FROM GEO TO “GEN-AI ENGINE OPTIMIZATION”: USING AD SPY DATA TO INFLUENCE WHAT AI RECOMMENDS, NOT JUST WHAT GOOGLE RANKS

Most teams are treating GEO the way they treated “Universal Search” or “voice search” on day one: as a reporting layer. A new column in the dashboard. A few slides with AI Overview impressions. A quarterly “impact of AI on SEO” update for leadership. Then it’s back to business as usual.

That mindset is exactly what’s going to cost you growth.

Across our own clients, roughly 80% of the time, what’s good for SEO is good for GEO. The initiatives that have moved the needle fastest were never “SEO projects” or “GEO projects” in isolation; they were growth programs where organic search and generative engines were treated as a single, integrated surface. GEO wasn’t a new discipline bolted on the side. It was the logical extension of well-run SEO.

Google is quietly telling you the same thing. In its guidance on AI search experiences, Google is explicit that generative features draw from the same index and same ranking systems as traditional results, and that AI Overviews still depend on fundamentals like crawlability, site structure, mobile usability, page speed, and internal linking, exactly as they do for classic search visibility, as the team at WordStream explains. For a Phoenix plumbing company with a sluggish mobile site, that’s not two technical problems; it’s the same problem manifesting in two interfaces.

Underneath the hype, three themes keep resurfacing in Google’s messaging: foundational SEO, demonstrable expertise, and context-specific trust (especially local trust). None of that is new. It’s a clarification of the existing playbook now applied to AI-powered surfaces. Your historical investment in content, authority, and technical soundness is more valuable than the “AI optimization” gold rush wants you to believe.

Data from the search side backs this up. When AI Overviews launched, some pages kept ranking and kept impressions but lost clicks—what one Ahrefs analysis called “pages whose rankings/impressions held steady while clicks fell away.” That pattern doesn’t signal a ranking failure; it signals an attribution shift. The answer box is doing the consuming. Those pages are not dead assets; they’re prime candidates to be reworked into the kind of material AI engines want to cite, or repositioned around questions that can’t be fully resolved in a single synthesized paragraph.

This is where most organizations miss the plot. They either:

• Treat GEO as “just SEO” and bury it inside existing budgets and processes, or
• Treat GEO as magic—some secret checklist of “AI triggers” that lives in a completely separate universe from search.

Both are losing strategies.

As one contributor at MarTech argued, insisting that “it’s just SEO” is a kind of commercial self-sabotage. If you refuse to name GEO as a distinct category of work, it never gets a real brief, budget, team, process, or target. But the answer is not to discard SEO; it’s to recognize that GEO is SEO growing up into the rest of marketing—where the objective isn’t merely to rank, but to be recommended, referenced, and chosen inside AI-led journeys.

On the other side, the “AI slop” industry is discovering the hard way that you cannot checklist your way into citations. Platforms and algorithms are already dampening undifferentiated machine-written output: YouTube and Pinterest have introduced measures that reduce the reach and monetization of AI-only content, and SEO firm Graphite has observed that content farms are seeing their generic AI posts picked up less frequently in both search and AI chat responses, making the economics far less attractive, as Jeff Bullas notes. Generative engines are disproportionately rewarding content with original perspective, specific expertise, and a recognisable human voice—the opposite of “prompt + publish.”

The strategic shift, then, is not “add GEO reporting” or “buy GEO tools.” It’s to reframe organic from “how do we rank?” to “how do we become the most quotable, linkable, and reference-worthy source in our space—for humans and for machines?” That means:

• Using SEO fundamentals to make your content discoverable and technically accessible.
• Using GEO thinking to make that same content the obvious choice for AI systems to summarize, cite, and recommend.

When you approach GEO and SEO as one growth strategy aimed at being the canonical answer—wherever and however the answer is served—you stop chasing AI as a threat and start using it as an amplification layer for the work you’re already doing well.

If you want to influence what AI engines recommend, you first have to understand how they decide whom to trust.

By 2026, three layers are doing most of the work: the old-school search index, the LLM’s own trust graph, and a fast-expanding mesh of real‑world signals like purchases, reviews, and creator mentions. GEO sits on top of all three.

First, AI engines still lean on classic search systems far more than the hype suggests. Google has been unusually explicit that its generative experiences are “rooted in our core Search ranking and quality systems,” meaning AI Overviews and AI Mode are powered by the same index, ranking models, and crawl infrastructure that drive blue links. As one breakdown of Google’s GEO guidelines explains, Gemini‑powered search uses query “fan‑outs” to fetch additional results, but those results still come from the traditional index and its existing relevance and quality signals, not some separate “AI index” living off in the cloud. In other words, if you’re invisible to search, you’re invisible to AI.

Second, AI engines have started building an opinionated trust graph on top of that index. They don’t just ask, “Which pages rank?” but, “Which entities show consistent, demonstrable expertise across many related questions?” Google’s public guidance calls out exactly that: foundational SEO plus “demonstrable expertise” and “trust and local relevance” as the levers that move both organic results and AI Overviews. A fast, crawlable site with clear topical focus and rich internal linking still matters, but those basics now feed a higher‑order model of who is credible enough to summarize, compare, or recommend.

That’s why “who” is speaking has become nearly as important as “what” is on the page. Generative engines are constantly looking for stable, repeatable authorities they can anchor answers to. Sometimes those authorities are brands; just as often, they’re humans: doctors, mechanics, solo consultants, or niche creators. As one AdExchanger report on GEO adoption noted, marketers are already watching which creators and publishers the major LLMs repeatedly cite, because those citations quietly define which voices the engine sees as trusted on a topic.

Third, AI engines blend this authority layer with behavioral and outcome data—especially around commerce. Generative systems aren’t just answering questions; they’re shepherding purchase decisions. In a recent Semrush study on AI‑assisted buying, researchers found that AI chatbots talked nearly 60% of users out of making a purchase by surfacing negative reviews, cheaper alternatives, or more convincing competitors. For the models, those outcomes are feedback: they learn which sources and angles actually resolve user intent, not just which pages are well optimized.

Put together, this is how a “trusted source” is born in 2026:

• The brand or individual is technically discoverable and well structured in the search index, so retrieval is easy.
• Their content clusters tightly around specific problems or use cases, giving LLMs dense material to synthesize and quote.
• Other authoritative entities—publishers, experts, and now AI systems themselves—link back and reference them repeatedly.
• When the engine tests their recommendations in the wild, users stick with those answers, click through, and often convert.

The result is that GEO and SEO are no longer separable checklists; they’re two faces of the same trust problem. As one analysis of Google’s AI search guidelines put it, “about 80% of the time, what’s good for SEO is good for GEO,” because both are feeding the same underlying systems.

The remaining 20%—the part most teams are missing—lives in this trust graph: understanding which entities your buyers see in AI answers today, which sources those answers lean on, and where you need to insert your brand into that ecosystem so that the next time an LLM fans out a query, you’re already in the short list of voices it knows it can trust.

Marketers talk about “training the AI” as if it only happens in research labs. In reality, you’re training it every time you light up a paid campaign.

Most GEO dashboards treat ad spend as background noise: a channel you measure separately, maybe use for remarketing, but not a signal that shapes what AI engines recommend. That’s a miss. The same impressions, clicks, and conversions you buy to influence humans are now continuously ingested as grounding data and feedback for AI systems deciding which brands to trust.

The shift started when search and media platforms wired their generative layers directly into ad-tech plumbing. Warner Bros. Discovery’s move to a cloud‑native stack with “advanced audience forecasting, and enhanced measurement and attribution,” as described in an AWS‑backed overview, is a blueprint: unify planning, targeting, and optimization so that every impression feeds a single decision engine. That “stewardship” layer isn’t just automating bids; it’s creating a constantly updated graph of which audience–message–publisher combinations reliably move people to act.

LLMs don’t see your media plan, but they absolutely see the exhaust. They read the articles, watch the videos, and crawl the product pages and review sites that your dollars are pushing into circulation. When your paid campaigns drive a surge of mentions, co‑citations, and structured feedback (reviews, ratings, testimonials), that becomes part of the corpus AI engines use to ground their answers.

This is where GEO and performance marketing quietly converge. GEO teams, as one MarTech analysis put it, are already tracking which sources AI engines cite and how those answers change week to week. Performance teams, meanwhile, are running thousands of micro‑experiments across audiences, creatives, and offers. AI engines can’t see your ROAS report, but they can see where buyers land, what content they consume, how they talk about you afterward, and which creators and publishers are consistently associated with high‑intent activity.

Consider three concrete pathways where paid becomes training data:

1. Creator and publisher signals. When you sponsor a creator or outlet, you’re not just buying reach, you’re underwriting content that AI crawlers later treat as evidence. The eczema creator whose interview on HealthCentral led ChatGPT to surface him as a recommended partner for skincare brands is a perfect illustration, as reported in AdExchanger’s daily roundup. Brand deals made him more visible; that visibility fed into the LLM’s graph of “people associated with eczema advice,” which in turn created more deals. Paid sparked an organic AI flywheel.

2. On‑site behavior and outcomes. When GEO practitioners talk about knowing “what AI chatbots currently say” and then “fix[ing] the gaps in the sources those answers draw from,” they’re describing a loop that’s increasingly informed by performance data, not just rankings. Semrush’s research on how often AI chatbots talk users out of buying emphasizes that a robust GEO strategy must track your share of AI recommendations and align it with actual conversion behavior across millions of prompts, using tools like their AI Visibility Toolkit. The more your paid traffic produces credible, positive behavioral signals—low bounce, deep engagement, repeat visits—the stronger the case that your brand is a safe recommendation.

3. Query and intent mapping. Platforms are now fan‑ing out a user’s initial question into a cluster of related needs and tasks, then using both organic and paid interactions to refine that map. Google’s own GEO guidance, as unpacked on the Moz blog, highlights “query fan‑outs” as a core mechanism: the system fetches additional relevant results around an initial query and blends them. Your paid search and social campaigns, with their tightly matched keywords, audiences, and landing pages, help define which user intents belong together and which brands are consistently relevant within that cluster.

The implication is blunt: if you’re only using paid to chase last‑click conversions, you’re under‑leveraging one of the few levers that reliably shape AI training data in your category. GEO isn’t just about observing what AI engines say; it’s about deliberately feeding them better evidence.

That means planning campaigns with two scorecards. The first is familiar: CPA, ROAS, incremental revenue. The second is GEO‑aware: did this campaign increase the volume and quality of content that AI engines can ground on? Did it activate creators and publishers who already appear in your category’s AI answers? Did it produce behavioral signals that reinforce your status as a low‑risk recommendation?

In a world where buyers can ask a chatbot once and skip your entire funnel, the impressions you buy today are part of the training set that decides whether you’re recommended tomorrow. Treat your paid campaigns as disposable, and AI will treat your brand the same way.

Traditional GEO tools start by asking, “What does the AI say about me?” An AI‑first GEO program flips that question to: “What data does the AI see when it decides what to say—and how do I feed it better evidence than my competitors?”

Your most underused evidence layer is the stream of real user behavior you’re already buying through Meta, Google, Amazon, TikTok, retail media, and affiliates. Every impression, click, add‑to‑cart, and conversion is a live‑fire test of how real people respond to different angles, promises, and objections. Generative engines are desperate for exactly this kind of grounded signal. Your job is to translate paid performance into a research spine that tells you what to create, where to place it, and which entities and experts need to echo it so AI systems can safely recommend you.

Think about how GEO vendors operate today. As AdExchanger reported, most tools hammer the same prompts into ChatGPT, Claude, Perplexity, and AI Overviews thousands of times—“What’s the best running shoe for beginners?”, “Which eczema cream works without steroids?”—then track how answers change over weeks and months. That monitoring is essential, but it’s mostly descriptive. It tells you what the models are saying, not why they’re saying it or how to change the underlying evidence they rely on.

Ad spy data fills that gap. When you pull creative, copy, and landing pages from winning campaigns across your category, you’re not just “borrowing hooks.” You’re reconstructing the live test rig that’s teaching platforms what works. If a certain benefit statement or problem framing keeps showing up in high‑spend, long‑running ads, you can assume two things: (1) humans are responding to it with their wallets, and (2) platforms are feeding that success pattern back into their own recommendation and ranking systems.

The same logic now applies to AI engines. In Google’s own words, its generative experiences pull from the same index and ranking systems as classic search, so “what’s good for SEO is good for GEO” most of the time, as the WordStream team explains. But what Google and other engines increasingly privilege inside that index are signals of real‑world utility: content that maps cleanly to proven user intents, resolves objections, and is backed by visible engagement, reviews, and expert citations.

Treat your ad spy stack as the AI‑first research layer that discovers those intents and objections before you ever brief a writer or record a video:

• Mine paid angles into GEO clusters. Take the headlines, CTAs, and hooks from your top‑performing and competitor ads and group them into intent clusters: beginner vs. expert, budget vs. premium, fear of failure vs. desire for status, and so on. Those clusters become the spine of your GEO roadmap—the questions and anxieties you want AI engines to address with your brand as the safest answer.
• Turn winning messages into authoritative, human‑voiced assets. GEO is not about flooding the web with spin‑offs of ad copy. It’s about building deep, differentiated content that AIs can cite with confidence. As Jeff Bullas argues, generative engines increasingly reward original perspective, cited expertise, and a recognisable voice, while down‑ranking generic AI “slop.” When you see a paid angle working—say, “sensitive‑skin safe for eczema‑prone kids”—turn that into a long‑form guide, a physician‑reviewed FAQ, and a creator‑led testimonial rather than another 10 variants of the same ad.

• Align creators and entities with what the models already trust. GEO monitoring tools have revealed an unexpected side effect: creators who just happen to be cited in reputable coverage get surfaced repeatedly when brands ask LLMs who to partner with, as AdExchanger’s eczema‑creator example shows. Your ad intelligence should therefore inform not just your messaging, but who you put that message in the mouths of. If AI systems already associate certain publishers, medical specialties, or niche communities with your key claims, prioritize partnerships there and make sure those endorsements are crawlable, structured, and easy to quote.

• Feed back what AI is talking people out of. In Semrush’s research, AI chatbots talked 57.5% of AI users out of buying at least once—often by surfacing risks, better alternatives, or missing features. Those “AI objections” are gold. Your ad and landing‑page testing should deliberately stress‑test those objections: build variants that confront the concerns bots raise, then measure which rebuttals actually move conversion. The winners shouldn’t just roll into your next campaign; they should be baked into your GEO content, FAQs, schema, and review prompts so that the next time an LLM weighs your product, it sees stronger, better‑supported counter‑evidence.

When you work this way, ad spy data stops being a voyeuristic look at what competitors are running this week and becomes the front door to Gen‑AI Engine Optimization. Paid media is the quickest, cheapest laboratory for discovering which narratives, benefits, and proofs real humans accept. GEO is the discipline of turning those proven narratives into durable, cite‑worthy assets that live in the open web, in creator ecosystems, and in your own properties—exactly where generative engines go looking when they decide what to recommend next.

And here’s the uncomfortable part: none of this happens in a vacuum. You’re not the only one trying to reverse‑engineer “what the AI wants.” Your competitors, their agencies, and an entire cottage industry of GEO vendors are doing the same thing, often with the same public playbooks.

That’s why the next layer of a Gen‑AI Engine Optimization program isn’t just “use your ad data better,” it’s: systematically watch what everyone else is feeding the models, then decide where to follow, where to counter‑program, and where to attack.

Most GEO tools already probe Gemini, ChatGPT, and other models with thousands of prompts and log the outputs. As one AdExchanger piece described, vendors hammer queries like “What’s the best running shoe for a new runner?” on repeat, then trend the answers over weeks. That’s not research for curiosity’s sake; it’s live‑fire competitive intelligence. Every time a rival appears more often, or moves from “also consider” to “top recommendation,” that’s evidence their inputs have changed: a new explainer hub, a self‑promotional “best X” roundup, a burst of PR, or a coordinated ad blitz that’s suddenly generating more branded search and engagement.

An AI‑first brand uses that stream of GEO output like an ad‑spy feed. Instead of only watching who’s bidding on your keywords, you’re watching who’s consistently embedded inside the AI’s “shortlist” for your category and what narratives the model is learning about them. Are they leaning hard into “best for beginners” language? Have they quietly shifted to a “most trusted by enterprises” angle? Are they cross‑pollinating verticals by showing up in prompts well outside their original niche? Those patterns tell you exactly what kinds of inputs the model is rewarding right now.

This is also where you separate passing GEO fads from durable strategy. The wave of self‑serving “best [X] software” pages is a good example. As one analysis in Search Engine Journal showed, vendors that ranked their own products #1 on “best” lists successfully hijacked AI Overviews for a while, stuffing Google’s generative answers with biased praise. But the same coverage also documents how aggressively this tactic has been exposed, conference‑ified, and spammed. When you see that your rivals’ AI visibility is pegged to a trick that regulators, journalists, and the search teams themselves are now scrutinizing, you have a choice: chase the same sugar high, or design a more resilient play.

The emerging consensus is that “resilient” looks less like hacks and more like stacking signals. Google’s own GEO guidance reiterates that generative results still lean on the same index, ranking systems, and quality measures as traditional search, emphasizing that foundational SEO and demonstrable expertise are the common backbone of both experiences, as summarized by the team at WordStream. When you factor that into your competitor monitoring, AI output stops being mysterious and starts looking like a weighted blend of three things you can see and measure:

1. What’s in the index (their content footprint and structure).
2. How people respond (clicks, engagement, conversions—your ad‑spy layer).
3. How consistently they show up in AI answers (your GEO tracking).

Your advantage comes from triangulating all three over time. If a rival suddenly appears more often in AI recommendations without any meaningful change in their organic footprint, it’s a good bet they’ve altered the behavior signals—creative rotation, offer strategy, audience mix—and you can test counter‑moves with your own campaigns. If their AI presence rises and falls in lockstep with a narrow tactic (say, a burst of “best” pages or over‑optimized comparison tables), you treat that as brittle and build around it rather than chasing it.

In other words, the “An” in this story is analysis: treating generative engines as observable systems you can study, not black boxes you complain about. You’re not just trying to rank in a static list; you’re playing in a live ecosystem where your ads, your content, and your competitors’ experiments are all training the same models. The brands that win are the ones that make that feedback loop visible—and then move faster and smarter inside it than everyone else.