Buyers now ask an AI system a question and get an answer, often with a shortlist attached, before they ever visit a vendor's website. If your company isn't part of that answer, you may never learn that you were in the running.
That is the practical meaning of two terms you'll see throughout this paper. Answer engine optimization (AEO) is the work of structuring your content so search features and assistants can pull a clear, correct answer from it. Generative engine optimization (GEO) is the work of earning citations and recommendations inside responses from tools like ChatGPT, Gemini, Claude, and Perplexity. They overlap heavily, and most teams will run them as one program.
The evidence points in the same direction from several angles. Pew Research found that people click far less often when Google shows an AI summary. Bain estimates that around 60% of searches now end without a click. McKinsey reports that half of consumers use AI-powered search, and that brand-owned websites make up only 5% to 10% of the sources those systems reference. Meanwhile, 6sense finds that the winning vendor is already on the buyer's shortlist 95% of the time before sales gets involved.
Put together, that changes what go-to-market teams are responsible for. The job is no longer only to rank a page and capture the click. It is to become a source that machines trust and a name that buyers already recognize when the shortlist forms. This paper covers what has changed, what the data says, where the traditional playbook breaks down, and the nine action items we recommend, which fall into five priorities:
Audit how AI systems describe your brand today.
Restructure your content so answers are easy to extract and cite.
Build a presence on the third-party sources AI systems actually read.
Rework your measurement so it captures influence, not just clicks.
Make GEO a shared responsibility across marketing, sales, and product, with a named owner.
What actually changed
For about twenty years, search worked like a directory. A buyer typed a query, scanned ten links, and clicked the ones that looked useful. Marketing teams competed for those clicks, and the whole discipline of SEO grew around winning them.
Answer engines work differently. The system reads many sources, writes a response, and shows a handful of citations. Sometimes the buyer never leaves the response. Sometimes they click a citation or two. Either way, the ranking of your page matters less than whether your content was used to build the answer.
Two terms describe the response to this shift.
AEO (answer engine optimization) focuses on making your content the direct answer: clear questions and concise responses, structured markup, and pages that are easy for a system to parse. It grew out of featured snippets and voice search and now extends to AI summaries in traditional search results.
GEO (generative engine optimization) focuses on how large language model products describe and recommend you. It covers what those systems say about your brand, which sources they lean on when they say it, and how often you appear when someone asks for a recommendation in your category.
SEO doesn't go away. Pages still need to be crawlable, fast, and relevant, and traditional search still drives a large share of traffic. But it is now the foundation rather than the whole building.
The data behind the shift
No single study settles this, and the numbers vary with method and audience. Read together, though, they tell a consistent story: fewer clicks, more answers, and a buying process that gets decided earlier than most teams assume.
Pew Research Center: Users clicked a traditional result in 8% of visits when an AI summary appeared, versus 15% without one. Only 1% of visits included a click on a link inside the summary.
Bain & Company: About 80% of consumers rely on AI-written results for at least 40% of their searches. Around 60% of searches end without a click. Organic traffic is down an estimated 15% to 25%.
Ahrefs: Position-one click-through rate was 34.5% lower on informational keywords that triggered an AI Overview.
McKinsey: 50% of consumers use AI-powered search. 44% of those users call it their primary source of information. About $750 billion in U.S. revenue will flow through it by 2028.
Gartner: 45% of B2B buyers used generative AI during a recent purchase, mainly to research vendors and products. 69% prefer to validate AI-generated insights with a sales rep.
6sense: 94% of B2B buyers use LLMs in their process. The winning vendor was already on the buyer's Day One shortlist 95% of the time.
Adobe Analytics: AI-referred visits to U.S. retail sites grew 138% year over year in May 2026 and converted 54% better than non-AI traffic.
Two caveats are worth stating plainly. First, the click studies measure Google's AI summaries, not every AI product, and the Pew sample is U.S. adults from a single month. Second, forecasts about the size of the drop vary widely. Gartner predicted in 2024 that traditional search engine volume would fall 25% by 2026, while McKinsey puts 20% to 50% of traditional search traffic at risk. Whichever estimate you prefer, the direction is the same, and the traffic that still arrives from AI sources appears to be high quality: Adobe's retail data shows those visitors spending 53% more time on site.
The Gartner and 6sense findings matter most for B2B teams, because they show two things at once. Buyers lean on AI to build their list, and they still want a person to confirm what the AI told them. Both moments are opportunities, but the first one happens where you can't see it.
Where the traditional playbook breaks
Most go-to-market plans were built around a simple sequence: attract traffic, capture the lead, nurture, hand off to sales. Answer engines put pressure on several parts of that sequence at once.
The top of the funnel moves off your site. A buyer who gets a useful answer from an assistant has less reason to read your blog post. Informational content that used to drive traffic now often gets summarized without a visit. You can still win the citation, but the click may not follow.
The shortlist forms before you know about it. 6sense's research found that buyers make first contact with a vendor when they are, on average, 61% of the way through their process, and that the vendor contacted first wins about 80% of the time. If AI tools help shape the Day One list, then being named in those answers is now a pipeline input, even though no form fill records it.
Your website is one voice among many. McKinsey found that brand-owned sites account for only 5% to 10% of the sources AI-powered search references. In categories like consumer goods and financial services, more than 65% of sources come from publishers, user-generated content, and affiliates. What the wider internet says about you carries more weight than what you say about yourself.
Buying groups are large, and every member is asking. Forrester's 2026 research describes a typical purchase involving 13 internal stakeholders and 9 external influencers. Each of them may ask an assistant a slightly different question. A finance lead asks about cost and risk, an engineer asks about integration, and an executive asks who the category leaders are. Your content has to hold up for all of them.
Attribution gets murkier. Someone who hears your name in a ChatGPT answer and then types your URL shows up as direct traffic. Last-click reporting will understate the channel, and most teams aren't yet measuring it at all. McKinsey's CMO survey found that only 16% of brands systematically track AI search performance.
Accuracy becomes a brand risk. Buyers know AI can be wrong, which is a big reason 69% of them check AI-generated insights with a sales rep (Gartner). If a model describes your product wrongly or lists an outdated price, buyers will carry that into the conversation with your sales team.
What AI systems actually cite
If brand websites are a small slice of what gets cited, the next question is what fills the rest. Two studies give useful answers.
Muck Rack's May 2026 analysis of more than 25 million links cited in responses from ChatGPT, Claude, and Gemini across 17 industries found that 84% came from earned media, meaning coverage and content that a third party published. Paid or advertorial content made up 0.3%. Journalism alone accounted for 27%. The share has stayed between 82% and 89% across three editions of the study going back to July 2025. The platforms also differ: ChatGPT cited a source in 96% of responses and most often pointed to Wikipedia, while Claude cited sources in 55% of responses but used more of them when it did.
The second is academic. Researchers from Princeton, IIT Delhi, and other institutions tested nine content tactics on a benchmark of 10,000 queries and published the results at KDD 2024. Adding quotations improved source visibility by 41%, adding statistics by 40%, and citing sources by 30%, measured with a position-adjusted word count. Keyword stuffing, a habit carried over from older SEO, made things worse. The study used a controlled benchmark rather than live products, so treat the percentages as directional. The pattern is still useful: material that reads like a well-sourced reference gets used more.
The practical reading is that AI systems behave like a cautious researcher. They prefer sources that are specific, corroborated, and published by someone other than the company being described.
Key action items
These are the moves we'd put in front of a leadership team, in the order we'd tackle them.
Find out how AI describes you today. Write 30 to 50 questions a real buyer would ask, covering category discovery, comparisons, pricing, and objections. Run them across ChatGPT, Gemini, Perplexity, Claude, and Google's AI features. Record whether you appear, how you're described, which competitors show up, and which sources are cited. Repeat monthly. McKinsey found only 16% of brands track this systematically, so even a basic baseline puts you ahead.
Fix what the models get wrong. Look for outdated pricing, discontinued products, mislabeled categories, and missing use cases. Trace each error to its source, whether that's an old press release, a directory listing, or a thin page on your own site, and correct it at the source.
Write for extraction, not just for reading. Lead each page with the direct answer, then support it. Use question-based headings, short definitions, comparison tables, and clear FAQs. Add schema markup where it applies. Follow the Princeton findings and include original data, named sources, and expert quotes rather than generic claims.
Build third-party presence on purpose. With 84% of citations coming from earned media, your PR, analyst relations, partner, and community work are now part of your discoverability. Prioritize coverage in the publications your category's AI answers already cite, keep review-site and directory profiles complete, and contribute expert commentary where your buyers ask questions, including forums like Reddit and industry communities.
Publish proprietary information only you have. Original research, benchmarks, customer outcomes, and specific pricing guidance give a model something it can't find elsewhere. Gartner's own advice on this topic is to produce unique content that is useful to customers and prospects, and to show expertise, experience, authority, and trustworthiness.
Keep the technical basics clean. Make sure the crawlers you want can reach your key pages, that important content isn't hidden behind scripts or logins, and that pages load quickly. Decide deliberately which AI crawlers to allow, since that choice affects whether your content can be used at all. Keep publication and update dates accurate.
Equip sales for the validation step. Since 69% of buyers check AI-generated insights with a rep, sellers need to know what AI tools are telling prospects. Give them a short brief on how the company shows up in common queries, the errors buyers may have absorbed, and proof points that confirm or correct them.
Rework measurement and goals. Add AI visibility metrics next to traffic and pipeline (see the next section). Ask new leads how they first heard of you, in their own words, and read the answers. Expect some influence to arrive labeled as direct traffic.
Assign an owner and a cadence. GEO touches content, PR, web, product marketing, and sales enablement. Without one accountable person and a monthly review, it tends to fall between teams. Treat it as a standing program, since model behavior and the sources they favor keep shifting.
Measuring what matters
Clicks and rankings still count, but they no longer tell the whole story. A useful scorecard adds a few measures of visibility and influence, reviewed on the same schedule as your other marketing metrics.
AI share of voice: How often you're mentioned versus competitors across your test questions; Run the same question set monthly on each platform and log mentions.
Citation rate: How often your own pages are cited as a source; Note cited URLs in each response; some analytics and SEO platforms now automate this.
Sentiment and accuracy: Whether the description of your brand is positive and correct; Score each response against a short checklist of facts.
Source mix: Which third-party sites AI systems rely on in your category; Tally cited domains, then plan outreach around the top ones.
AI referral traffic: Visits arriving from AI products; Segment referrers in your analytics, and watch conversion separately from other channels
Branded and direct lift: Whether more people search for you by name; Track branded search volume and direct visits over time.
Self-reported attribution: How new leads say they found you; Add an open-text field to forms and review it in sales calls
Two cautions apply. AI answers vary from one run to the next, so look at trends across many prompts rather than any single response. And the referral numbers will undercount the real effect, since a buyer who learns your name from an assistant often arrives later by another route.
A 90-day starting plan
You don't need a large budget to begin. You need a baseline, a short list of fixes, and a habit of checking results.
Days 1 to 30 - Baseline and diagnosis: A question set of 30 to 50 buyer prompts, a first scorecard across platforms, a list of factual errors with their sources, and a map of the third-party sites cited in your category
Days 31 to 60 - Fix and restructure: Corrected listings and pages, your top 10 to 15 pages rewritten for direct answers, schema and crawler access reviewed, and one original piece of research or data in production
Days 61 to 90 - Extend and operationalize: A targeted outreach plan for the sources AI systems cite most, a sales enablement brief, updated lead-source questions on forms, a named owner, and a monthly review on the calendar
At the end of the 90 days, rerun the baseline. The comparison will show you which changes moved your visibility and where to invest next.
How Gallop GTM can help
Most companies we talk with agree this shift is real. The harder part is knowing where to begin, who should own it, and how to connect it to pipeline rather than treating it as another SEO checklist.
Gallop GTM helps teams build and run a go-to-market strategy that accounts for how buyers now find and evaluate companies. We can run the baseline audit, identify the errors and gaps holding your brand back, restructure your content, plan the third-party presence that AI systems draw on, and set up measurement your leadership team can act on. We can also work alongside your existing team and train them to carry it forward.
If you'd like to see how your brand shows up in AI answers today, get in touch with Gallop GTM and we'll walk you through what we find.
Sources
Pew Research Center, Google users are less likely to click on links when an AI summary appears in the results, July 2025
Bain & Company, Consumer reliance on AI search results signals new era of marketing, February 2025
McKinsey & Company, New front door to the internet: Winning in the age of AI search
Gartner, Search engine volume will drop 25% by 2026, February 2024
Gartner, 69% of B2B buyers turn to sales reps to validate AI-generated insights, May 2026
6sense, 2025 B2B Buyer Experience Report
Forrester, 2026 State of Business Buying
Muck Rack, What Is AI Reading? May 2026
Aggarwal et al., GEO: Generative Engine Optimization, KDD 2024
Digital Commerce 360, Adobe: AI-referred traffic to retail sites doubles in a year, June 2026 (reporting Adobe Analytics data)