FROM SERPS TO ‘SPYRPS’: HOW COMPETITIVE AD INTELLIGENCE BECOMES YOUR SECRET WEAPON IN AI SEARCH & AI OVERVIEWS

If you feel like Google’s new AI Overviews and chat-style search experiences are less “results pages” and more “always‑on sales reps,” you’re not imagining it. The old SERP behaved like a directory: a ranked list of options you could browse, open, compare, and bounce between. AI search behaves much more like a high‑performing ad funnel: it captures the query, qualifies the intent, curates a tight shortlist of solutions, and then nudges the user toward a next step—often without ever sending them to your site.

That shift shows up clearly in the data. In a large‑scale analysis of 4 million prompts, ChatGPT pulled 88.46% of its citations from the classic search index, meaning “good old‑fashioned” rankings are still doing most of the heavy lifting inside AI answers, even as the interface looks completely different, according to Search Engine Journal’s breakdown of AI search myths. In other words, the pipeline feeding these AI responses is still search—but the way those responses are packaged to users now looks and feels like a conversion experience, not a research sandbox.

AI Overviews intensify this funnel‑like behavior by collapsing multiple buyer‑journey stages into a single screen. As the Semrush team found in their study of how AI Overviews are expanding across commercial search, many of the highest‑CPC, highest‑value keywords are now the ones most likely to trigger an Overview, and those Overviews are appearing “deeper into the buyer journey,” especially around complex, high‑stakes decisions in categories like finance and education, as Semrush’s analysis of commercial AI Overviews explains. A user who once clicked three or four links to compare options can now see a summarized recommendation set, supporting citations, and paid placements in a single, vertically compressed canvas.

Layer ads on top of this, and the “funnel” metaphor becomes literal. New anstrex.com/blog/5-brilliant-tools-to-monitor-competitors-facebook-ad-strategies" target="_blank" rel="noreferrer noopener">competitive intelligence from Similarweb shows that 26% of ChatGPT responses and nearly 30% of ad‑eligible Google AI Mode queries already include sponsored placements embedded inside the AI answers themselves, as reported in Search Engine Watch’s coverage of Similarweb’s AI Ads product. Those ads don’t sit off to the side, visually separate from the “real” results. They’re interleaved with the assistant’s guidance, giving brands the chance to buy their way into what feels like a trusted, personalized recommendation flow.

At the same time, these AI surfaces are no longer neutral lists of links; they’re judgment calls about which brands deserve to be surfaced, cited, and recommended. As MarTech’s discussion of AI search and trust signals points out, AI systems are effectively acting as new visibility gatekeepers, relying heavily on perceived authority, reputation, and technical quality to decide which sources get pulled into the experience. In practice, that means your brand is being “positioned” for the user before they ever click—AI Overviews, citations, and embedded ads collectively communicate what kind of player you are in the category.

For marketers, this turns AI search into something that looks very much like a blended paid‑and‑organic funnel. The organic side is your eligibility: Are you cited in the Overview? Do you show up across the fan‑out of related queries feeding that answer set, given that only about 38% of AI citations now come from traditional top‑10 rankings, as Search Engine Journal’s dataset on AI citation patterns shows? The paid side is your amplification: Are you present in the sponsored modules inside ChatGPT, Google AI Mode, or AI Overviews when a user is most ready to act, based on what Similarweb is now tracking inside those responses via their AI Ads visibility data?

The result is a search landscape where discovery, evaluation, and persuasion converge inside the AI result itself. Instead of thinking of “rankings over here” and “ads over there,” it’s more accurate to view AI search as a unified, algorithmically curated funnel—one where organic authority earns you a seat at the table, paid placements accelerate exposure at high‑intent moments, and the assistant orchestrates both into what feels, to users, like a single trusted conversation.

Under the hood, AI search is powered by one big idea: retrieval‑augmented generation (RAG). Instead of “thinking” in a vacuum, the model runs a fast, focused mini‑crawl in response to each query, retrieves a handful of pages, and then generates an answer on top of those sources. The result looks like an opinionated sales rep synthesizing “the internet,” but in practice it’s synthesizing a tiny shortlist of pages that passed its internal trust and relevance checks in milliseconds.

Those checks are brutally pragmatic. As the team at Ahrefs explains in their breakdown of retrieval‑augmented generation, RAG gives preference to content it can access quickly, parse easily, and match tightly to the user’s intent. Long, meandering essays or brand‑centric fluff create friction for a system whose job is to assemble a clear, BLUF‑style (“bottom line up front”) answer. By contrast, high‑intent landing pages are engineered to do exactly what RAG systems need: define the problem crisply, foreground a solution, and back it with specific details, comparisons, and proof.

In other words, the “AI answer” layer is increasingly built out of pages that look and behave like top‑performing ads.

Think about the anatomy of a good PPC landing page. A strong hook that mirrors the query. A sharp problem statement. Scannable sections that map directly to user objections. Concrete features and benefits. Social proof. Pricing or at least clear next steps. PPC‑focused strategists have been urging brands to build these deep, problem‑solving destinations for years; one analysis of how ChatGPT is reshaping search behavior argues that “deep landing pages” are now critical both for humans and for AI tools that need rich, structured input to answer questions well, especially as.

From an AI model’s perspective, that structure reads as trust and clarity. The more your page feels like a high‑performing ad or offer page—explicit about who it’s for, what it solves, and how it compares—the less “interpretive work” the model has to do. That lowers risk, which matters, because these systems are increasingly optimized to avoid hallucinations and minimize reputational blowback. When an AI assistant has to pick a handful of sources to stand behind, it will default to pages that look like they were built to support a decision.

Trust is not just about layout, of course. Generative systems are under pressure to cite brands with recognizable authority signals across the broader web. As one analysis of AI search visibility notes, models lean heavily on brands with strong digital credibility built through authoritative earned media. This moves us beyond classic “E‑E‑A‑T” checklists into a world where your entire reputation graph—mentions, reviews, PR, expert bylines—becomes the background radiation shaping which pages RAG even considers.

That’s why it’s not enough to pump out AI‑generated blog posts and hope the model finds you. When teams went all‑in on generic AI content without a clear tie to their core positioning, some initially saw a spike in visibility, only to watch rankings revert to a lower baseline as algorithms and assistants deprioritized thin, undifferentiated material. One case study described how a software competitor that flooded the web with off‑brand AI content briefly peaked, then slid back to earlier visibility levels once it became clear the content lacked “unique insights, firsthand expertise, and original perspectives” that humans like Lily Ray argue are essential. RAG systems have similar biases: they favor pages that exhibit a strong, consistent point of view and verifiable expertise.

Put all of this together and a pattern emerges:

• RAG is engineered for precision, not exploration.
• Precision favors pages that behave like ad funnels: purpose‑built, tightly scoped, and conversion‑oriented.
• Trust is inferred from both the page itself and the brand’s broader reputation graph.

AI search, then, doesn’t just “prefer” pages that look like ads by accident. It prefers them because they’re the cleanest, safest raw material for building confident, decision‑driving answers—turning your best landing pages into the invisible scaffolding of the new “SpyRPs” your buyers never see, but increasingly rely on.

Traditional PPC and SEO tools are already whispering how AI Overviews work—you just have to listen to the right signals.

If you strip away the branding, an AI Overview is basically a high‑impact ad unit stitched together from organic pages. It’s fed by RAG, but it behaves like a hybrid of shopping comparison block, featured snippet, and “people also ask” carousels. And most of the levers we already track in competitive ad intelligence—visibility, share of voice, volatility, cannibalization—map surprisingly well to this new layer.

First, visibility. AI search is still small in raw traffic terms, but it punches far above its weight on conversions. HubSpot’s analysis of answer‑engine traffic found that AI referrals are under 1% of visits in large datasets, yet they convert materially higher than classic organic sessions, with one study showing generative AI visitors converting about 1.2x more than search visitors in aggregate, according to their summary of WebFX and Microsoft Clarity data. That’s exactly how we’ve treated top‑of‑page ad inventory for years: a small slice of clicks that disproportionately drives pipeline. So when you see AI Overviews sitting on top of your most expensive commercial keywords, assume you’re looking at a new, free‑ish ad slot that’s capable of doing real revenue damage—or delivering real upside.

Next, coverage and overlap. Semrush’s large‑scale study of commercial queries shows that AI Overviews are expanding deeper into the buyer journey and increasingly appearing on high‑CPC terms where the stakes are already high, as their research on commercial AI Overviews makes clear. In those SERPs, your brand can now appear in four distinct zones: inside the AI Overview answer, inside its citations list, in the organic rankings below, and in paid placements above, beside, or even inside that AI block. That’s classic paid/organic cannibalization—just with an AI layer welded on top.

If you’re already using tools to map where ads appear, how often they crowd out organic results, and how much impression share you’re losing to competitors, you’re 80% of the way to understanding AI Overviews. The playbook is similar:

• Identify which of your high‑value keywords regularly trigger an AI Overview.
• See which competitors are “owning” that unit through citations and mentions.
• Quantify how often your brand is absent there, even when you’re paying for the click or ranking below.

In other words, your SERP analysis becomes “SpyRP” analysis: you’re not just spying on who’s bidding or ranking—you’re spying on who AI has quietly cast as the category’s default advisors.

Third, volatility and message control. Many marketers assume that because AI answers change frequently, they’re impossible to track. Yet large‑scale analysis of AI responses shows a different pattern: while the citation set reshuffles, the actual stance of the answer tends to hold steady over time, as Search Engine Journal’s coverage of Ahrefs’ Brand Radar data points out. That’s the same dynamic you see in auction‑based ad placements: positions in the carousel move, competitors rotate, but the platforms converge on a stable mix of “acceptable” advertisers and value propositions.

For AI Overviews, that means two things:

• Treat answer engines as narrative infrastructure, not random dice rolls. Once Google decides which angles, brands, and frameworks “belong” in a given commercial topic, they’re sticky.
• Borrow your competitive ad‑tracking mindset. The same way you log who appears above you for core PPC terms, you should log which brands AI leans on when it explains your category, and how their messaging frames the decision.

Finally, intent and pre‑qualification. AI search behaves a lot like a nurturing funnel: it asks follow‑ups, clarifies constraints, and then presents a narrow band of options that match that refined intent. HubSpot’s work on answer‑engine optimization highlights how these engines implicitly pre‑qualify users by interrogating sub‑queries and tailoring responses, which is why they see AI‑driven visits skewing more qualified than traditional search. That’s almost exactly what sophisticated PPC teams do with tightly segmented ad groups and deep, problem‑specific landing pages.

Put differently: every AI Overview is a free intent‑refinement layer that may be helping or hurting your funnel. Your existing spy tools already tell you:

• Where intent is commercial and expensive (your high‑CPC, high‑competition terms).
• Where those same terms now trigger AI Overviews.
• Where competitors are consistently present in that AI block while you’re invisible.

Once you overlay those three views, the pattern pops: AI Overviews are just another premium placement in a crowded auction. And the same competitive intelligence you’ve used for years to win that auction is now your best clue to how Google’s “secret” AI layer really works.

In a world where AI answers are stitched together from a tiny subset of the web, your competitors’ ads and funnels are no longer just about their ROAS. They’re a live feed of the pages, angles, and proof points most likely to get cited inside AI Overviews and conversational search — and to shape how those systems talk about your entire category.

Competitive ad intelligence tools have already made this explicit in classic SERPs. Now, platforms that report on AI search surfaces are extending that same x‑ray vision into ChatGPT answers, Google AI Mode, and AI Overviews. Similarweb’s new AI Ads product, for example, tracks which brands are winning sponsored placements inside ChatGPT and Google AI experiences, along with their ad creatives, landing pages, and relative share of voice across these AI‑native units, according to their coverage of ads inside ChatGPT and Google AI Mode. That means every ad and landing page your competitors test in AI search doubles as a signal: “This is the narrative, offer, and proof mix that both the auction and the AI like enough to show users.”

The key shift is to stop looking at those competitor flows only as conversion machines and start reading them as training data. AI systems over‑weight independent, editorial‑style coverage and deep, problem‑solving content when deciding who to mention, as recent analysis of AI visibility and citations in search experiences has shown in detail about who writes about your brand. But the way users arrive at those pages — typically via high‑intent queries and ad funnels — is just as important. When you map competitors’ ad copy, targeting, and landing pages against the pages that keep surfacing in AI responses, you get a predictive model of which themes and entities the AI is learning to associate with each brand.

Practically, that means loading your favorite spy tool, pulling your rivals’ search and social ad libraries, and tagging each creative and funnel step by: problem framing, product positioning, proof source (customer logos, media quotes, benchmarks), and topical depth. Then you compare those themes to where AI is already citing them across AI Overviews, ChatGPT, and other assistants using an AI citation tracker such as the type of “Brand Radar” reporting described in recent RAG and AI visibility research. When the same competitor page is both the destination for multiple high‑intent ads and a recurring AI citation, you’ve identified a “dual‑threat” asset: it converts users directly and trains the AI to keep mentioning that brand as the standard.

This is also how you forecast which competitors are about to own the sentiment in AI answers, even before rankings fully reflect the shift. If a rival floods the zone with ad traffic to a new “definitive guide” or comparison page, and that page is built like the editorial coverage AI loves — deep, non‑salesy, rich in stats and external references — you can assume that, given enough user interaction, it will start showing up in AI Overviews for your core queries. Over time, as analysts of human‑led SEO have emphasized, the brands that consistently align paid, content, and editorial angles build a compounding advantage in how search surfaces describe them, because a coherent narrative is easier for AI to recognize and reuse, particularly when there are humans in the loop ensuring every asset reflects authentic expertise and brand perspective, as recent work on the human edge in AI‑era SEO has argued.

The same logic applies to protecting your own brand sentiment. If AI systems are increasingly influenced by third‑party coverage and by the pages users engage with after ads, you can’t simply “optimize” your site and hope for neutral treatment. You need to know which competitor funnels are successfully seeding narratives like “best value,” “safest choice,” or “enterprise standard” into both human minds and machine summaries — and then decide whether to counter them head‑on (with your own comparative content and PR) or flank them by dominating adjacent, higher‑intent problems. Competitive ad spying becomes less about copying bids and more about reverse‑engineering which stories about your category are gaining enough traction, and enough AI attention, to become the default script buyers hear before they ever see a SERP.

Rankings used to be the scoreboard. In an AI‑first world, they’re just one input. The real game is whether answer engines choose your brand as the “default” narrative when they compress an entire category into a few sentences and a handful of citations.

Competitive ad intelligence is how you start engineering that narrative on purpose.

Instead of treating PPC data as a channel report, treat it as a live map of the language, proof, and journeys that reliably convert high‑intent searchers. As Real FiG’s PPC analysis points out, transactional and commercial searches remain remarkably stable even as informational queries migrate to AI. That means your paid search and social campaigns are still where buyers reveal the exact problems, objections, and decision criteria they care about at the moment of purchase. Those are the same inputs large language models lean on when they generate buying guides, comparisons, and “best X for Y” recommendations.

“AI‑first” brand presence starts by codifying those inputs into a system:

1. Mine ad and funnel data to define your AI‑era positioning.
   Pull your best‑performing search, social, and native campaigns and look for the recurring angles: risk reversal, total cost of ownership, niche use cases, integrations, implementation speed. Then map which landing pages and assets close the loop for each angle. You’re not just looking for keywords; you’re looking for the story that repeatedly turns cold clicks into pipeline.

This is where human judgment remains non‑negotiable. When marketers chased volume with generic, AI‑generated content untethered from a clear narrative, visibility spikes faded as fast as they appeared, as one IT software rival learned in a case study summarized on Neil Patel’s blog. The brands that sustained growth used humans to curate and refine the angles AI suggested, making sure every asset expressed a distinct point of view that couldn’t be copy‑pasted by anyone else.

2. Refactor winning ad angles into AI‑optimized “answer assets.”
Once you know which pitches and pages win, you can rebuild them in formats AI systems love to cite: tightly scoped explainers, comparisons, checklists, and how‑to frameworks that answer a narrow question comprehensively. Layout matters as much as language: clear subheads, bulleted criteria, pricing ranges, and short, quotable definitions all make it easier for models to extract and reuse your ideas.

The key shift is that you’re designing these assets first for answer engines, second for rankings. That aligns with the way AI search engines now surface content via three distinct pathways — direct citation, summary without explicit citation, and recommendation based on inferred authority — a framework outlined in HubSpot’s guide to AI search optimization. Your goal is to be structurally “easy to quote” in all three modes.

3. Use competitive ad intelligence as an “AI‑gap detector.”
Spy on competitor campaigns to see which pains and personas they’re over‑owning in paid — and where they’re strangely quiet. If rivals flood the zone with starter‑tier offers and generic “how to choose” content, but spend nothing on advanced use cases or complex deployments, you’ve just found a white space. Build deep, AI‑friendly answer assets around those neglected scenarios, and then reinforce them with tightly targeted campaigns so that both buyers and models start associating your brand with that higher‑value territory.

This is also where you reconcile rankings with AI visibility. Research summarized on MarTech shows that only a minority of AI Overview citations come from traditional top‑10 results, and that independent editorial coverage disproportionately shapes which brands get recommended. When you see competitors’ talking points taking off in ads and then reappearing in media coverage, that’s a signal to respond with differentiated angles, not just bid wars.

4. Measure brand presence by “share of answer,” not position.
Finally, shift your reporting from “where do we rank?” to “how often do we get named, cited, or paraphrased when buyers ask AI the questions our ads target?” Tools like HubSpot’s AEO visibility and benchmarking framework show how to identify answer competitors, track mentions across engines, and tie AI‑influenced sessions to downstream revenue.

When you connect those dots back to campaigns and creative — this ad angle, to this landing page, to this AI citation pattern — you stop treating AI search as a black box. You start treating it as another performance surface you can design for: one where your best ad insights get translated into durable, AI‑native brand presence that persists long after the click.

Spy data is a superpower, but it’s also a trap.

If you’re not careful, it pushes you toward a world where every decision is driven by what the dashboard says competitors are doing, instead of what your customers actually need and what your brand can credibly own.

In an AI‑first search landscape, that’s the fastest way to become invisible.

Competitive ad intelligence will absolutely tell you which hooks are scaling, which funnels are getting budget, and which pages are being stress‑tested for conversion. It can even help you reverse‑engineer which assets are most likely to be pulled into retrieval‑augmented systems and cited in AI Overviews. But none of that matters if you don’t have humans in the loop to interpret, prioritize, and push back.

Several large‑scale studies have already shown that AI visibility now operates on its own logic. For example, one analysis of millions of AI responses found that only about 38% of URLs cited in AI Overviews also sat in the top 10 organic results for the same query, down from roughly 76% a year earlier, as Google shifted toward fan‑out retrieval across related queries rather than just the original keyword, according to research using Ahrefs’ Brand Radar data. Another study of commercial AI Overviews observed that some of the most valuable query spaces—finance, jobs, education—now force brands to compete simultaneously in paid units, organic results, and AI‑generated answers on the same page, which makes judgment calls about where to lean in or pull back far more consequential, as Semrush’s analysis of AI Overviews in commercial search explains.

Dashboards can surface those patterns. They cannot decide what they mean for your brand.

That’s the first role of humans in this loop: editorial judgment. If your “SpyRP” view shows that a competitor is blitzing AI‑heavy categories with thin, high‑volume content, you need someone who can say, “We’re not copying that, because it doesn’t square with who we are or how our buyers make decisions.” One client case documented on Neil Patel’s blog about the human edge in SEO illustrates this perfectly: a software competitor rode an AI content surge to a temporary ranking spike, only to see visibility fall back to pre‑push levels once the dust settled, largely because the material drifted away from the brand’s core identity and purpose.

Your competitive ad intel will almost certainly show similar temptations: cheap angles, shallow FAQs, generic authority‑washing that appears to “work” in the short term. A human strategist keeps you from over‑indexing on what’s easy to imitate and under‑indexing on the kind of firsthand expertise, proof, and narrative consistency that AI systems increasingly reward over time. As Lily Ray has argued in her research, quoted in that same human‑led SEO discussion, AI‑supported content still needs unique insight and original perspective—two things no scraping tool can hand you.

The second human job is orchestrating trade‑offs that tools can’t see. Spy data can tell you your CPCs are rising where AI Overviews appear. It can’t decide whether your best move is to double down on paid, shift that budget into creating reference‑worthy assets more likely to be cited by AI, or go after adjacent fan‑out queries where you can more easily become the default source. Platforms like Ahrefs have shown that you can build a feedback loop—optimize, publish, then track shifts in citations across ChatGPT, Perplexity, and Google’s AI surfaces with tools like Brand Radar’s AI citation tracking—but someone still has to interpret when a small change in AI visibility is strategically meaningful versus statistical noise.

Finally, humans are the only ones who can translate spy data into brand‑safe execution. When AI systems overweight independent editorial coverage, as one recent analysis of AI Overviews and media signals in MarTech’s deep dive on who writes about your brand points out, the logical response is not “flood PR with generic thought leadership.” It’s to build an integrated program where your ad intel, your PR priorities, and your owned content roadmap all ladder up to a single story that others actually want to cite.

Competitive ad intelligence can show you where the game is being played and how other teams are lining up. But it’s your humans—strategists, editors, media buyers, PR leads—who decide what game you’re actually in, which plays fit your identity, and when to ignore the decoy patterns the dashboards inevitably surface.

In the next section, we’ll pull all of this together into a practical framework for turning raw SpyRP visibility into a living, cross‑functional operating system for AI‑era search—one where tools do the watching, but humans still do the choosing.