AI Max has shaken up the PPC world, and for many it feels like another clear signal that Google is doubling down on an AI-powered future. Search behaviour is shifting quickly, and the pace of change is starting to show up not just in product updates, but in the way people actually search.
Last week’s webinar was an exciting look into the future of Search, with Ginny Marvin, Product Lead at Google, sharing deeper insight into the role AI Max is set to play. She started by highlighting a series of statistics that show just how quickly behaviour is changing. Over the past year, around 70% of queries have become more conversational and long-tail, with an 800% increase in conversational searches made up of eight words or more. Search is also moving beyond text, with Lens searches growing 65% year-on-year and Google referencing 100 billion visual searches. Together, these trends set the scene for why AI Max is becoming such a critical part of Search’s evolution towards a more complex future.
With that in mind, this webinar came to answer this million-dollar question: what role does search play in the future, and where does AI Max fit within it?
What makes AI Max the future?
Google sees this as the future because the way people search no longer fits neatly into the old keyword-in, link-out model.
User behaviour has shifted towards more conversational, exploratory, and intent-led searches. People are asking longer questions, using follow-ups, switching between text, voice, and images, and expecting Google to understand what they mean rather than exactly what they type. AI is the only realistic way to interpret that level of nuance at scale, which is why products like AI Max exist in the first place.
From Google’s perspective, AI also helps close the gap between intent and outcome. Instead of advertisers trying to anticipate every possible query, AI Max allows Google to use signals from queries, content, and behaviour to expand coverage into relevant searches that would previously have been missed. That means better user experiences, more relevant ads, and, conveniently, more opportunities for advertisers to drive conversions without manually building thousands of keywords.
There is also a structural shift happening in Search itself. With AI Overviews and AI Mode, Search is becoming less about a list of blue links and more about answers, journeys, and decision-making within the SERP. For Google to monetise that future responsibly, ads need to feel helpful and well-matched to intent. AI-powered systems make that possible in a way traditional keyword-based approaches simply can’t.
Measuring What Matters in an AI-First Search World
As Search becomes more AI-led, the way we measure success needs to evolve alongside it. Keywords still matter, but they are no longer a literal targeting mechanism. Instead, they act as intent signals, helping Google understand the type of user you want to reach rather than the exact query you want to appear for. This means performance analysis should focus less on individual keyword control and more on whether the right intent is being captured.
Match types are a big part of this shift. Broad match has changed significantly in recent years and, when paired with Smart Bidding, is often where AI-driven performance unlocks. With AI Max and similar products, we are seeing a clear movement towards keywordless expansion and broader query coverage, where success is defined by incremental value rather than perfect query matching. Reviewing search terms with an open mind becomes critical, not just to exclude waste, but to understand where AI is finding new opportunities.
That said, structure still plays an important role. Well-themed ad groups help Google learn intent from keyword context, ad copy, and landing pages working together. While unexpected matches will happen, stronger theming improves the quality of intent signals and gives you more confidence that expansion is happening in the right direction. The goal is not to remove structure, but to make it work harder as a guide for AI, rather than a set of rigid rules.
Rethinking Overlap and Focusing on what Actually Matters
One concern that naturally comes up with AI Max is overlap. With Search, Performance Max, and AI-driven formats all expanding coverage, it’s easy to worry about campaigns competing with each other. Ginny addressed this directly, and the message was clear: overlap shouldn’t be the primary focus.
The bigger mindset shift is moving away from obsessing over campaign types and towards thinking in terms of business goals. The question becomes less “which campaign won the click?” and more “which channel is best placed to deliver the outcome we care about?” In an AI-led setup, Google is optimising across signals and intent, not neatly defined campaign boundaries, and measurement needs to reflect that reality.
Ginny also hinted at where this is heading longer term, with Search moving towards a more agentic experience. That means systems that don’t just respond to queries, but actively help users move through decisions and actions. In that world, worrying too much about internal overlap risks missing the point. The real priority is choosing the right levers, setting clear objectives, and letting each channel play the role it’s best suited for as Search continues to evolve.
Our takeaways
AI Max is not just another product update, it is a signal of how Search is evolving. With queries becoming more conversational, more visual, and more intent-led, Google needs AI to interpret nuance at scale and keep ads relevant within experiences like AI Overviews and AI Mode. The stats Ginny Marvin shared underline that this shift is already happening, and AI Max is Google’s way of aligning Search campaigns with where user behaviour is heading.
For marketers, the takeaway is a mindset shift: focus less on rigid keyword control or worrying about overlap, and more on clear business goals, strong intent signals, and measurement that reflects incremental value. Keep campaigns well-structured so the system learns from context, test proactively if you rely on legacy approaches, and use learnings to guide decisions rather than chase perfect coverage. In a world moving towards more agentic search experiences, the winners will be the teams that adapt early and optimise for outcomes, not mechanics.