AEO Strategy: Getting Cited in

AEO Strategy:
Getting Cited in AI-Powered Research Tools

AI-powered research tools - Perplexity, ChatGPT, Claude, Gemini - are now a meaningful B2B discovery channel. A B2B cybersecurity software company implemented a structured AEO programme to earn citations in AI-generated responses to key decision-stage queries. Here's the strategy, implementation, and results.

Harmonic London

The situation

A B2B cybersecurity software company (Series B, £15M ARR, targeting IT and security teams at 200-2,000-employee UK firms) had strong SEO performance - 12,000 monthly organic sessions, multiple Page 1 rankings for commercial queries. But their head of growth noticed something troubling: when he searched for their primary solution category using AI tools (Perplexity, ChatGPT with browsing), competitors were regularly cited in AI-generated answers and his company wasn't. Given that 34% of their ICP was now using AI tools as a primary discovery channel (per their annual buyer survey), this was a pipeline visibility gap with material commercial impact.

The challenge

The challenge was understanding what drove AI citation. Unlike traditional SEO, where ranking factors are relatively well-documented, the signals that determine which brands AI systems cite are less transparent. Initial analysis identified three patterns in the AI responses where competitors were cited: (1) their competitors had more comprehensive FAQ-structured content with direct question-answer pairs; (2) competitor content used specific structured data (FAQPage schema, HowTo schema) that AI systems appeared to extract from; (3) competitor brands appeared in training-data-rich contexts - G2 reviews, industry publications, analyst reports - that AI systems weighted as credible sources.

Our approach

We designed a six-component AEO implementation programme.

Content restructuring: We audited the 40 highest-traffic pages and restructured content to lead every major section with a direct answer to an implied question - replacing prose introductions with question-format H2s followed by 2-3 sentence direct answers. This created the question-answer structure that AI systems extract from.

Schema implementation: We added FAQPage schema to all service and product pages, Article schema with explicit datePublished/dateModified to all blog content, HowTo schema to all process-based content, and Speakable schema to key FAQ sections. We updated Organisation schema on the homepage with detailed entity description.

Original research publication: We commissioned and published two original research reports with proprietary statistics - a "State of B2B Cybersecurity 2025" survey (250 respondents) and a breach cost analysis using client data. Original data with specific statistics is the content type most frequently cited by AI systems in generated responses.

Review platform investment: We ran a structured G2 review generation campaign - achieving 45 new G2 reviews in 90 days (from 12 to 57 total), with reviews specifically mentioning the use cases and solution categories we wanted AI systems to associate with our brand.

Industry publication presence: We placed 8 thought leadership articles in relevant B2B cybersecurity publications (SC Magazine, Infosecurity Magazine, IT Pro) - creating third-party mentions with named brand and solution category in high-trust domains that AI training data weights heavily.

Citation monitoring: We implemented a monitoring process using Perplexity, ChatGPT, and Claude to track citation frequency for our target queries monthly - creating a measurable baseline and tracking improvement over the programme period.

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Results

AI citations at start

The company had zero citations across Perplexity, ChatGPT, and Claude at programme start, with no structured AEO content or schema in place.

Citations at month 6

Citations achieved across target queries in Perplexity, ChatGPT, and Claude - from zero - within six months of programme implementation.

Query coverage

Target queries monitored across AI research tools throughout the programme, covering core ICP research topics in cybersecurity and compliance.

Key learnings: what this tells us about AEO in practice

The shift from zero AI citations to 34 AI Overview appearances and 20 Perplexity citations across the client's primary topic cluster took 14 months of focused AEO investment. The learnings from this programme are the most direct evidence we have of what actually moves the needle — versus what sounds plausible in theory.

Schema implementation produced measurable citation improvement within 6 weeks

The fastest visible impact came from adding FAQPage schema to the existing content library. Within 6 weeks of implementation, 8 pages that had zero AI Overview presence began appearing in AI Overview citations for relevant queries. The content had not changed — only the structured data signals. This is not a permanent shortcut; schema amplifies the authority of good content, it does not compensate for thin content. But for existing high-quality pages, schema implementation is the fastest ROI action available in AEO.

Topical cluster depth mattered more than individual page quality

Pages from the client's content cluster began appearing in AI citations only after the cluster reached a critical mass of 8-10 interlinked pieces covering the same topic area from different angles. Individual excellent pages were not being cited before the cluster was built. The lesson: AI systems infer authority from comprehensive topic coverage, not from individual page quality in isolation. A 15-page cluster on a topic area outperforms a single exceptional guide every time.

Original statistics were cited 4× more frequently than synthesised claims

The client's proprietary benchmark data — published in a research report — was cited in AI-generated responses 4× more frequently than editorial content making similar claims without original data. AI systems preferentially surface specific, attributed statistics because they can be cited with higher confidence. One compelling original statistic from a well-promoted research piece can generate more AI citation than months of editorial content production.

Question-structured headings directly improved extraction rates

When the editorial team rewrote section headings from declarative to interrogative format — "What is AEO?" rather than "AEO overview" — and ensured each section opened with a direct answer sentence, extraction by AI systems improved measurably. The heading-answer pair is the fundamental unit of AI content extraction. Content structured around this pair is significantly more likely to be cited than content structured around narrative flow.

AI citation did not fully correlate with organic rankings — and that distinction matters

Several pages appeared in AI Overview citations while ranking on page 2 of Google for the same queries. And some page 1 rankings did not produce AI citations. This confirms that AEO and SEO are related but distinct disciplines. Schema, question-answer structure, and topical authority affect AI citation probability independently of traditional ranking signals. Organisations that only optimise for organic rankings are missing a meaningful portion of their potential AI search visibility.

Applying these learnings to your programme

Start with schema implementation on existing high-quality pages: Before creating new content, implement FAQPage schema on your best existing pages. This is the fastest path to AI citation improvement for most organisations — it requires no new content and can be completed in 1-2 sprints.
Build clusters, not pages: If you have one strong pillar page on a topic but no surrounding cluster content, you are not yet building the topical authority signal that AI systems require. Map the questions in your topic area and fill the gaps systematically.
Commission original research this year: A single well-designed survey of your ICP, published with full methodology and promoted actively, generates citation value over 2-3 years. The ROI on original research is significantly higher than equivalent investment in editorial content.
Measure AI citation as a KPI alongside organic rankings: Set up a monthly tracking exercise — run your 20 target queries through Perplexity, ChatGPT, and Google AI Overviews and record whether your domain is cited. Track this trend over time. Improvement in AI citation rate is the leading indicator of growing authority in your topic area.

Programme summary

FAQPage schema is the single highest-impact AEO implementation: Adding FAQPage schema to service pages produced the fastest and most visible citation improvement. AI systems appear to specifically extract FAQ content as a format for answer generation.

Original statistics are disproportionately cited: Specific proprietary statistics from the research reports (e.g., '68% of UK mid-market firms experienced a successful phishing attack in 2024') appeared in AI-generated responses more frequently than general analytical content - AI systems cite specific, quotable data points.

G2 reviews function as AI training signals: The correlation between review volume growth and AI citation improvement suggests that review platform data is a meaningful input to AI system brand association for category queries - particularly 'best [solution] for [ICP]' queries.

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AEO Strategy:
Getting Cited in AI-Powered Research Tools

The situation

The challenge

Our approach

Results

Key learnings: what this tells us about AEO in practice

From 0 to Page 1: B2B SEO Transformation

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