I remember the first time I saw an AI Overview pull an answer straight from a competitor’s page instead of ours. Not a ranking. Not a snippet. A full, synthesised answer sitting above everything else on the page. That was the moment I realised the rules had changed.

To get your B2B content cited in AI overviews, you need to do three things differently:

• Shift from keyword targeting to question answering,
• Structure your content so AI systems can actually extract it,
• Build genuine author authority.

This is generative engine optimisation (GEO), and it’s reshaping how search works.

AI overviews now trigger on 48% of all tracked queries, up 58% year on year BrightEdge. For B2B technology queries specifically, that number is 82%.

SEO vs GEO: what actually changed

Traditional SEO optimised for rankings and clicks. You targeted a keyword, earned a position on page one, and waited for traffic. GEO optimises for something different: being the source AI systems cite when they generate an answer.

58.5% of US Google searches now end without a single click SparkToro. AI overviews compress what used to be ten blue links into one synthesised answer. For the user, that’s convenient. For the brand that isn’t being cited, it’s a problem.

For B2B, the impact runs deeper than lost clicks. 67% of B2B buyers now prefer a rep-free buying experience, and 45% used AI tools during their most recent purchase Gartner.

Buyers are doing more independent research before they ever speak to a vendor. AI overviews accelerate that research phase. If your brand isn’t the source being synthesised, you can be invisible for an entire consideration window without ever knowing it.

The upside is real, though. Brands cited in AI overviews earn 35% higher organic click-through rates than brands on the same results page that aren’t cited Seer Interactive. Getting cited doesn’t just protect traffic. It amplifies it.

The shift from SEO to GEO isn’t a tweak to your keyword strategy. It changes what visibility actually means.

How to structure content AI overviews actually cite

AI overviews don’t cite randomly. They pull from content that’s clear, authoritative, and directly responsive to the question being asked. The format matters as much as the substance.

Here’s what I’ve seen work across the brands I’ve worked with in healthcare, beauty, travel, and e-commerce:

• Answer first, always. AI extracts the first clear answer it finds. Lead every section with the direct answer, then build the supporting detail beneath it. If your best insight is buried in paragraph fourteen, AI will skip it.
• Structure for extraction. Use clear H2 headings that mirror how people phrase questions. Keep paragraphs short. Use bullet lists for steps, criteria, and comparisons. AI can’t extract a useful answer from a wall of text.
• Build from real questions, not keyword tools. The queries reaching AI search today are full sentences, not two-word keyword strings. Forums like Reddit, Quora, and LinkedIn surface the exact language your buyers use when they’re not talking to vendors. That language matches AI search queries far better than anything a keyword planner will give you.

I learned this the practical way. I was working with a healthcare brand where organic visibility had flatlined. Instead of chasing more keywords, I started reading forums to understand how the audience spoke about their concerns. Based on those real conversations, I created blog content and started contributing on Reddit, Medium, and Quora.

Here’s what surprised me most: the brand’s domain authority grew from 15 to 28 in six months. More importantly, the brand started appearing in LLM-generated responses. Not because we gamed anything, but because we were answering the exact questions AI systems were being asked.

That experience shaped how I think about building content that AI search engines actually cite. It’s less about optimisation tricks and more about genuinely answering what people need to know.

Why author authority matters more than ever in AI search

And honestly? This is the part most B2B teams skip.

E-E-A-T (Experience, Expertise, Authoritativeness, Trustworthiness) is the framework Google uses to evaluate whose content deserves to be cited. In an AI overview environment, these signals carry even more weight because AI systems are choosing a single source to synthesise, not ranking ten.

Generic, anonymous brand content struggles here. AI systems favour content from named authors with demonstrable credentials and real experience.

82% of AI citations come from earned media sources rather than self-published content Muck Rack. Third-party validation whether through industry publications, guest contributions, or expert commentary matters more than it ever has.

For B2B marketing teams, this means investing in visible human experts who write under their own names. Build author pages with real credentials. Link author profiles across platforms. Contribute to industry publications. The brands winning in AI search aren’t the ones with the biggest content libraries. They’re the ones with recognisable, credible people behind the content.

How to start optimising for AI overviews today

The question was how to optimise for AI overviews. The answer isn’t a technical trick. It’s a shift in how you build content.

• AI overviews now trigger on 48% of queries. In B2B tech, it’s 82%.
• Structure every section to answer one question directly. Answer first, detail second.
• Build content from real buyer questions found in forums, sales calls, and support tickets, not keyword tools alone.
• Invest in named author expertise. E-E-A-T is how AI decides who to cite.
• Getting cited isn’t just defensive. Cited brands earn 35% higher click-through rates.

The rules changed. But if you’ve been building genuine expertise and answering real questions all along, you’re already closer than you think.

Author Details:

Shweta GuptaMarketing Executive at Fifty-Five and Five, an AI-driven digital marketing agency in London. I work at the intersection of research, content, and AI-enabled marketing systems.