How AI Search Is Rewriting Search Intent for Paid Marketing
AI search is collapsing the funnel and reshaping query intent. Here is how to restructure keywords, match types, and bids before your CPL doubles.
Most paid search strategies still assume the user is typing three or four words into a box and scanning ten blue links. That assumption is now wrong often enough to be dangerous. The query stream feeding Google Ads, Microsoft Advertising, and the new AI answer engines looks nothing like the query stream of 2022, and the intent behind each query has shifted in ways that break legacy keyword structures, match-type logic, and bid rules.
The uncomfortable summary for performance teams: top-of-funnel informational traffic is collapsing into AI answers, bottom-funnel commercial queries are getting more expensive and more crowded, and the queries that still convert are longer, more conversational, and harder to map to a traditional ad group. Rebuilding your account for that reality is not optional work for 2027. It is the work of this quarter.
The Query Itself Has Changed Shape
Start with the raw unit of paid search: the query. A Nectiv study of more than 8,500 prompts found that ChatGPT's internal search queries average 5.48 words, roughly 61% longer than the 3.4-word Google average. Length is only the surface change. Prompts arrive with context ("I run a two-person dental practice in Scottsdale and need…"), constraints ("under $400, ships this week"), and comparative framing ("vs. Invisalign for mild crowding") baked in before the user ever sees a result.
That matters for paid search because every one of those additional tokens is a signal the ad auction never used to see. The modern query is closer to a brief than a search. When Google's AI layer rewrites that brief into the keywords and auctions you actually bid in, your old exact-match structure stops catching the parts of the funnel it used to own.
The intent tiers themselves need re-mapping. The classic informational / navigational / commercial / transactional model is still useful, but the volumes inside each tier have moved. Informational is draining out of paid-click territory entirely. Navigational is partly absorbed by chat interfaces that never render a SERP. Commercial and transactional are compressing into a smaller surface where answer engines, shopping modules, and ads all fight for the same square of screen.
Informational Traffic Is Leaving the Open Web
If you still run broad informational keywords expecting cheap top-of-funnel volume, the data has caught up with you. Informational queries now resolve on the SERP at a 74% rate, versus 31% for transactional queries, and the Status Labs comparison of answer completeness found that ChatGPT returned a complete answer in a single response 87% of the time for multi-part questions, while Google required an average of 3.2 site visits to assemble the same information.
Two implications follow for paid budgets. First, the "capture them while they're learning" ad group is largely dead on cost-efficiency grounds. The impressions still exist, but the clicks are drying up and the ones you do pay for correlate poorly with downstream pipeline. Second, informational content on your own site is now mostly a citation play into LLM answers rather than a landing-page destination for cold paid traffic. Keep producing it, but stop sending expensive paid clicks to it.
This is where many accounts quietly bleed. A broad-match keyword scoped around a question phrase ("how does title insurance work") used to pull a mix of researchers and near-buyers. In 2026 it pulls mostly impressions that never resolve, plus a residue of low-intent clicks you are overpaying for. Our own breakdown of PPC keyword match types walks through how to tighten this before AI Max expansion makes it worse.
Bottom-Funnel Competition Is Being Compressed
While top-of-funnel evaporates, the opposite is happening at the bottom. A Semrush study of more than 600,000 keywords between November 2025 and April 2026 found that AI Overviews on commercial-intent SERPs grew an average of 71%, and Google Ads now appear alongside AI Overviews roughly twice as often as a year earlier. At Google Marketing Live in May 2025, Vidhya Srinivasan confirmed that ads would be served directly within AI Overviews, and that Google's models detect commercial intent even in queries that do not look commercial on their face.
Two things happen when that stack lands on a results page. One, the paid real estate itself gets squeezed into less space, and Seer Interactive data shows paid click-through rates dropping from 19.7% to 6.34%, a 68% decline, when an AI Overview appears. Two, every advertiser who used to monetize informational SERPs is now piling into whatever commercial auctions remain, which pushes CPCs up before any single bid is adjusted.
Expect three symptoms in the account: higher auction insight overlap on branded and category-plus-intent terms, rising CPCs on exact-match transactional keywords, and a widening gap between impression share and click share on queries that trigger AI answers. If your reporting is still averaging CTR across SERP types without segmenting for AI Overview presence, you are flying blind.
The New Answer-Engine Ad Surfaces
Google is not the only auction being reshaped. Perplexity launched advertising in November 2024 on a CPM model with rates above $50 per thousand impressions before pausing new advertisers roughly eleven months later, which tells you two things at once: the demand to buy placement inside answer engines is real, and the formats are not stable yet. ChatGPT's ad surface is maturing in parallel, and the strategic calculus between the two platforms is covered in our ChatGPT Ads vs Google Ads benchmarks.
For an operator, the near-term playbook is not to pile budget into every new surface. It is to profile each one honestly against the few axes that actually determine whether it will work for your account.
The right posture for most teams right now is a small, instrumented test budget on the AI-native platforms while the primary P&L stays on Google and Meta. The point of the test is not immediate ROAS. It is to learn how your conversion events behave when the pre-click journey happens inside an LLM.
Rebuilding Keyword Structure and Bid Logic
Here is a working framework for restructuring an account around the new intent reality. It assumes you still have access to search terms reporting, negative keyword controls, and conversion-based bidding, which remain the levers that matter even as AI Max absorbs legacy campaign types.
- Re-tier your keywords by whether a human or an LLM is the likely reader. Short head terms and classic question phrases now compete with AI answers for the same impression. Longer, context-rich, constrained phrases are closer to the modern query shape and tend to convert better per click.
- Separate campaigns by AI Overview presence, not just by match type. Segment search terms by whether an AI module appeared, and treat the two cohorts as different auctions with different CPC ceilings and different conversion expectations.
- Tighten negatives aggressively on broad and AI Max expansion. Google's own reporting now shows an "AI Max" source column in search terms. Review it weekly and cut the long tail that does not match a real buyer profile.
- Shift bid strategies toward value, not clicks. With fewer clicks per impression and more variance in intent per click, target ROAS and target CPA strategies outperform manual CPC on all but the tightest brand campaigns. Our write-up on flexible bid strategies covers the trade-offs.
- Instrument assisted conversions from LLM referrers. Users who first interacted with an answer engine often arrive via branded or direct traffic. If your attribution only credits last-click Google, you will under-invest in the queries doing the real top-of-funnel work.
None of these moves are theoretical. They are what a disciplined PPC audit surfaces inside almost every account built before mid-2025, and they are the first things to fix before layering on any AI PPC tooling. Automation on top of a misstructured account just scales the mistakes.
Measurement Has to Change Too
The last piece is reporting. Click-through rate as a top-line KPI is losing meaning when the auction itself keeps mutating under you. Impression share on AI-adjacent surfaces, assisted-conversion share from direct and branded traffic, and conversion rate segmented by query length are the three signals that actually tell you whether your account is adapting.
Branded search volume is a particularly useful tell. When top-of-funnel discovery moves into LLM answers, buyers increasingly land on your brand name as the first query they type into Google. If branded search is growing while non-brand clicks fall, your upstream presence in AI answers is working. If both are falling, the pipeline is contracting and no bid tweak will fix it.
What Operators Should Do This Quarter
The posture that works right now is pragmatic, not reactive. Audit the share of your spend sitting on classic informational keywords and either reallocate or restructure. Segment every performance report by AI Overview presence. Run a small, honest test on at least one answer-engine ad surface so your team learns the mechanics before the formats stabilize and CPMs climb. Keep brand campaigns tight, because the AI answer layer is making brand recognition a bigger share of what wins the final click.
Generative answer engines did not kill paid search. They changed what a query is, what a click is worth, and where in the funnel ads actually earn their keep. Accounts rebuilt around that reality are already quietly outperforming the ones still optimizing for a SERP that no longer looks the way it did two years ago.