954975030890497-hGmU)
- [In 1949, FMEAs meant pen and paper. In 2026, a chatbot and a prayer.](https://www.linkedin.com/posts/tudordragos_fmea-dfmea-pfmea-activity-7508131062070415360-etmn)
- [3 years, $2m and the perfect FMEA database is finished](https://www.linkedin.com/posts/tudordragos_fmea-qualityengineering-manufacturing-activity-7506319298685136896-vGRO)
- [1 master DFMEA. 50 variants. 0 consistency.](https://www.linkedin.com/posts/tudordragos_fmea-dfmea-pfmea-activity-7511753820616581120-Jpgh)
- [Layering AI on existing master and application FMEAs](https://www.linkedin.com/posts/tudordragos_fmea-dfmea-pfmea-activity-7511025245244252160-rE5r)

## Brand relationships

- **Tacit AI** (owned_business)

## Partnerships

Dragos Tudor's five posts discuss FMEA quality practices and Tacit AI product capabilities, and include direct-message calls to action. They do not specify sponsorship terms, paid package structures, audience guarantees, editorial independence rules, disclosure practices, or an official partnership inquiry route. Before budgeting or defining deliverables, ask them to confirm partnership availability and these details.

## Alternatives

- Stephanie Fels: Choose this one when the manufacturer’s problem is production and quality operations rather than FMEA method. One post walks through three customer examples: a Tier 1 supplier spending 20 minutes of admin on each of 2,000 equipment receptions a year; a 30-site Tier 1 supplier whose non-conformance data sat in Excel files, paper forms and email threads while audit prep took weeks; and a supplier managing 4,000 purchase-order lines through email and manual ERP re-entry. The proposed workflow layer sits on top of the existing ERP rather than replacing it, starting with one workflow, one site or one team. A second post shows where visibility breaks in program reviews: status scattered across inboxes, leaving managers to react instead of prevent; quality exceptions found late in the flow; and supplier risk flagged only when shortages hit the line. For non-conformance closure, audit readiness or supplier readiness in aerospace and regulated manufacturing, this is the closer match.
- Annesha Basu: Consider this one when the deliverable is paid-media execution rather than a risk process. One post offers a pre-launch checklist for ChatGPT Ads: confirm the pixel fires on the landing page and thank-you page, not just the homepage; set source and medium as you want them to appear in the CRM; decide which event counts as a conversion before launch; give visitors still researching a clear next step on the landing page; compare platform reporting with the CRM from day one; and keep spend caps small while the channel is new. A second post gives a naming convention: source is the platform in lowercase, medium is how you paid for it, using one spelling per channel; campaign matches the platform campaign name; and content marks the variant being tested. Build URLs in a shared sheet and check the CRM after the first week. If the problem shifts to launching and measuring a new paid channel, this advice is directly usable.
- Anish S.: Choose this one if the campaign broadens beyond FMEA into demand capture and search visibility. One post argues that small-business marketing starts before the ad: with the untracked phone call, the DM reply and how the front desk handles someone already annoyed. It frames a good review response, a thoughtful DM answer and a well-handled complaint as distribution and conversion assets, not service overhead. The other reports that roughly 64.82% of Google searches in 2026 end without an outbound click, argues that this decoupling of visibility from traffic breaks click-based measurement, and calls for a shift from traffic you can measure to presence you can verify in AI answers, snippets and panels. For a buyer asking how a manufacturing brand gets found now, this is the relevant argument.

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AI-assisted summary of public posts. Audience demographics are not available. Engagement appears only where we checked recent posts. Method: https://b2binfluencer.marketing/methodology/