Interview: How manufacturing stays digitally visible and AI-relevant

Why expertise is an underrated asset in B2B marketing

In the manufacturing sector, purchasing decisions are increasingly being made before the sales team is even involved. About 70 percent of the B2B buying journey is already complete by the time a buyer speaks with a supplier for the first time (6sense B2B Buyer Experience Report).

The reason: design engineers, planners, maintenance staff, and buyers research online through specialist forums, technical documentation, and, increasingly, AI-generated answers.

How do marketing leaders at manufacturing companies secure digital visibility and build a GEO strategy that serves AI while also strengthening trust with B2B customers?

Fabian Littau, Director Industry Business at valantic, shares his answers and recommendations in conversation with Darya Basarhina, Vice President Sales & Manufacturing Practice Lead at valantic.

Manufacturing marketing: balancing technical expertise and GEO content strategy

Fabian, why is technical expertise such an important marketing asset, especially in the manufacturing industry?

In a B2B context especially, trust isn’t built through advertising slogans or glossy brochures, but through demonstrable knowledge and real-world experience. Buyers trust design engineers, engineers, and developers who understand their craft, can explain a problem in detail, and can solve it. Anyone who wants to bring that asset into marketing has to weave that expertise into content production and campaign planning.

Why are these experts often the least visible to the public?

Subject-matter teams simply lack the time to turn answers from a consulting conversation or a customer question into a readable post for LinkedIn, a newsletter, or other marketing channels. For them, content creation is an extra task on top of their day-to-day work. But here’s the thing: AI can now take on a lot of the preparation and drafting work, helping internal expertise surface publicly more often. Visibility and how that content gets distributed then become marketing’s responsibility.

What’s the first step you’d recommend to marketing teams who want to tap into this expertise?

It helps enormously to identify the three people who most often advise customers on technical questions. They’re the experts on the questions, problems, and topics that matter to your target audience, and they can answer with real depth. These are the people who should be involved when it’s time to plan the next campaign or content series.

GEO is a central topic when it comes to digital visibility. How is it changing content and MarTech strategy, compared with SEO, for instance?

Classic SEO aims for ranking in search engines. GEO — Generative Engine Optimization — is about making sure language models can extract and reproduce information. That always requires a machine-readable structure and a clean data foundation, regardless of industry, whether that’s product specifications in machinery and plant engineering or assortment data in retail. Even genuinely excellent content that carries valuable expertise stays digitally invisible if large language models (LLMs) can’t read and cite it. Every B2B marketing team needs to factor that into its content and GEO strategy.

Structured data as the foundation for AI visibility

What does that mean in concrete terms for B2B marketing teams? What should a plant or machinery manufacturer pay attention to, for example?

Every product and specification page needs machine-readable markup based on Schema.org, technical parameters presented in tables rather than in running text, and clear answers to specific questions like “Which systems are suitable for ambient temperatures above 80 degrees?” Relevance and visibility for AI never come from content alone. It’s the interplay between the systems that provide the technical parameters and data — a PIM, for example — and the structure the content is embedded in.

Do you have an example of how product data quality and GEO go hand in hand in B2B marketing?

This becomes clear, for example, with Geberit Global and a BIM plug-in for Autodesk Revit: Product data is structured and machine-readable, embedded directly into the planning software in a format that AI systems can easily parse and reference.

What if a company has no idea yet how visible it is to AI?

These companies should start by conducting an assessment as soon as possible. valantic can support them with two formats:

A GEO Audit shows where a company currently ranks in AI search results, how well it is understood by language models, and what marketing professionals can implement immediately to improve visibility in AI responses.

A GEO Workshop, on the other hand, is aimed at marketing and communications teams who want to understand how SEO is evolving in the age of AI and what that means for their content strategy in the long term. The focus here is less on evaluating existing websites and more on knowledge transfer and skill-building to integrate GEO expertise into daily work.

Neither format replaces a long-term strategy, but both provide guidance within a few hours and minimize the risk of projects falling by the wayside.

MarTech projects without the big bang, measured against sales KPIs

What surprises companies most when they see how AI models perceive them?

Most don’t even expect to show up in language models at all, and then they’re mostly surprised by what the AI answers actually serve up: usually outdated or incorrect information. That happens, for example, when the model draws on a three-year-old blog post or a competitor’s comparison portal as its source. That’s not just a visibility problem — it’s a reputational risk.

Do companies need to launch a major overhaul of their MarTech stack to improve their digital visibility for AI?

No, quite the opposite. One of the most common mistakes is to tackle the entire system landscape at once and kick off a year-long project that ends up losing everyone’s support. A more effective and promising approach is to start with a single use case that delivers results within a few weeks, for example a GEO project for a single product line or specification page. For that use case, the first step is to build clean structure, markup, and parameters, then measure the impact and optimize iteratively.

How can the success of MarTech and GEO content strategies be measured?

The concrete value becomes clear, for example, in a before-and-after comparison of won deals in that product line. That’s a really important point: MarTech’s contribution to pipeline and revenue growth only becomes visible once it’s tied to sales KPIs, not marketing metrics like clicks or sessions.

Those were valuable recommendations for the next steps in manufacturing marketing. Thank you for your insights, Fabian!