Google Ads works based on intent

Most PPC specialists still build campaigns using the traditional model: they create keyword lists, set match types, and structure ad groups around search queries. This has become professional muscle memory.

However, today Google’s advertising auction works differently.

Search is increasingly behaving like a conversation rather than a simple information lookup. In AI-powered modes, users ask follow-up questions, change phrasing, and gradually refine their needs. AI Overviews first construct an answer and only then determine which ads can support that answer.

In Google Ads, auction triggering is increasingly based not on a specific keyword, but on interpreted user intent.

If campaigns are still built around exact and phrase match as the foundation, this means planning for a search model that is gradually losing relevance. The new core logic is user intent: not just the words people type, but the goals behind the query.

An intent-first approach allows you to build a more resilient system for campaign planning, creative development, and measurement as Google introduces new AI-driven formats.

Keywords are not disappearing, but they are no longer the foundation of the entire campaign architecture.

Read more about how to create effective ad copy for Google Ads.

What’s actually happening when someone searches now?

Most of the changes in search already happen at a level the user doesn’t see.

Today, when someone enters a query, Google uses a technology called query fan out — a complex question is split into sub-queries, and the system runs multiple parallel searches to build a comprehensive response.

In some cases, the ad auction may start before the user even finishes typing.

The key shift is that AI can infer commercial intent even from purely informational queries.

For example, a user asks: “Why is my pool turning green?” Formally, this is an informational query — the person is looking for the cause of a problem. But the system detects that this problem can be solved with products and serves ads for pool cleaning supplies alongside the explanation.

In other words, the user didn’t search for a product directly, but the system predicts they may need one.

This auction logic is fundamentally different from the traditional model. It’s no longer about matching keywords to queries — it’s about matching your offering to the user’s need state, determined by context and behavior.

If your campaign structure still assumes users search only in isolated, transactional moments, you’re missing the full customer journey.

Anatomy of a search query in Google AI

Let’s break this down further: how to build a modern targeting strategy in Google Ads.

What an intent-first approach means

An intent-first strategy does not mean you should stop doing keyword research. It means you should stop using keywords as the primary organizational principle.

Instead, you need to structure campaigns around why the user is searching.

What problem is the user trying to solve?
What stage of decision-making are they in?
What job are they trying to complete using your product?

The same intent can be expressed through dozens of different queries, and the same query can have multiple intents depending on context.

For example, “Best CRM” can mean either “I need feature comparisons” or “I’m ready to buy and want to check reviews.” Google’s AI can now distinguish these differences, and your campaign structure should reflect that.

This is more of a mindset shift than a tactical step.

You will still build keyword lists, but now you group them by intent state rather than match type.

You will still write ads, but now you address user goals instead of simply repeating queries.

What changes in practice

In practice, the impact shows up quickly — in eligibility, landing page requirements, and how the system learns.

Campaign eligibility

If you want to appear in AI Overviews or AI Mode, you need broad match keywords, Performance Max, or newer AI Max for Search campaigns.

Exact and phrase match are still useful for brand protection and high-visibility placements above AI summaries. But they rarely get you into the conversational discovery layer.

Landing page evolution

Listing product features is no longer enough. If your page explains why and how someone should use your product (not just what it is), your chances of winning the auction increase.

Google’s reasoning layer rewards contextual alignment. If the AI builds an answer around solving a problem and your page directly addresses that problem, you gain an advantage.

Asset volume and training data

The algorithm prioritizes:
— rich metadata
— multiple high-quality images
— optimized shopping feeds with all relevant attributes filled in

Using Customer Match and first-party data helps the system understand which user segments represent the highest value. This directly affects how aggressively it bids for similar audiences.

What you need to consider: limitations and blind spots

Lack of detailed reporting

Google still doesn’t provide separate reporting for ad performance in AI Mode versus traditional search.
In practice, you monitor overall cost-per-conversion and assume part of upper-funnel traffic converts later.

The budget barrier

AI-driven campaigns need sufficient conversion volume to scale effectively (often around ~30 conversions within 30 days).
Smaller advertisers may face a so-called “scissors gap”: not enough data to train algorithms → harder to compete in automated auctions.

Funnel position matters

AI Mode drives more exploratory, upper-funnel traffic. Conversion rates will naturally be lower than branded or transactional search.

Problems occur when this traffic is evaluated using bottom-of-funnel benchmarks without adjusting KPIs and expectations.

Where to start

You don’t need to rebuild all campaigns at once.

Choose one campaign where user intent is likely more complex than the keyword list suggests. Try restructuring it around user goals rather than search term groupings.

Test broad match in a controlled way. Rewrite at least one landing page so it answers not only “what this is,” but also “why the user needs it,” instead of just listing specifications.

The shift to intent-first is not just a tactic — it’s a new lens. And it’s the most durable way to plan as Google continues introducing new AI-driven ad formats.

Read this article in Ukrainian.