AI Content Strategy: AEO & GEO for B2B Visibility

Generative AI is turning search engines into Answer Engines, reducing the visibility of traditional content. New B2B visibility requires an integrated strategy of SEO, AEO, and GEO.

For years, B2B content marketing was a reliable engine: invest in SEO, achieve relevant rankings, build organic visibility, and generate leads. This mechanism, which became the central growth logic for many marketing teams, is noticeably losing its reliability.

The reason is the integration of generative AI models into information retrieval. Whether directly in Google’s search results or via dedicated tools like Perplexity and ChatGPT – the systems deliver answers above the classic blue links or entirely outside the familiar Google spectrum.

For decision-makers in marketing and sales, this raises a fundamental strategic question: “What happens to the value of our content investments if the click on our own website is no longer absolutely necessary?”

Analyses already indicate that this is not a temporary phenomenon. Gartner predicts that the volume of traditional search engines will decrease by 25% by 2026¹. A forecast that we consider conservative, given the speed at which our search behaviour is changing – in our estimation, the decline is likely to be significantly more drastic.

This is not a technical “SEO update”. It is a strategic paradigm shift in the information behaviour of B2B buyers. Previous strategies that focused primarily on keyword density and backlink volume must be fundamentally rethought.

The evolution of the search landscape. What are the differences between SEO and GEO approaches?

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From Search Engine to Answer Engine: What Is Fundamentally Changing

To draw the right strategic conclusions, the difference between the two models must be clear:

Old model (Traditional Search Engine). Classic search engines like Google or Bing functioned as a directory. The goal of optimisation was to direct the user to one's own site via a high ranking, in order to answer the question there and achieve a conversion.

New model (Answer Engine). AI systems, whether integrated into Google or as standalone tools, act as synthesisers. Their goal is to answer the user's question directly in the interface by combining and preparing information from various sources.

The consequence for B2B companies: superficial “me-too” content that merely reformulates existing knowledge will drastically lose relevance. If the answer is trivial, the AI will provide it without a click on a company page occurring.

In this new model, visibility is primarily gained by those who are used and cited by the AI as a trustworthy source for the synthesis of the answer. This signals a clear departure from the old logic of “quantity over substance”.

The New Glossary for B2B Decision-Makers

Traditional Search Engine Optimisation (SEO)
Foundation: Optimised so that a website can be found by crawlers

Answer Engine Optimisation (AEO)
Citation: Optimised so that content can be used as a direct answer

Generative Engine Optimisation (GEO)
Authority: Optimised so that the brand is preferred as a trustworthy source for the answer

Manpower steigert Unternehmenseffizienz durch standardisierte CRM-Prozesse

Acht Länder, acht unterschiedliche Marketing-Prozesse – vor dieser Situation steht Manpower. Die Folge: Uneinigkeit darüber, welche Leads Priorität haben, sowie erschwertes Benchmarking und Austausch über Best Practices.

Um internationale Vergleichbarkeit zu schaffen und Lernprozesse im Unternehmen anzuregen, will das nordeuropäische Marketing-Team um Projektleiterin Tina Hingston ein länderübergreifend konsistentes Lead Scoring und Reporting einführen. Dafür holt sie sich Unterstützung des Strategiepartners andweekly.

Die Marketing-Landschaft bei Manpower

Von der herausfordernden und zeitaufwendigen Rekrutierung geeigneter Fachkräfte sind Unternehmen in vielen Branchen und Regionen betroffen. Das Ziel von Manpower ist es, dem Personalmangel weltweit mit innovativen Lösungen zu begegnen. Die ManpowerGroup mit Hauptsitz in den USA und Niederlassungen in rund 80 Ländern zählt zu den weltweit führenden Unternehmen in der Personalbranche.

Kerngeschäft ist die Vermittlung von Fachkräften aus zahlreichen Branchen an Unternehmen, die sich nicht mit zeitaufwendigen Rekrutierungsprozessen beschäftigen wollen. Darüber hinaus hilft Manpower, kurzfristige Personalengpässe zu überbrücken und Produktionsspitzen mit geeigneten Human Resources auf Zeit abzufedern. Zum Unternehmen gehören zahlreiche Tochterunternehmen – darunter auch der IT-Dienstleister Experis, den wir bereits bei seiner Marketing-Strategie unterstützt haben.

Lokale Marketing-Vielfalt bei Manpower birgt Herausforderungen

Die ManpowerGroup unterhält in jedem Land ein eigenes Marketing-Team, das individuelle Ansätze im Online-Marketing verfolgt. Zwar wurde HubSpot als All-in-one-Plattform für Marketing in den meisten Landesgesellschaften etabliert, doch das HubSpot-Knowhow und der hinterlegte Lead-Management-Prozess sind sehr unterschiedlich.

Das Problem bei Manpower: Die uneinheitlichen Marketing-Prozesse der Landesgesellschaften führen zu inkonsistenter Lead-Qualifizierung: Ein Lead, der in einer Landesgesellschaft als Sales Ready eingestuft wird, kann in einer anderen als Marketing Qualified Lead (MQL) eingestuft werden.
Daraus ergeben sich für Manpower folgende Herausforderungen:
Mangelnde Vergleichbarkeit. Unterschiedliche Definitionen und Prozesse machen es schwierig, die Leistung und Effektivität von Marketing-Aktivitäten zwischen verschiedenen Landesgesellschaften zu vergleichen. Ohne einheitliche Standards können sie Best Practices nicht identifizieren und erfolgreiche Strategien kaum replizieren.

Schwierigkeiten bei Zusammenarbeit und Kommunikation. Inkonsistente Definitionen führen immer wieder zu Missverständnissen und Fehlkommunikation zwischen Marketing- und Vertriebsteams, insbesondere wenn diese länderübergreifend zusammenarbeiten.
Verpasste Verkaufschancen. Unterschiedliche und nicht immer optimale Definitionen von MQLs und SQLs bewirken, dass Mitarbeitende bestimmte Leads unter- oder überschätzen. Falsche Prioritäten in der Lead-Bearbeitung kosten wiederum wertvolle Ressourcen.

5 erreichte Projektziele

Standardisierung der Marketing-Automatisierungsprozesse für eine nahtlose Customer Journey in den verschiedenen Manpower-Landesgesellschaften

Entwicklung homogener Dashboards auf globaler Ebene zur einheitlichen Erfassung, Analyse und Vergleich der Performances von Marketing-Kampagnen

Optimierung der CRM-Strategie durch Implementierung von Best Practices für Lead-Erfassung, -Qualifizierung, -Scoring und Reporting mithilfe des HubSpot Marketing Hub

Erzielung von Effizienzgewinnen durch Reduzierung von Inkonsistenzen zwischen den Landesgesellschaften

Erhöhung der Transparenz zwischen den Landesgesellschaften hinsichtlich Lead-Generierung, Lead-Qualität und Marketing-Performance zur Verbesserung der Entscheidungsfindung und Performance

The New Alphabet of Visibility: SEO, AEO, and GEO as an Integrated System

The response to this change must not consist of isolated tactics. It is not about an “either/or”. Future visibility in the B2B sector is based on an integrated system of three pillars:

SEO (Search Engine Optimisation): Remains the indispensable foundation. Technical health, loading times, crawlability, mobile optimisation, and a clean site structure are the prerequisites for content to be found and indexed at all. Without this foundation, even the best answer will not be processed.
AEO (Answer Engine Optimisation): This is the content strategy level. AEO aims to prepare content so precisely, clearly, and structured (e.g., through Schema Markup, clear FAQs, direct answers to “What is” questions) that it is readable and usable for AI models as ideal answer components. AEO optimises for the direct answering of the user's query.
GEO (Generative Engine Optimisation): This refers to the strategic meta-level. This is about establishing one's own brand, one's own data, and one's own experts as the unavoidable, citable authority in the market. GEO is the answer to the question: “Why should the AI trust our company more than our competitors?” GEO is the practical implementation of Google's E-E-A-T principles (Experience, Expertise, Authoritativeness, Trust).

Why Authority Is Becoming the Central Lever in AI Content Strategy

AI models can brilliantly aggregate and reformulate information. What they cannot do: generate original expertise, proprietary market experience, or unique, primary data.

In a market flooded with AI-generated content, human, verifiable expertise becomes the most important differentiator. A strong AI content strategy is therefore, above all, a human expertise strategy.

The strategic goal is thus shifting: it is not about writing “content for AI”. It is about preparing “content from real experts” in such a way that the AI recognises, prioritises, and cites it as the best and most trustworthy answer.

From Theory to Practice: 5 Steps to Implementation

How do those responsible for marketing translate this three-pillar model into an applicable strategy? It is about a methodical realignment, not short-term hacks.

1. Conduct an “Authority Audit”

Marketing teams should analyse: In which niche topics is the company already the undisputed source? Where are the real, human Subject Matter Experts (SMEs)? Resources should be focused on expanding these “authority clusters”, instead of trying to be moderately visible in every topic.

2. Move from Keywords to “Question Clusters”

The hunt for individual keywords is becoming less effective. Instead, thematic clusters should be built that definitively and comprehensively answer all relevant questions from a target group about a complex problem (e.g., “implementation of a zero-trust architecture”).

3. Implement AEO Technically (Schema Markup)

Answers must be machine-readable. This requires clean technical markup (Structured Data). The implementation of schema types like FAQ-Page, Article, Author (to link experts), and Organisation is the “language” that Answer Engines understand.

4. Implement GEO Strategically (Verifiable Authority)

Measures for Generative Engine Optimisation include:

Experts (SMEs): Author profiles must be strengthened, and their expertise (e.g., through publications, lectures) must be clearly verifiable.
Content quality: Content must be clearly understandable, contextually in-depth, and easily referenceable (citable).
Source validity: The focus must be on reliable, primary data (proprietary studies, surveys, analyses) and the correct citation of recognised external sources.

5. Think Holistically Instead of Linearly

A strong answer cited by AI does not just have an effect at the beginning of the purchasing process (Awareness). It builds trust in every phase of the customer journey – from the initial anonymous research to the negotiation and customer retention phase.

Frequently Asked Questions About AI Content Strategy

What Is the Main Difference Between AEO and GEO?

AEO (Answer Engine Optimisation) is primarily content-technical. It is about how content is structured (e.g., clear answers, Schema Markup) so that an AI can use it as an answer component. GEO (Generative Engine Optimisation) is strategic. It is about why the AI should trust a brand (e.g., expert authority, primary data, reputation). AEO ensures readability; GEO ensures credibility.

How Do I Measure the ROI of an AEO or GEO Strategy if Clicks Become Less Important?

The focus of measurement is shifting. Instead of pure clicks and rankings, new metrics are becoming more important: “Share of Voice” in AI answers, the number of citations as a source (Branded Search Volume), the quality of the leads generated (as users are better pre-informed), and the conversion rates of content that serves as authority pillars.

How Do I Start Implementing AEO/GEO in My Existing Content Strategy?

The first step is an audit. Identify your strongest “authority clusters” and your customers' most important questions. Start by optimising this content for AEO and making the authors (SMEs) of this content more visible for GEO.

Which B2B Content Formats Are Best Suited for an AEO/GEO Strategy?

Formats that definitively answer complex questions and demonstrate expertise are particularly suitable: in-depth pillar pages, “What is” glossary entries, white papers with primary data (e.g., proprietary studies), detailed “how-to” guides, and content that clearly reflects the experience of subject matter experts.

What Role Do Subject Matter Experts (SMEs) Still Play When AI Can Write Content?

Their role is more important than ever. AI can aggregate and formulate, but it cannot innovate or contribute authentic experience. SMEs are the source of the original analyses, opinions, and primary data that expose AI-generated content as interchangeable. They are the core of the GEO strategy.

Conclusion: Content Substance Determines New Visibility

AI-supported search is not a short-term trend, but a stable further development of information access. B2B companies that continue to rely primarily on the old SEO logic (ranking, keyword volume, quantity) risk strategic disadvantages and the loss of their digital visibility.

The argument for the C-level must be clear: investments are no longer just being made in “content”, but in “digital authority”. In an environment where basic information is delivered free of charge and instantly by AI, verifiable, citable authority becomes the central differentiator and the most important driver of trust.

The task for B2B marketers is not to trick the AI. The task is to make their own brand and their own experts the unavoidable, most reliable answer in the market. This requires a consistent focus on substance.

Sources

¹ Gartner (2024): "Gartner Predicts Search Engine Volume Will Drop 25% by 2026, Due to AI Chatbots and Other Virtual Agents"

Content Strategy for AI Search