# Ferdinand Goetzen

Ferdinand Goetzen shares perspectives on B2B marketing, content strategy, and practical AI adoption. Their posts discuss making content distinctive, testing AI workflows in real marketing work, and gathering input from B2B marketing leaders.

- Profile: https://b2binfluencer.marketing/creators/ferdinand-goetzen/
- LinkedIn: https://www.linkedin.com/in/ferdinandgoetzen
- LinkedIn followers: 11,596
- Topics: artificial-intelligence, content-marketing, b2b-content-marketing
- Based on 9 dated posts, latest 2026-10-07

## Summary

Ferdinand Goetzen is most useful for B2B content strategy and practical AI adoption perspectives.

- Best for: A B2B martech brand wanting content on practical AI workflow adoption for in-house teams.
- Not for: A high-volume sponsored post package with rates, reach guarantees, and demographic targeting.
- Check first: Confirm commercial terms and recent sponsored results on comparable AI workflow topics.

### Takeaways

- Content marketing no longer works by default, so distinctiveness is the test.
- AI workflows fail in real use because they are built without marketing knowledge.
- Test AI in a safe environment before implementing it where it counts.

## Main ideas

- **Content marketing no longer works by default, so distinctiveness is the test**: Goetzen's position is that content marketing still works, but no longer automatically. For years, a team could do keyword research, fill the calendar, publish on schedule and get something back for the effort. That floor is gone: AI answers now sit between the article and the click, and every channel is crowded with lookalike pieces. What survives is content a buyer would want even if they had never heard of the company. The quality bar is a single test, not a metric: if an article could sit on a competitor's blog with the logo swapped and nobody would notice, it has added to the noise rather than risen above it.
- **AI workflows fail in real use because they are built without marketing knowledge**: Goetzen argues that almost every AI workflow that looks incredible in a demo falls apart in real-life use because technology experts build them without understanding how a B2B marketing function runs day to day. The automations look fine on the surface but miss something fundamental about how the work happens, so the output lacks quality when it enters a business. In-house marketing teams fail from the opposite direction. They know AI matters, but keeping up with every new tool and capability is a full-time job, and they already have one. So they guess, pick and choose, and squeeze implementation into whatever time is left. The result: under-tested workflows on one side and rushed adoption on the other.
- **Test AI in a safe environment before implementing it where it counts**: The prescription: try a workflow somewhere safe first, then implement it in the real operation and judge it by whether it survives real business contexts, not by how the demo looked. Goetzen argues that closing the gap between builders and marketing teams requires people who stay on top of the technology and test it in real business contexts. An agency, they say, is well placed to do this: it can dedicate time to staying current, test on its own operations before working with clients, then bring what works to clients across different industries — a harder task for a team that is already maxed out.
- **Original research built by recruiting the right respondents, not the largest sample**: Goetzen treats the State of AI in B2B Marketing survey as a flagship content asset, not a lead-capture formality. It takes five to seven minutes; the link is deliberately not public and is sent person to person because the aim is marketing leaders, not a random sample. Respondents get the report before it goes out publicly, and the stated preference is for the right 200 people over any 2,000. The survey gathers data for content: the previous edition drew answers from 110 marketing leaders, and Goetzen calls the resulting report the most valuable piece of content published that year.

## Work to read

- [Why content marketing stopped working by default](https://www.linkedin.com/posts/ferdinandgoetzen_b2b-content-marketing-strategy-examples-activity-7506249453742333952-jKmn)
- [AI workflows that look great in a demo and fall apart in real marketing work](https://www.linkedin.com/posts/ferdinandgoetzen_almost-every-ai-workflow-ive-seen-that-looks-activity-7500813531655626752--uev)
- [Running the State of AI in B2B Marketing survey](https://www.linkedin.com/posts/ferdinandgoetzen_were-running-the-state-of-ai-in-b2b-marketing-activity-7513497196537761794-dnZL)

## Partnerships

Ferdinand Goetzen's posts do not set out sponsorship terms. Across three LinkedIn posts, they write as the person commissioning and publishing content rather than as a creator advertising inventory: they point readers to an article written by the head of content, invite senior B2B marketing leaders into a survey the team runs, and argue that AI marketing workflows need testing inside real businesses—a case for working with an agency. The posts publish no media kit, rate card, sponsorship policy, disclosure statement, or public inquiry route. Ask directly for partnership terms and how to make an inquiry.

## Alternatives

- Mansoor Ali: Choose this route when the campaign's real objective is search visibility and authority building inside one vertical, rather than helping a marketing team put AI into its working week. In two posts, Mansoor Ali draws a line between publishing and being seen: content and visibility are two different things; a brand can publish dozens of articles and still struggle in search. They argue that visibility comes from being present on the right platforms in front of the right audience, and that quality placements beat producing more content. A second post sharpens the point: 30 backlinks, DR 70+ sites, 20 referring domains and 100% link delivery still say little about whether placements strengthened the brand, because a SaaS company should be visible around SaaS and a fintech company around finance. If the campaign is really about category authority through placements and the workflow-adoption framing is incidental, this is the more direct match on subject.
- Jake Huber: Choose this route when the bottleneck the buyer is trying to fix is hiring capacity, not content. In two posts, Jake Huber runs the arithmetic on a first full-time recruiter for Seed and Series A teams: benefits, taxes, an ATS, a LinkedIn Recruiter seat and sourcing tools sit on top of base salary; ramp takes four to six weeks; and the hiring plan the recruiter was hired against can change inside a quarter. They argue the move is clearly right only above roughly 15 hires a year across repeatable roles. A second post separates two failure modes a founder usually confuses: nobody good entering the funnel is a sourcing problem, while good candidates dying inside the process is a process problem. Buying sourcing when the problem is process makes the leak more expensive. If a sponsor's actual goal is to reach founders and early-stage operators deciding how to staff up, this framing speaks to that decision directly.
- Raymond C.: Choose this route when the channel is owned email for an ecommerce or direct-to-consumer brand, and the work is diagnosing and repairing live lifecycle flows. The posts offer unusually concrete methods. In one, a brand that switched email vendors discovered a month later that every image in every automation had vanished because the old vendor had hosted them. Raymond C. found the problem in week one of onboarding and traced it to flows running unattended: nobody opens their own welcome flow, so broken automation stays broken. The fix is specific: host images inside the brand's own account, and audit flows weekly by opening them on a phone as a subscriber. A second post sets out a reactivation procedure after a year on a different sending platform: build a baseline from everyone who opened at least twice in the last three months, split the rest into a second bucket, run a 25% sample, keep the blend near a 40% open line, hold the sample size rather than raising it to bring people back faster, then rebuild the engagement baseline across 30, 60, 90 and 120 days. For a retention-focused sponsor, this is a tested operating sequence rather than a point of view.

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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/