Most PPC teams still build campaigns the same way: pull a keyword list, set match types, and organize ad groups around search terms. It’s muscle memory.
But Google’s auction no longer works that way.
Search now behaves more like a conversation than a lookup. In AI Mode, users ask follow-up questions and refine what they’re trying to solve. AI Overviews reason through an answer first, then determine which ads support that answer.
In Google Ads, the auction isn’t triggered by a keyword anymore – it’s triggered by inferred intent.
If you’re still structuring campaigns around exact and phrase match, you’re planning for a system that no longer exists. The new foundation is intent: not the words people type, but the goals behind them.
An intent-first approach gives you a more durable way to design campaigns, creative, and measurement as Google introduces new AI-driven formats.
Keywords aren’t dead, but they’re no longer the blueprint.
The mechanics under the hood have changed
Here’s what’s actually happening when someone searches now.
Google’s AI uses a technique called “query fan out,” splitting a complex question into subtopics and running multiple concurrent searches to build a comprehensive response.
The auction happens before the user even finishes typing.
And crucially, the AI infers commercial intent from purely informational queries.
For instance, someone asks, “Why is my pool green?” They’re not shopping. They’re troubleshooting.
But Google’s reasoning layer detects a problem that products can solve and serves ads for pool-cleaning supplies alongside the explanation. While the user didn’t search for a product, the AI knew they would need one.
This auction logic is fundamentally different from what we’re accustomed to. It’s not matching your keyword to the query. It’s matching your offering to the user’s inferred need state, based on conversational context.
If your campaign structure still assumes people search in isolated, transactional moments, you’re missing the journey entirely.
Dig deeper: How to build a modern Google Ads targeting strategy like a pro
What ‘intent-first’ actually means
An intent-first strategy doesn’t mean you stop doing keyword research. It means you stop treating keywords as the organizing principle.
Instead, you map campaigns to the why behind the search.
- What problem is the user trying to solve?
- What stage of decision-making are they in?
- What job are they hiring your product to do?
The same intent can surface through dozens of different queries, and the same query can reflect multiple intents depending on context.
“Best CRM” could mean either “I need feature comparisons” or “I’m ready to buy and want validation.” Google’s AI now reads that difference, and your campaign structure should, too.
This is more of a mental model shift than a tactical one.
You’re still building keyword lists, but you’re grouping them by intent state rather than match type.
You’re still writing ad copy, but you’re speaking to user goals instead of echoing search terms back at them.
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What changes in practice
Once campaigns are organized around intent instead of keywords, the downstream implications show up quickly – in eligibility, landing pages, and how the system learns.
Campaign eligibility
If you want to show up inside AI Overviews or AI Mode, you need broad match keywords, Performance Max, or the newer AI Max for Search campaigns.
Exact and phrase match still work for brand defense and high-visibility placements above the AI summaries, but they won’t get you into the conversational layer where exploration happens.
Landing page evolution
It’s not enough to list product features anymore. If your page explains why and how someone should use your product (not just what it is), you’re more likely to win the auction.
Google’s reasoning layer rewards contextual alignment. If the AI built an answer about solving a problem, and your page directly addresses that problem, you’re in.
Asset volume and training data
The algorithm prioritizes rich metadata, multiple high-quality images, and optimized shopping feeds with every relevant attribute filled in.
Using Customer Match lists to feed the system first-party data teaches the AI which user segments represent the highest value.
That training affects how aggressively it bids for similar users.
Dig deeper: In Google Ads automation, everything is a signal in 2026
The gaps worth knowing about
Even as intent-first campaigns unlock new reach, there are still blind spots in reporting, budget constraints, and performance expectations you need to plan around.
No reporting segmentation
Google doesn’t provide visibility into how ads perform specifically in AI Mode versus traditional search.
You’re monitoring overall cost-per-conversion and hoping high-funnel clicks convert downstream, but you can’t isolate which placements are actually driving results.
The budget barrier
AI-powered campaigns like Performance Max and AI Max need meaningful conversion volume to scale effectively, often 30 conversions in 30 days at a minimum.
Smaller advertisers with limited budgets or longer sales cycles face what some call a “scissors gap,” in which they lack the data needed to train algorithms and compete in automated auctions.
Funnel position matters
AI Mode attracts exploratory, high-funnel behavior. Conversion rates won’t match bottom-of-the-funnel branded searches. That’s expected if you’re planning for it.
It becomes a problem when you’re chasing immediate ROAS without adjusting how you define success for these placements.
Dig deeper: Outsmarting Google Ads: Insider strategies to navigate changes like a pro
Where to start
You don’t need to rebuild everything overnight.
Pick one campaign where you suspect intent is more complex than the keywords suggest. Map it to user goal states instead of search term buckets.
Test broad match in a limited way. Rewrite one landing page to answer the “why” instead of just listing specs.
The shift to intent-first is not a tactic – it’s a lens. And it’s the most durable way to plan as Google keeps introducing new AI-driven formats.
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