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.
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
Dragos Tudor writes about FMEA and risk management in manufacturing, especially how AI can help engineers keep failure analyses useful without taking engineering judgment out of their hands.
The posts make a consistent case: an FMEA should guide decisions, not become a stale document built for an audit.
They illustrate the problem with copied analyses that drift across product variants, a costly database that goes out of date soon after completion, and generic chatbot output that engineers must spend hours correcting.
Tudor makes abstract arguments about AI and risk management concrete.
In one post, a field issue fixed in one of 50 product variants never reaches the master analysis, so the next variant inherits the gap.
In another, they contrast generic AI-generated failure modes and invented plant controls with analyses grounded in a company’s own documents and reviewed by its engineers.
These examples show where they see AI helping—reducing repetitive work and connecting updates—and where human judgment remains essential: deciding which risks matter and approving changes.
The posts are useful to readers evaluating AI in FMEA because they address potential efficiencies, failure modes and approval boundaries.
Dragos Tudor writes about failure mode and effects analysis (FMEA) in manufacturing and where AI belongs in that work.
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Across the posts, the position is consistent: FMEA fails as a one-time document and succeeds as a living record.
AI should take on the tasks engineers dislike—brainstorming failure modes from memory, copying old analyses, and haggling over scores—while people decide whether to accept risks.
The posts connect that argument to Tudor’s product, described as layering AI onto existing FMEAs, with engineers reviewing and approving every change.
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.
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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.
In a post asking whether AI is killing FMEA, Dragos Tudor draws on work with FMEA teams in pharma, chemicals, mining, and automotive. Their conclusion: FMEA is not dying; it is becoming what it was designed to be—a decision tool rather than a documentation exercise.
Generic chatbot output shifts FMEA work around instead of saving it
Dragos Tudor is skeptical of using a general chatbot as a shortcut.
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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?
Pasting a process into ChatGPT returns a table where roughly half the failure modes are generic. The engineer then spends around three hours editing rows, fixing scoring, and removing entries that make no sense, ending up with an FMEA effectively built from scratch.
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.
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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.
When an 8D investigation finds a gap in a variant, the fix can be approved once and reach the master and every variant that shares the affected row. That way, growing a family to hundreds of variants—or consolidating it back to a few—does not lose the lesson.
Dragos Tudor starts with the failures of copy-paste variant management and generic chatbot output, then presents a linked, approval-gated process as the answer. The argument and product description reinforce each other rather than standing as separate claims.
Start here for the clearest statement of how Dragos Tudor thinks about AI and FMEA.
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It answers the question newcomers are likely to have: does automation replace risk analysis or just the tedious parts?
Tudor argues that AI does not replace FMEA, only the parts engineers dislike: brainstorming failure modes from memory, copying old analyses and sitting through long meetings about whether something scores a 6 or a 7.
They draw a clear line: AI cannot decide what matters or whether a risk is acceptable for a given product, process or patient.
That judgment stays with engineers.
The framing identifies exactly what AI automates—and what it deliberately leaves alone.
Tudor draws on experience across pharma, chemicals, mining and automotive, applying the same argument across different regulated settings.
For brands planning a campaign
For a medical device manufacturer, this is the strongest opening for a talk: it addresses the concern that AI could undermine risk management. The argument frames AI as a way to cut copy-paste and meeting overhead while leaving acceptance decisions with the engineering team.
This post shows what Tudor considers a bad use of AI—a useful counterpoint to the hype.
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It lays out the failure mode: paste a process into a general chatbot and get back a plausible-looking table.
Tudor describes a familiar shortcut: paste a process into a general chatbot, get a table, then find that about half the failure modes are generic, severity ratings are guesses, listed controls do not exist in the plant and cause chains are shallow.
Engineers spend hours repairing rows, adding real failure modes from memory, correcting scores and deleting nonsense.
Tudor asks what the AI actually saved.
The post ends with a pointed alternative: if close enough is good enough, the document is not really being used as a risk management tool.
For brands planning a campaign
This offers a concrete counterexample to generic chatbot output, separating serious AI-assisted FMEA work from a table that only looks convincing. For a workshop, use it as a before-and-after exercise to show why AI needs to draw on a company's own documents and plant reality.
This post explains why FMEA work becomes fragile once the original project ends.
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It is Tudor's clearest case for treating risk analysis as a living record, not a completed deliverable.
Tudor describes a company that spends three years and about $2 million building a pristine FMEA database with engineers, consultants and enterprise software.
The database is polished and comprehensive, but six weeks later a design changes, a new failure mode appears in the field, a supplier changes a material or the person who built it retires.
The FMEA is out of date, and nobody opens it until the next audit.
The numbers make the point: three years and $2 million to build something accurate for six weeks.
For brands planning a campaign
This story makes the case for an ongoing AI-assisted update process rather than a one-off FMEA project. For a medical device audience, it supports keeping risk files current between scheduled reviews instead of treating them as audit-time artifacts.
This post details the product-family problem and explains why copy-paste FMEA management breaks as variant counts grow—a practical issue for companies with many related products.
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Tudor walks through a master DFMEA with 50 variants.
Each starts as a copy of the master, then drifts: variant A drops a sensor, variant B adds a connector, and variant C changes a material.
When a field issue is fixed in variant C's DFMEA, the fix never reaches the master, so the next variant inherits the same gap.
Tudor says this is not a discipline problem: copy-paste works for a handful of variants but breaks well before hundreds.
The master stops being the master and becomes the oldest copy.
For brands planning a campaign
This is the clearest case for linking variant FMEAs to a shared master. For a manufacturer with product families, it can anchor a talk segment on traceability and whether lessons from an 8D should flow back automatically or first be reviewed by variant owners.
This is the intended solution picture: Tudor describes how their tool works in practice, including the controls that keep engineers in charge of approvals.
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Tudor describes a system that ingests existing master and application FMEAs in any format, evaluates their quality, flags gaps, maps them to AIAG-VDA structure and standardizes them across a library.
It generates outputs from a company's own documents and data.
Users can ask what-if questions in a chat, evaluating new failure modes, design changes 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, flags the gap and proposes an update.
Tudor says nothing changes without engineer review or publishes without approval, and that data and IP stay in the customer environment.
The post links this to PPAP traceability from DFMEA through control plan.
For brands planning a campaign
This is the demonstration piece for showing AI-assisted FMEA grounded in existing documents and approval workflows.
For a medical device brief, focus on closed-loop traceability and engineer approval.
Ask Tudor to substantiate the capability claims in their product description.
FMEA as a living decision tool, not a one-time document3
AI-assisted FMEA with engineer approval2
Master and variant DFMEA drift1
Skeptical evaluation of generic AI FMEA output1
Cross-industry FMEA practice and risk acceptability1
AI-assisted FMEA with engineer approval
Dragos Tudor describes layering AI onto existing master and application FMEAs: uploading any format, evaluating quality, flagging gaps, mapping to AIAG-VDA structure, running what-if scenarios, and tracing non-conformances or complaints back to FMEA rows.
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They draw a clear boundary: engineers retain full control; nothing changes without review or publishes without approval, and data and IP stay in the environment. A separate post makes the same distinction, arguing AI can remove brainstorming, copy-paste, and rating debates but cannot decide what risk is acceptable for a product, process, or patient.
Product-led workflow explanation and an argument for a clear judgment boundary.
Ask for an independent case study or measured outcome before treating the Tacit AI post as evidence of results.
For brands planning a campaign
For a medical device manufacturer, this is a strong basis for a talk or workshop on AI as an assistant to FMEA work, not a replacement for engineering judgment. The session should show review and approval gates, traceability from DFMEA through control plan, and closed-loop handling of complaints or non-conformances.
Field fixes made in a variant DFMEA do not reach the master, so the next variant inherits the same gap.
When variants are retired or the family is consolidated, lessons can be lost.
Dragos Tudor argues that copy-paste works for a handful of variants but breaks at scale.
They describe a linked approach where each variant stays connected to the master and an 8D fix can flow back once engineers approve it.
Scenario narrative built around a master FMEA, variants A to D, and an 8D field issue.
Ask for quantified failure data and named product examples.
For brands planning a campaign
Useful for a workshop on product families and variant control in medical device manufacturing. The practical focus is maintaining FMEA lineage, change control, and lesson reuse across variants without weakening approval discipline.
FMEA as a living decision tool, not a one-time document
Across several posts, Dragos Tudor argues that an FMEA is not finished when the database or pack is complete.
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One post describes a three-year, $2m FMEA database that was accurate for six weeks before a design change, field failure, supplier material change, or retirement made it stale.
Another criticises generic ChatGPT output that produces plausible-looking tables with generic failure modes, guessed severity ratings, and controls that do not exist in the plant.
A third frames AI as removing the disliked parts of FMEA while leaving risk acceptability and engineering judgment to engineers.
Parable, critique of generic AI output, and reframing argument.
Ask whether the $2m and six-week figures are based on audited project data.
For brands planning a campaign
A medical device workshop should address the operating model around FMEA: review cadence, ownership, change triggers, and how AI keeps the analysis current without turning it back into a documentation exercise. The talk can contrast a living risk decision tool with an audit-driven document.
One post tests the common shortcut of pasting a process into ChatGPT and accepting the returned table.
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The result is around half generic failure modes, guessed severity ratings, controls that do not exist, and surface-level cause chains.
The engineer then spends hours correcting rows, adding real failure modes from memory, fixing scoring, and deleting nonsense.
Dragos Tudor asks whether AI saved time or just moved the work around, and whether FMEA is being treated as a risk management tool or a document produced because someone asked for it.
Before-and-after ironic example with direct questions.
Ask for a measured comparison between generic ChatGPT use and specialised FMEA platforms.
For brands planning a campaign
This gives a medical device audience a realistic frame for an AI-integration talk: show where generic AI fails, what validation and human correction require, and how a specialised workflow differs. It also supports a workshop exercise comparing generic output with engineer-reviewed output.
Cross-industry FMEA practice and risk acceptability
Dragos Tudor says they have worked with FMEA teams in pharma, chemicals, mining, and automotive, among other sectors.
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Their recurring boundary is that AI can take over memory-based brainstorming, copy-paste from old analyses, and rating debates, but engineers must decide whether a risk is acceptable for a particular product, process, or patient. The post treats FMEA as a decision tool rather than a documentation exercise.
Sector list and a clear human-judgment boundary.
Ask for client, project, or case references to substantiate the cross-industry experience claim.
For brands planning a campaign
For a medical device buyer, this shows sector range and a judgment-first position. A talk can focus on regulated and high-risk environments, using medical device examples to test how AI-assisted FMEA preserves patient and process risk decisions.
The posts are short LinkedIn arguments built around concrete scenarios, product announcements, direct questions, and hashtags.
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They often invite readers to comment or send a direct message, using accessible language rather than technical specifications. The conversational, question-led approach ties the commercial message to a practical FMEA problem.
Short scenario posts, product demos, direct questions, and call-to-action endings.
Ask for examples of long-form content, video, or event speaking; none appeared in the reviewed corpus.
For brands planning a campaign
Brands can evaluate Dragos Tudor for LinkedIn-native educational content and direct-response campaigns. A talk or workshop would likely suit a question-led format that starts with an operational problem and moves to a working method, rather than a formal keynote.
The posts document one commercial relationship: Tacit AI, an FMEA software product Dragos Tudor says they built and actively promote.
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They do not name an employer, clients, sponsors, or affiliates, and disclose no paid partnership; buyers should ask whether the Tacit AI promotion is paid. ChatGPT appears as an example of generic AI output, while AIAG-VDA is the standard the product maps to; neither is presented as a commercial relationship with them.
Tacit AI
Owned business
Posts describe Tacit AI as FMEA software built by the poster’s team: “That is why we built Tacit AI.”
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The product layers AI on existing master and application FMEAs, flags gaps, maps documents to AIAG-VDA structure, and runs what-if analysis from chat, with engineers reviewing and approving every change.
The creator says their own data and IP stay in their environment.
The posts invite readers to comment or send a direct message, showing active product promotion.
The exact role and any ownership stake are unclear; buyers should ask what the creator’s role is and whether they hold a stake.
A post about a master DFMEA copied into fifty variants and a parable about a three-year, $2m FMEA database that stayed accurate for six weeks explain why Dragos Tudor treats FMEA as a living record, not a one-time deliverable.
In a post asking whether AI is killing FMEA, they draw the boundary that runs through the rest: AI can absorb brainstorming, copy-paste and scoring debates, but engineers decide what risk is acceptable for a given product, process or patient.
Read the ChatGPT critique next to see what generic tools get wrong, then the Tacit AI posts on the linked, approval-gated process.
The posts are short, scenario-led and usually close with a question or invitation to comment, making the discussion threads part of the material.
For adjacent manufacturing-quality reading, Stephanie Fels writes about non-conformance data, audit readiness and production visibility in aerospace supply chains.
Both write short LinkedIn posts about manufacturing quality operations, using concrete scenarios such as non-conformances, corrective actions, audit preparation and the gap between what a system records and what a team can act on.
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Both frame the problem as one of process and data before naming a product.
Fels writes about adding an operations and visibility layer on top of existing ERP and MES systems, using supplier case studies on production admin, non-conformance trails and purchase-order handling. For FMEA expertise, look elsewhere: her posts don’t cover the method, AIAG-VDA structure, variant management or AI’s role in risk analysis.
The measured LinkedIn following is 10,315 on 7 October 2026.
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Posts focus on FMEA, risk management, quality engineering, and manufacturing, with recent themes around using AI to maintain DFMEA and PFMEA libraries while keeping engineering approval with people.
This makes the audience appeal plausible for a medical-device manufacturer improving FMEA, but Dragos Tudor’s posts don’t verify that followers work in medical devices, hold quality or regulatory roles, or have budget authority.
Treat them as a possible route to a general FMEA and manufacturing-quality audience, not verified access to medical-device decision makers.
Ask for recent results on comparable FMEA and AI posts and any audience survey covering industry, job role, seniority, and geography.
Dragos Tudor's five posts discuss FMEA quality practices and Tacit AI product capabilities, and include direct-message calls to action.
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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.
Strong fit for a paid talk or workshop. There’s no documented offer or rate structure for a standing sponsorship package.
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Brief
A medical device manufacturer wants to improve its FMEA and risk management practices and is considering a talk or workshop on integrating AI into FMEA without handing risk decisions to a tool.
Reasons
Risks and gaps
The process described in the product post is Dragos Tudor’s own product description, not an independent case study. No medical device client, project or measured outcome is named; ask for independent evidence before claiming results in a regulated device environment.
The LinkedIn following was 10,315 on 7 October 2026. The posts don’t show how many followers work in medical device quality, regulatory or engineering roles, or whether they hold budget. Topic relevance can support a talk, but this information can’t support a campaign promising reach into device decision-makers.
The reviewed corpus shows no speaking, workshop or event track record, and there’s no video or long-form writing to assess delivery. If a proven conference speaker is required, ask for recordings or past session feedback before booking.
No sponsorship formats, rate card, media kit or partnership inquiry route are documented. Agree on budget and deliverables in a written offer rather than inferring them from the posts.
When to choose a specialist
Poor fit. Dragos Tudor’s posts provide no support for a campaign targeting young professionals interested in personal finance, commercially or in terms of content.
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Brief
A consumer personal-finance app wants paid short-form video placements from creators who can reach a broad young-professional audience, with published rates, a stated disclosure practice and platform demographic guarantees.
Reasons
What would change the answer
A written sponsorship offer with rates, disclosure terms and audience data by industry, role, seniority and geography.
Evidence of video or audio work, or a pilot delivered and measured before committing to a video campaign.
A brief that needs FMEA, quality engineering or regulated manufacturing audiences rather than consumer finance reach.
A worked campaign example
Arc
Detail
Present the master DFMEA with 50 variants. Variant A drops a sensor, variant B adds a connector, and variant C changes a material. A field issue fixed in variant C never reaches the master, so variant D inherits the same gap. When the family is consolidated, the lesson retires with the variant. Ask the room how many master FMEAs they maintain and how many variants sit under each. The point lands because it reflects their situation, not a hypothetical.
Tell the story of the three-year, roughly $2m database that was accurate for six weeks. Name the four things that break it: a design change, a new field failure mode, a supplier material change and the retirement of the person who built it. Ask what in their process detects those four events today. Most rooms will not have a good answer, setting up the rest of the session.
Set the boundary plainly: AI can absorb brainstorming from memory, copy-paste from old analyses and the argument over whether something is a 6 or a 7. It cannot decide whether a risk is acceptable for a particular device, process or patient. That decision stays with the engineers in the room.
Where AI helps and where it does not (10 minutes).
Detail
Give each group a short process description and ask them to produce a failure mode list twice: once with a general chatbot and no company context, and once against their own existing FMEA and plant controls. Then have them mark which rows they would sign their name to. The generic list should produce roughly half generic failure modes, guessed severity ratings, controls that do not exist on the floor and shallow cause chains, requiring hours of repair. The exercise makes the case for grounding without anyone having to argue for it.
Exercise: generic output versus grounded output (25 minutes).
Detail
Show a grounded example: existing FMEAs ingested and mapped to AIAG-VDA structure; what-if questions run against the FMEA, control plan and DVP&R; and a non-conformance or customer complaint traced to the relevant row with a proposed update. Spend most of the time on governance: nothing changes without engineer review, nothing publishes without approval, and data and IP stay in the company environment. Regulated device teams will scrutinise this closely.
The closed loop and the control model (20 minutes).
Detail
End with an unresolved question from the source material: when a variant's 8D finds a gap, should the fix flow back to the master automatically, or should every variant owner re-review it first? Have the room choose and defend an answer against their own change-control process. The debate forces the operating model into the open.
Close with the decision the room owns (15 minutes).
Audience
FMEA owners, design and process engineers, quality and reliability engineers, regulatory affairs, and engineering managers who approve risk decisions. Keep the room to about 25 people so groups can work effectively.
Format
A 90-minute workshop for one company, or a 45-minute talk followed by 30 minutes of questions. The workshop makes better use of the topic: the value is in the exercise, not the slides.
Measurement
Agree on the success test before the session: for example, a documented approval gate for master FMEA updates, a named owner for each change trigger, and a second session booked within 90 days to review what the first changed. Satisfaction scores alone will not show whether the process improved.
Next step
Ask Dragos Tudor for a written workshop outline, a fee, and any past session recording or feedback from a regulated manufacturing client. Confirm the medical device references rather than assuming them from the stated pharma, chemicals, mining and automotive experience.
Takeaway
A one-page decision sheet the team fills in during the session: current master and variant inventory; the four change triggers they can and cannot detect; where AI will and will not be allowed to act; and the approval gate for any update reaching a master FMEA. It should be usable without the workshop or the software.
Title
From Copy-Paste to Control: Integrating AI into FMEA Without Giving Up Engineering Judgment
Why this angle
The brief asks for AI integration without losing engineering judgment. Every segment above is built from content already published, and the judgment boundary is stated in the source rather than invented for the pitch. The session also gives the manufacturer something portable: an agreed review gate and a change-trigger list, which matters more than a demo if the goal is a better risk process rather than a tool purchase.
Choose this one when the manufacturer’s problem is production and quality operations rather than FMEA method.
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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.
The posts do not address the FMEA question: they cover neither DFMEA and PFMEA nor AIAG-VDA structure, variant control or where engineer approval should stop AI output.
A risk-management workshop focused on those topics would lack the judgment boundary it needs.
The content reads as product case material for a named platform, and the supplier—not an independent source—reports the time and closure figures.
Ask for independent validation before treating those numbers as evidence of the method’s effectiveness.
Consider this one when the deliverable is paid-media execution rather than a risk process.
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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.
These posts cover channel operations at the account level, not quality engineering, manufacturing risk or regulated products. They offer no teaching for an engineering audience and cannot support a medical-device FMEA need.
Choose this one if the campaign broadens beyond FMEA into demand capture and search visibility.
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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.
Neither post covers risk, reliability or regulated manufacturing, so neither addresses an FMEA or design-control need.
The claims are broad and partly statistical, with no cited study.
Ask for the supporting study, audience figures and campaign cost before assessing fit for an FMEA or design-control campaign.
Dragos Tudor's LinkedIn posts cover FMEA and DFMEA/PFMEA practice, master and variant consistency, database maintenance, generic chatbot output, and an AI-assisted FMEA process.
The posts describe Tacit AI, an FMEA software product presented as built by Tudor's team, alongside the argument that AI can absorb brainstorming, copy-paste and scoring debates while engineers keep the decision about acceptable risk.
The posts do not show publication dates, so no chronological change can be assessed; the LinkedIn account showed 10,315 followers on 7 October 2026.
The posts here do not include video, audio, long-form writing, event recordings, demos or named client cases, and do not publish sponsorship terms, rates or a partnership inquiry route.
Tudor's descriptions of the Tacit AI process are not independently verified.
This page covers only Tudor's LinkedIn account and those five posts.
