By Paul Okhrem · paul-okhrem.com
At some point in the last two years, something shifted in how B2B buyers do research.
It wasn't a sudden change. It happened gradually, then it was just the new normal. Buyers started asking ChatGPT which vendors to consider. They started using Claude to compare approaches. They started asking Perplexity questions they used to type into Google — and getting answers that didn't require clicking through to ten different websites.
For B2B marketing, this is a structural change, not a trend. The question isn't whether to care about AI visibility. It's how to approach it.
What AEO and GEO mean in practice
Answer Engine Optimization (AEO) and Generative Engine Optimization (GEO) are terms for essentially the same practice — optimizing how a brand appears in AI-generated responses, not just in traditional search results.
The distinction from SEO is real, though the two are not independent. SEO is about ranking pages. AEO/GEO is about being included in AI-generated answers, with enough specificity to be useful. A brand can rank on page one of Google and still be invisible in AI responses on the same topic. The reverse is also true.
For B2B companies, this matters because of where AI is entering the buyer journey — and how.
Where LLMs are showing up in B2B buying
Early-stage research. When a VP of Operations is starting to think about a software category she hasn't evaluated before, she might ask an AI to explain the landscape. What are the main approaches? Who are the vendors? What should I be thinking about? The response she gets shapes her initial mental model — which vendors she's heard of, which are considered serious players, which are associated with the use case she cares about.
If your brand is invisible at this stage, you don't get corrected later. You never enter the consideration set.
Vendor shortlisting. After initial research, buyers increasingly use AI to help narrow down options. "Compare [Vendor A] and [Vendor B] for mid-market manufacturing." The AI draws on available information to characterize each option. If that information is thin, outdated, or unfavorable, the characterization reflects it.
Technical due diligence. B2B buyers are often asking detailed questions: how does this handle multi-currency pricing? What's the integration path with SAP? What are the known limitations? AI responses to these questions draw on documentation, third-party reviews, community discussions, and analyst content. Companies with deep, accurate, accessible technical content are better represented in these responses.
Objection handling. Late-stage, buyers are often stress-testing their reasoning. "What are the risks of implementing [your product] in a company with a legacy ERP?" If AI is part of that process and your brand is associated with clear, honest content about trade-offs and implementation realities, you're in a better position than if your marketing content only talks about benefits.
What the research shows about what drives AI visibility
Based on the GEO Visibility Benchmarks we published (full data at paul-okhrem.com), a few patterns are clear.
Third-party corroboration matters more than self-published content. AI responses tend to include companies that are talked about in credible third-party sources — analyst reports, industry publications, detailed review platforms, academic or practitioner content. A company with a thin external footprint but extensive self-published content is less visible than a company with equivalent content and meaningful third-party coverage.
This has implications for where B2B marketing investment goes. Getting covered in the right publications, getting real customers to write detailed reviews, contributing to industry conversations in ways that create citable content — these matter for AI visibility in ways they didn't fully matter for SEO.
Specificity beats volume. AI pulls from content that answers specific questions well, not content that covers topics broadly. A 2,000-word technical guide on a specific integration scenario is more likely to drive AI visibility on that topic than ten 500-word overview posts. The old SEO playbook of "cover the topic broadly" is a worse GEO strategy than "answer specific questions with depth."
Consistency of claims across sources. If your website says one thing about your capabilities and a review platform says something different, AI models that encounter both will either hedge or omit. Consistent, corroborated claims — your positioning confirmed by third parties — build stronger AI representation than positioning that only exists in your own content.
Structured content helps. FAQ pages, comparison pages, structured documentation, clear headers — content formats that are easy for AI to parse and extract tend to perform better than dense, unstructured prose. This overlaps with traditional SEO but has its own character in AI contexts.
What B2B companies should actually do
Audit your AI visibility before investing in optimization. Run the queries your buyers are likely to ask across ChatGPT, Claude, and Perplexity. See where you appear, how you're characterized, and what your competitors' visibility looks like. The benchmark matters before the optimization.
Map the buyer questions, not the marketing topics. The queries that matter for GEO are the ones buyers actually ask, which are often different from the topics marketing teams want to cover. "Best ERP for mid-market manufacturing" is a buyer query. "Our industry-leading ERP platform" is a marketing topic. The first drives AI visibility. The second doesn't.
Invest in third-party presence. Analyst relationships, industry publication contributions, structured review platforms (G2, Capterra, industry-specific alternatives) — the external content ecosystem matters for AI visibility. This isn't a new insight, but it deserves more weight than it typically gets in B2B marketing planning.
Build content that answers specific questions with depth. Identify the 20 most important questions buyers ask in your category and build content that answers each one comprehensively. Not optimized for keyword density — optimized for being the best available answer to that specific question.
Track AI visibility as a metric. This requires methodology (our benchmark approach is one option), but treating AI visibility as an untracked factor while investing in GEO optimization is flying blind. Build the measurement before you scale the investment.
The honest caveat
AEO/GEO is a real discipline with real leverage for B2B companies. It's also early enough that the best practices are still being established, the measurement methodologies are imperfect, and the vendor landscape is full of people who've relabeled old SEO services with new terminology.
The fundamentals — be credible, be specific, be present in third-party conversations, answer real questions well — are not complicated. The implementation requires care. The measurement requires discipline. The companies that do all three will have a meaningful advantage as AI becomes a standard part of how B2B buyers work.
Paul Okhrem researches and advises on AI visibility for B2B companies. More at paul-okhrem.com
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