AEO or GEO First? Industry Benchmarks and Priorities

Start with crawlable pages, sound SEO and clear answers to buyer questions, then expand the same program to strengthen public evidence and measure AI visibility. Industry benchmarks can guide which surfaces to monitor, but your own audience and baseline should decide the priorities. Our AEO vs GEO guide explains the terminology.

Key Takeaways

• Diagnose before allocating budget. Check crawlability, important buyer questions, entity accuracy and gaps in public evidence before purchasing separate AEO and GEO workstreams.
• Sequence work around the actual gap. Repair access and answer clarity first where those are weak; prioritize credible evidence where your pages are accessible but your claims lack support.
• Use industry benchmarks as context. A platform’s share of referral traffic in one study does not establish its value for every brand or measure all unclicked exposure.
• Earned media is part of the public-evidence layer. No platform publishes citation weights, but independent coverage, expert quotes and editorial mentions are the kind of verifiable public evidence AI and search systems may draw on.
• Measure AI citations, not just SERP rank. Track brand mentions inside ChatGPT, Perplexity, and Google AI Overview answers alongside traditional keyword positions, over a fixed prompt set, and treat results as observations rather than guarantees.

Which Should You Invest in First: AEO or GEO?

For most brands, start with the foundations both share, then widen into GEO. Fix crawlability, indexability and clear direct answers on your most important pages first, because Google says normal SEO best practices still apply to its AI features. Then build the public evidence GEO depends on: earned media, original data and consistent facts about your brand. Plan the program over a six-month minimum; meaningful movement commonly takes 6–12 months or longer, and no ranking or citation is guaranteed.

How Industry Niches Dictate The Right Strategy

Use industry benchmarks to decide which questions and search surfaces deserve investigation. They describe sampled traffic or search results; they do not prove which tactic caused a citation or determine the right budget for every company. Combine them with your own audience research, Search Console data and fixed-panel observations.

In Conductor’s 2026 report, Information Technology had the highest share of total website traffic coming from AI referrals, at 2.8%. This is a referral-traffic measure in the study’s enterprise-site sample, not the share of buyers using a particular engine or the rate at which brands were cited.

Consumer Staples also had a relatively high AI-referral share, at about 1.9%, yet only 6.82% of its sampled Google queries triggered AI Overviews. Those different measures illustrate why teams should inspect the specific surface and query set rather than assuming every industry follows the same pattern.

Use the industry figures to choose where to investigate. Referral share, citation frequency and AI Overview prevalence describe different parts of discovery and should remain separate in the decision.

In Conductor’s dataset, Health Care queries triggered AI Overviews 48.75% of the time. That sampled rate is useful context for monitoring, not a promise of coverage or an explanation of Google’s internal selection rules. Health-related content needs careful sourcing and qualified review.

The financial sector is also experiencing rapid transformation. Financial queries received AIO results 25.79% of the time, and Utilities saw AIO results generated for 25.4% of queries. For financial firms, issuers, and B2B SaaS platforms working with sensitive capital, Google applies higher standards of reliability, so strong editorial authority matters for "Your Money or Your Life" (YMYL) topics.

For organizations in these sectors, prioritize accurate expert-reviewed content and accessible supporting evidence. The benchmark figures can guide where to investigate visibility, but they do not establish that one discipline should receive a fixed share of the budget.

For brands operating in these volatile sectors, reviewing the full Conductor benchmark analysis provides immense tactical value. It allows marketing leaders to benchmark their own AI traffic against the industry average and diagnose whether their current visibility gaps are due to poor technical AEO or a lack of GEO-driven domain authority.

Understanding these industry benchmarks allows founders to allocate budgets intelligently. If your sector triggers AI Overviews on half of all queries, it is worth reviewing how SEO budget is split with AI visibility work; Google says SEO foundations still apply to AI features. The data is clear: the transition is happening at different speeds, but it is universally disrupting the discovery phase of the buyer journey.

AEO vs GEO vs SEO. What Each Optimizes For

Framework: SEO helps you get found, AEO helps you get quoted, and GEO covers the wider picture of how you are described. Modern brands run all three as one integrated program; see our Authority Buildout program for how the workflows overlap.

With this shift in user behavior underway, it is worth planning for these strategies now. Based on Hubspot's evolving marketing benchmarks and our own internal tracking, AI-assisted search is continuously displacing traditional query volume. Navigating this change requires a deeper understanding of SEO vs. GEO and committing to a comprehensive editorial approach.

Furthermore, early adoption provides a compounding advantage. Just as early SEO adopters benefited from accumulating historical domain authority, early movers in the AI search space are establishing baseline citation footprints. When your brand becomes the default trusted reference in a generative engine’s retrieval-augmented generation (RAG) system, it may reinforce visibility over time, although no platform documents how durable that is.

What is the meaning of AEO and GEO?

AEO means answer engine optimization. Smart Money Media treats it as the answer-focused part of broader GEO, or generative engine optimization. The GEO label comes from the 2023 Aggarwal et al. preprint; it is industry terminology rather than a Google product or requirement.

Use the distinction to assign work: make useful answers accessible, keep entity details accurate, substantiate claims and measure the results across relevant search surfaces.

Is AEO the same as GEO?

The scopes differ, but the work overlaps. Treat technical SEO, editorial work and public evidence as one program with shared ownership. For example, a software company can improve its product documentation while earning relevant independent reviews; neither action guarantees a citation.

For related buyer guidance, see when to hire an AEO agency.

What is the difference between AEO and GEO and LLM?

An LLM is a language model that generates responses. AEO and GEO describe work publishers and marketers undertake, not model architectures. An AI product may answer from its model or retrieve external sources depending on the product and mode. Publishers can improve accessible information; they cannot control model training or source selection.

In Conductor’s 2026 AEO / GEO Benchmarks Report, ChatGPT accounted for 87.4% of AI referral traffic across the ten industries analyzed. That is a finding about that dataset, not a universal market share or a measure of all AI-answer views.

Is GEO the same as AI SEO?

“AI SEO” can mean using AI tools for SEO or improving visibility in AI search. “GEO” and “LLM SEO” are often used for the latter. Ask a supplier to specify its deliverables, sources, measurement and limits rather than choosing on terminology alone. Our LLM SEO article provides related background.

Continue tracking ordinary search performance alongside AI mentions, citations and identifiable referrals. Useful original research, accurate facts and clear sourcing support both; no label creates an exception to normal quality or spam policies.

The Mechanics Behind Answer Engine Optimization

Answer Engine Optimization requires technical precision. Because AEO is fundamentally about extraction, the primary goal is removing friction for automated crawlers. If crawlers struggle to access or understand a page, it is less likely to be used as a source.

A useful supporting mechanism in AEO is structured data, particularly Schema markup. Implementing standard Schema, such as FAQPage, Article, Organization, and Person schemas, provides a machine-readable layer of meaning over your human-readable text. It helps machines interpret the page. Google states that no special schema is required to appear in its AI features (see Google Search Central: AI features and your website), so treat schema as support, not a prerequisite.

Beyond standard Schema, page architecture plays a massive role. Content must follow a strict, logical hierarchy. Question-led H2s and H3s that mirror how buyers ask, followed by a clear, factual answer, help readers and machines alike. Google's guidance says there is no need to rewrite or chunk pages specifically for AI features.

AEO also leans heavily on entity optimization. Search engines use Knowledge Graphs to map the relationships between different concepts, people, and brands. By explicitly linking your content to known Wikipedia pages, authoritative government databases, or industry glossaries, you help the AI contextualize your information and make your information easier to corroborate.

Some sites also publish an optional llms.txt file, a proposed Markdown map of their most useful pages. It does not replace robots.txt, Google does not require it for AI features, and no major platform documents it as a ranking or citation signal. See the llms.txt guide for what is documented.

Technical performance remains foundational. Core Web Vitals, page speed, and mobile responsiveness are prerequisite signals. If an Answer Engine is attempting to serve a voice query to a mobile device on a slow network, a fast, accessible page is a better experience. Technical debt can hold back AEO visibility.

Formatting density is the final mechanical lever. Paragraphs should be short. Bulleted lists, numbered instructions, and HTML tables help where the content genuinely calls for them. AI systems can read HTML tables to present data comparisons to users. If you have proprietary data, do not trap it in an image or a PDF; render it in clean HTML.

Ultimately, AEO turns your website into an API for search systems. By standardizing the way your data is presented, you make it easier for an AI system to use your page when it needs a fact, although no inclusion is guaranteed.

Generative Engine Optimization Core Principles

While AEO handles the technical formatting on your owned domain, Generative Engine Optimization requires a broader, externally focused strategy. GEO is about proving your brand deserves to be cited when AI models generate complex, localized, or highly strategic answers. The core principle of GEO is E-E-A-T: Experience, Expertise, Authoritativeness, and Trust.

Independent, relevant sources can corroborate a brand’s claims. Review the public evidence available to retrieval systems and correct factual inconsistencies, while recognizing that platforms do not publish a universal source-weighting formula or guarantee that a particular source will be selected.

This is why digital PR matters for GEO: earned editorial placements create a public paper trail that LLMs may draw on. We often advise clients evaluating a performance-based AEO service that sustained off-site editorial authority is a core part of the public evidence behind generative citations, not a guarantee of them.

Another core principle of GEO is original research. LLMs frequently draw on new data. They are trained on historical information but rely on real-time web browsing to answer current questions. By publishing proprietary surveys, industry reports, and validated data sets, you become the original source of facts an AI may need, which makes a citation more plausible.

Consistent mentions matter in GEO. It is not enough to be mentioned once. Your brand, your executives, and your proprietary frameworks need to be mentioned consistently across multiple authoritative platforms. A consistent web of associations makes it easier for systems to connect your brand with your topic.

Reputation work should address inaccurate information and legitimate customer concerns. Publish clear evidence and accurate corrections where appropriate. An agency cannot control how a model summarizes a brand or guarantee that new coverage will replace older information.

This is where executive thought leadership shines. Ghostwritten, generic blog posts offer zero value to a generative engine. High-contrast opinions, counter-narratives, and deeply analytical industry observations provide the kind of unique context that comprehensive summaries can use.

Brands successfully implementing GEO treat their operations as a digital footprint campaign, not merely a link-building scheme. They aim to be unavoidable in high-trust digital spaces. By steadily increasing their presence in credible, authoritative media, they build evidence that tends to hold up better through algorithm shifts.

Foundational Elements For Zero-Click Visibility

The era of measuring digital success purely by click-through rates is changing. Zero-click visibility, where users receive the entirety of their answer directly on the search engine or AI chat interface, is increasingly common. Brands that optimize solely for traffic are optimizing for a metric that algorithms are actively trying to eliminate.

To win in a zero-click environment, brands must optimize for brand impressions and mental availability. If an AI Overview explicitly cites your company as the leading provider of enterprise cybersecurity, the user receives that trust signal instantly. They may not click a link to your website immediately, but the brand has still been seen.

Building this foundation requires auditing how AI views your brand today. Answering this fundamental question is why we direct executives toward frameworks for winning zero-click searches. You must identify where the LLM's understanding of your organization is fragmented, outdated, or entirely absent, and proactively feed it corrective information.

A major element of zero-click foundations is managing third-party directories, review sites, and open-source wikis. Generative answers often cite sites like G2, Capterra, Trustpilot and Wikipedia. Maintaining robust, accurate profiles on these widely cited aggregators ensures that when an AI system hallucinates or attempts to verify features, it references accurate information.

Knowledge Graph presence also helps. Google's Knowledge Graph is essentially a massive database of verified facts and entities. If your brand is not recognized as a distinct entity with a populated Knowledge Panel, generative engines will struggle to differentiate you from generic terminology. Claiming profiles and mapping executive entities helps.

Executive branding matters in a zero-click world. People trust people, and generative engines trust established experts. A founder who regularly publishes insightful commentary on LinkedIn, is quoted in major business journals, and speaks at industry conferences creates a distinct entity trail. These executive credentials become public evidence that may support the overall brand.

Furthermore, brands must embrace multi-format content. Zero-click search is not restricted to text. Generative engines increasingly pull in YouTube timestamps, podcast transcripts, and authoritative infographics. Establishing a footprint across video and audio channels gives LLMs more context to draw on, broadening your discoverability surface area.

Shifting focus from "clicks" to "citations" is difficult for traditional marketing teams. It requires updating reporting dashboards to measure share of voice inside AI responses. However, brands that successfully build a foundation for zero-click visibility are better placed to be seen as an authority, and the visitors who do engage often arrive better informed.

Structuring Proprietary Data For Generative Citations

One of the most effective ways to give AI models a reason to cite your brand is through the creation and distribution of original, proprietary data. Generative models operate on a diet of information; they do not create facts; they only synthesize them. If you supply the facts that the industry relies on, you become a more plausible source, though recommendation is never guaranteed.

Proprietary data acts as an anchor against AI hallucinations. When ChatGPT or Perplexity is asked a statistical question regarding market trends, it seeks verifiable numbers. If your brand publishes an annual "State of the Industry" report, heavily fortified with primary research and rigorous methodology, you become a primary source.

The format of this data is critical. Data is easier to use when it is structured for both answer clarity and broader source context: semantic HTML, clear labels and question-led summaries help search engines digest it without confusion.

Insights published by Conductor noted that AI referral traffic is growing. With ChatGPT accounting for 87.4% of AI referral traffic, it makes sense to publish in formats that are easy to read and verify: clear definitions, well-formatted tables, and unambiguous statistical claims supported by verifiable external links. OpenAI does not publish a preferred citation format.

Gating content is a major strategic error in the AI era. If your best research is hidden behind endless lead-capture forms or trapped inside un-crawlable PDF documents, the LLM cannot read it. To win GEO, you must make high-value insights freely accessible. Ungating research allows AI crawlers to ingest the data and attribute it directly to your domain.

The distribution of this data is where PR merges with SEO. Simply publishing the data on your blog is insufficient. The data must be pushed into the broader internet ecosystem. When major news outlets, industry blogs, and competitors cite your statistics and link back to your original report, you create the independent corroboration that strengthens E-E-A-T.

When drafting content, utilize explicit attribution phrasing. Sentences like, "According to [Brand Name]'s analysis of global supply chains..." make it clearer that the insight belongs to your entity. Consistent linguistic patterns assist natural language processors in accurately parsing who generated the information.

Ultimately, data is the currency of the generative web. Brands that invest in original research, ungate their findings, and format the data using AEO best practices will be better placed in their niche. They transition from merely participating in the conversation to actively providing the raw material that the AI engines use to generate answers.

For a deeper dive, see our AEO vs GEO Guide: end-to-end frameworks and actionable steps.

For a deeper dive, see our llms.txt Guide: end-to-end frameworks and actionable steps.

Measuring Success In The New Search Era

As the mechanics of search fundamentally change, the KPIs used to define success must evolve in parallel. Tracking keyword positions in a vacuum is no longer a viable benchmark for visibility. Executives must implement measurement structures that capture AI citation rates, sentiment analysis, and the actual growth of AI-driven brand referrals.

The shift is measurable. According to Conductor’s 2026 AEO / GEO Benchmarks Report, AI referral traffic grew at approximately 1% month-over-month on average across analyzed industries. While AI referrals currently represent a smaller fraction of massive site traffic, this steady compounding growth curve represents the future baseline for digital acquisition.

To accurately track this, organizations are turning to specialized AI visibility audit tools that monitor how often a brand is mentioned in ChatGPT, Perplexity, and Google AIO responses. These tools simulate target queries and report back on citation frequency, helping marketers see where their brand is dropping out of the narrative. For the full KPI set, see the GEO and AI search KPIs guide.

Furthermore, setting up advanced web analytics to capture referral strings from generative engines is crucial. While some AI platforms obscure referral data, tools are increasingly getting better at isolating traffic originating from specific LLMs. Understanding which platform drives the most engaged users allows for tighter AEO and GEO optimizations.

Tracking the inclusion of brand entities alongside non-branded keywords is another vital metric. If a user searches for a generic service ("enterprise risk management software") and the AI Overview naturally recommends your brand within the summary, that is a strong GEO signal. Measuring share of voice in these synthetic summaries provides true ROI context.

Ultimately, measuring success requires patience. Plan over a six-month minimum; meaningful movement commonly takes 6–12 months or longer, depending on your starting point, execution and competition, and no outcome is guaranteed. As the month-over-month growth of AI referrals continues, brands that adopt these customized KPIs will be better able to show the value of strategic editorial positioning.

Moving Your Brand Authority Strategy Forward

The overlap between AEO and GEO calls for coordinated digital communications. You cannot simply optimize technical tags and hope for results, just as you cannot blast irrelevant news wires and expect to earn the trust of complex large language models. Technical precision and verifiable editorial authority work best together.

Navigating this complex hybrid requires specialized expertise. Brands that attempt to manage answer-clarity work in a silo while deploying a disconnected PR agency for basic media outreach often struggle to keep their entity signals consistent. A cohesive, well-evidenced brand narrative is easier for any system to understand.

Smart Money Media specializes in this exact intersection. Our strategic editorial positioning strategies are built specifically for the era of generative search. We focus on earned editorial coverage, clear entity signals and strong on-site architecture, the public-evidence layer AI and search systems may draw on. We do not promise AI Overview inclusion or citations.

If your brand is currently invisible in ChatGPT or conspicuously absent from Google's generated industry summaries, it is worth understanding why. Early movers are building public evidence that may be harder to catch up with later.

Zero-click answers that name competitors instead of you are a real cost. Aligning your strategy to cover both the answer-clarity work of AEO and the broader authority-building of GEO is one of the more important marketing decisions to make now.

Begin by analyzing how your baseline visibility stacks up right now. Secure a comprehensive view of your brand’s digital footprint, eliminate technical friction that halts AEO, and launch a focused program to build the off-site credibility that GEO depends on.

What Kinds of AEO and GEO Tools Exist?

The AEO and GEO tooling market is fragmenting fast. Most platforms specialize in one surface (Google AI Overviews or ChatGPT/Perplexity citations), and no single tool covers the full stack of measurement, on-page optimization, and off-site authority building. Here are the tool categories teams typically evaluate, with example products (capabilities change often; verify with each vendor):

The pattern: measurement tools tell you the score; they don't change it. If you've validated a visibility gap and want help with the underlying work (site structure, clear answers and earned media that creates public evidence), that is the work we do, with no citation or ranking guarantee. Start with a free AI visibility audit to see your current baseline across major AI answer engines.

Work with our team

If you are ready to operationalize this beyond a checklist, our AEO agency engagement focuses on answer presence across the major answer engines, and our GEO agency engagement covers the broader generative-search program, including Google AI Overviews and AI Mode. Most brands run both together because the underlying work overlaps.

Why AEO and GEO Now Work as One Program

The practical line between AEO and GEO has blurred, and a strict "AEO for Google, GEO for ChatGPT" split is misleading. Google AI Overviews and AI Mode draw on the same kinds of sources as other generative answers, ChatGPT search relies on its own crawlers and web retrieval (see OpenAI's crawler documentation), and Perplexity documents its own crawler (Perplexity crawler documentation). None of these platforms publishes how it weights sources, so treat any claim of platform-specific weighting with caution.

The strategic implication: run AEO and GEO as one integrated program with shared infrastructure (the same site architecture, the same content clusters and the same earned-media calendar) and differentiate mainly at the reporting layer. Track answer presence and accuracy across a fixed prompt set alongside broader citation share and AI traffic, but resource the underlying work as one team, one budget, one editorial calendar. Brands running AEO and GEO as siloed workstreams often pay twice for overlapping work. Our AEO engagement, GEO engagement, and combined Authority Buildout Program reflect this: one underlying playbook with two reporting views.

What hasn't changed: SEO foundations still apply, credible earned media remains an important part of public evidence, and entity clarity (Wikidata, Knowledge Panel, consistent Organization schema) helps systems understand who you are. The shift is about integration, not replacement.