AI is reshaping how PPC campaigns are planned, optimized, and measured. Manual bid strategies and strict keyword lists are giving way to audience signals, dynamic assets, and algorithm-driven delivery. Tools such as Performance Max (PMAX), AI Max, and Demand Gen promise efficiency at scale, but they also introduce new complexities around control, performance, and brand alignment.
At the core of this shift is intent recognition. This refers to a new role of AI, which is to infer what a user truly wants based on behavior across all Google sites, and not just what they type into a search engine. This interpretation of user intent is what drives the AI’s decision to show an ad at all, and if so, which ad will most likely drive user engagement. This is the new world of AI-driven ad campaigns, and it’s one where answers to user’s questions reign supreme.
This guide, a product of the Orange 142 Emerging Channels Council, focuses specifically on how AI is changing Google’s paid search offerings, particularly PMAX, AI Max, and Demand Gen, and what brands and agencies can do to adapt. While generative AI is transforming many marketing channels, this guide is designed to help you respond to one of the most urgent shifts: the restructuring of paid search around AI-powered, intent-driven systems.
We wrote this guide to help brands and agencies understand how to regain control in an environment increasingly governed by AI. While automation has reduced some of the manual complexity of running search campaigns, it has introduced new layers of opacity and risk. Advertisers now need a deeper understanding of how Google’s AI systems work, what signals they prioritize, and how to shape campaigns to align with both user intent and business outcomes.
The Emerging Channels Council serves as a thought leadership body within Orange 142, focusing on educating, guiding, and encouraging independent brands and agencies to experiment and excel in underutilized and innovative channels. Through collaboration, data-driven insights, and practical resources, the council will help Orange 142 clients obtain strategic growth through sustainable practices in digital advertising.
To access all of the Emerging Channels Council resources, please visit: https://orange142.com/emerging-channels-hub
If this topic is new to you, you’ll likely encounter a range of unfamiliar terms, some of which may seem interchangeable. It’s important to understand them, however, as they represent the core building blocks of how generative AI systems interpret, assemble, and deliver content in both organic and paid environments. (If you’re familiar with these terms feel free to move onto the next section.)
Answer-Based Content
Content that directly addresses user questions. Whether it's a web page, an ad, or a product listing, the goal is to anticipate what a user is trying to find out and deliver a clear, immediate response.
The practice of structuring content so that it can be cited, quoted, or summarized in AI-generated results like Google’s AI Overviews. It’s like SEO, but with a sharper focus on clarity, markup, and concise answers.
GEO goes beyond optimizing a single piece of content. It ensures your brand or product is recognized as an entity in the AI’s internal knowledge graph so it becomes part of the answer, not just a source.
When content fulfills user intent directly on the platform without requiring a click-through. That includes both AI summaries in search and paid placements across Google surfaces that deliver value within the feed.
Creative inputs like headlines, product images, and CTAs that are designed to be matched to user intent in real time. These assets are modular, and Google assembles them dynamically to form the “answer” ad.
AI-era keywords. These themes help Google understand what your ad is about and what kinds of intent it should respond to.
Inputs that help steer AI toward the right users: past website visits, CRM uploads, in-market behaviors, and more. These signals inform delivery but don’t control it.
Google’s automated campaign type that runs across Search, YouTube, Display, Shopping, and Discover. Advertisers provide goals, assets, and signals; Google handles the rest.
AI Max is a new campaign setting within Google Search that layers AI enhancements onto traditional keyword-driven campaigns. It interprets search queries more flexibly and uses broader signals, such as search themes, to improve delivery.
Google’s mid- to upper-funnel campaign format for visual, immersive storytelling across YouTube, Discover, and Gmail. It uses creative combinations and intent signals rather than keywords.
Google’s current best-practice recommendation: run PMAX, AI Max, and Demand Gen in tandem to cover the full funnel. It reflects the shift from keyword management to intent orchestration. Winning in this new environment means treating every campaign like an answer, because that’s exactly what users (and the AI) are looking for.
Generative AI is fundamentally reshaping how content is delivered across the web—and how users interact with it.
Organic results are shifting toward AI-generated answers and synthesized summaries, delivered by new platforms such as Google’s AI Overviews, Perplexity, and ChatGPT. These tools don’t just index and rank web pages; they synthesize responses in real time, often answering user questions without requiring a single click. In this new landscape, visibility depends less on page ranks and more on how well your content can be interpreted, cited, and surfaced by AI systems.
A similar shift is underway with PPC campaigns, long the bread-and-butter of building brands.
Traditional PPC campaigns once relied heavily on manual keyword lists and tight campaign structures. With the rise of formats such as Performance Max (PMAX), AI Max, and Demand Gen, Google now prioritizes intent over input. What does that mean in practice?
Rather than relying only on a single action—like someone typing a search into Google—AI now tries to understand what the user really wants by looking at their recent activity across multiple apps and platforms, such as Google Search, YouTube, and Discover.
When there’s an opportunity to show an ad, Google’s AI doesn’t just pick one from a library. It builds one on the fly, using a process called dynamic ad assembly. That means the system selects the headline, image, description, and call-to-action that it thinks will resonate best with that individual user, based on what they’ve been doing and what they seem to care about.
And that has changed the fundamentals of designing and executing PPC campaigns. For instance, keywords still matter, but not in the same way. The new model is about identifying and answering user intent, wherever they are.
Every Campaign is Now an Answer
Winning in an answer-based content environment means anticipating the user’s needs before they even search. Whether organic or paid, your content must be structured and written in a way that helps AI systems recognize its relevance to specific user intents. That way, when the algorithm assembles an ad or chooses content to feature, your brand shows up with something useful, timely, and in line with what the user actually cares about.
For organic content, that means shifting focus from long-form optimization to clarity, structure, and schema that helps AI systems extract answers (which is essentially what GEO is all about). For paid content, it means designing modular creative assets that can be combined and matched to the individual user’s inferred goals. You’re no longer just bidding for placement, you’re bidding to be the best answer in a moment of need.
This shift requires new tools, new metrics, and, most importantly, a new mindset. Here’s a more detailed look at how the fundamentals have shifted:
|
Aspect |
Traditional PPC Campaigns |
AI-Driven Formats |
|
Primary targeting method |
Manual keyword lists and match types directly determine who sees your ad |
Audience signals, search themes, and intent cues help the AI decide when and where to serve ads |
|
Keyword role |
Keywords are the core of campaign logic—each ad is generally triggered by specific matched search terms |
Keywords still matter but act as signals, not fixed triggers; AI considers context, variations, and broader behavioral cues |
|
Campaign structure |
Granular campaign structures built around tightly themed keyword groups and high-value phrases, often segmented by match type and aligned to specific KPIs. |
These campaigns are built around audience intent and personas, using high-quality inputs such as assets, themes, and signals to guide performance. Multiple campaigns may pursue the same goal in parallel, each optimized to play a different role. The system then assembles and delivers ads dynamically based on real-time user context. |
|
Creative Assembly |
Ads are static: you write each version manually |
Ads are modular and dynamic; the platform assembles combinations of headlines, visuals, CTAs based on user behavior and platform predictions |
|
Optimization |
Manual rule-based adjustments (bids, negatives, A/B testing) |
Continuous AI optimization based on engagement, conversion signals, and contextual feedback; uses creative rotation and learning loops |
|
Role of humans |
Hands-on control of every lever; clear lines between decisions and results |
Strategic guidance role: humans influence direction by curating inputs, managing exclusions, and interpreting algorithmic behavior |
|
Control levers |
Match types, negatives, bid strategies, dayparting, ad extensions |
New AI levers: search themes, audience signals, creative assets, exclusions (e.g., content blocks), and brand suitability permissions |
|
Measurement Focus |
CTR, CPC, CPA, conversion rate by keyword or ad group |
Full-funnel metrics including asset performance, cross-channel lift, conversion paths, and intent fulfillment |
|
Primary challenge |
Managing complexity and scale as account structures grow, as well as the ebb and flow of search trends, individual keyword performance and optimization bidding manually. |
Maintaining oversight and guiding automation to avoid misalignment, wasted spend, or irrelevant placements |
|
Winning strategy |
Build tightly themed campaigns with precise control, using honed keyword lists and appropriate match types. Test A/B variations and bid strategies one at a time to isolate what’s working. |
Supply the algorithm with high-quality assets and strategic inputs, then refine based on observed performance and platform behavior |
AI-powered campaigns don’t rely on fixed targeting lists or tightly controlled keywords. Instead, they respond to signals—clues about what the user might be trying to do—drawn from across the Google ecosystem. The system uses context, behavior, and predicted outcomes to make decisions in real time.
To succeed in this environment, marketers and their agencies need to shift from precision targeting to strategic guidance. That means providing inputs that the AI can use to make smart decisions. Those inputs include high-quality audience signals, flexible campaign structures, and clearly defined themes that reflect what users are searching for.
These inputs don’t tell the AI what to do, but they do guide it in important ways. Let’s take a closer look at those signals
In the past, marketers handpicked audiences based on demographics or keywords. In today’s AI-powered formats, the AI decides who to target. But, as a marketer, you can influence that decision by providing the AI model with audience signals. Think of them as nudges that steer the system toward the people most likely to convert, based on your knowledge of your own customers and industry sector.
Signals are suggestions, not actual targeting criteria. Google’s AI uses them to shape delivery, not restrict it. Put another way, the goal is to provide the system it needs to explore beyond your defined lists, while still staying grounded in your best data.
What’s driving the focus on user intent? How does Google predict what a user is really trying to accomplish? It all comes down to AI-driven intent matching, Google’s system for predicting what a user is trying to do based on their behavior, context, and past activity. If your ad is a good fit for that predicted intent, the system may show it to people with similar intent, broadening your reach. This lets Google serve ads based on real-time signals, not just rigid campaign rules, helping advertisers reach people in more relevant, high-intent moments.
Traditional PPC relies on granular campaign structures: tightly themed ad groups, match types, negative keywords, and detailed keyword segmentation. That level of control makes sense when every input must be managed manually. But AI-powered formats such as PMAX are built to learn, adapt, and optimize across a much broader set of signals.
In this new model, structure still matters, it just serves a different purpose. The goal is no longer tight management; its intelligent guidance. Consequently, campaigns should be organized to give the system meaningful signals, not restrict its range.
Here’s what that looks like in practice:
In other words, rather than build 10 near-identical campaigns for each product, audience segment or business goal, we now structure them to teach the AI how to recognize your customers and what they’re looking for. It’s less about spreadsheets and segment labels, and more about feeding the system the right signals so it can learn and improve over time.
Search themes in PMAX allow you to provide Google’s AI with critical insight into what your customers are searching for and which topics lead to conversions for your business. They’re not keywords in the traditional sense. Rather, they’re AI-interpreted topics that help Google understand the purpose and context of your ad.
Search themes help the system:
For example, a campaign promoting travel packages to Santa Fe might include search themes such as:
In short: search themes help the platform associate your ads with relevant search activity, even when the exact query isn’t in your keyword list. They’re one of the simplest and most powerful ways to guide AI-driven campaigns without overengineering your account structure.
Together with audience signals, Search themes give Google’s AI a clearer picture of your goals, without forcing you to control every keyword or audience segment manually.
In today’s AI-driven PPC campaigns, ads are no longer fixed messages tied to specific keywords. Rather, they’re built on intent-based assets. Those assets are modular components of ads, such as headlines, descriptions, images, videos, and calls-to-action. Google’s AI uses these assets to assemble ads dynamically so that they resonate with the users who see them.
Rather than serving a static ad in response to a fixed search query, Google interprets user signals from across its ecosystem, including Search, YouTube, Discover, Maps, and Display, to figure out what the user is trying to do. Based on that inferred intent, it then selects and combines the best-performing assets for that precise moment in time.
Think of it this way: it’s not just about relevance, it’s resonance.
To run an AI-driven PPC campaign, we begin by supplying the platform with:
Leveraging these inputs, the AI chooses when and where to show your ad, and builds the best version of it in real time based on what it thinks will work best.
This model offers powerful benefits. Ads feel more helpful because they’re matched to what the user is actually seeking whether that intent is explicit (e.g. typed into Search) or implicit (implied through recent behavior on YouTube, Maps, or Gmail).
That said, the marketer’s role remains essential. It’s up to brands and agencies to provide high-quality creative, define clear goals, and structure campaigns in ways that support accurate intent matching. Even the best algorithms can only work with the inputs they’re given.
Intent-based assets aren’t just new creative formats. They represent a fundamental shift in how campaigns are built, optimized, and measured, and they’re the key to succeeding with AI-driven campaigns.
Let’s look at some of the key aspects.
You’ve probably heard about the shift from “copy to content.” Nowhere is that more evident than in ad creative. AI engines now favor concise, intent-driven phrasing that answers user questions over vague slogans. In other words, if your brand sells running shoes, ad copy such as “Run the World in Style” will no longer cut it.
High-performing ads deliver clear, immediate relevance. Think:
This type of text is now prioritized because it aligns with how people interact with AI. For instance, a runner dealing with an injury and in-market for new running shoes is likely to ask about the options in full-sentence conversational prompts ("What's the best running shoes for people with bad knees?”).
Responsive Search Ads (RSAs) for Search and Asset Groups for PMAX extend this logic. Instead of writing one static ad, marketers submit a bank of headlines and descriptions. Google’s AI then:
The takeaway: focus less on slogans and more on providing useful, modular inputs that the AI can recombine based on real user behavior.
Further Reading:
In the spirit of usefulness to the end user, AI is eschewing pretty product shots in favor of more "instructional" visuals and videos. Think graphics and videos that demonstrate how products work, compare options, and show real-world applications.
What works best:
Google takes care of distributing your assets across Search, Shopping, YouTube, Discover, and Display through PMAX and Demand Gen.
In this era of AI-driven campaigns, video has become especially critical. Google has made clear that formats answering “how,” “which,” and “why” tend to get the premium placements. This reflects Google’s broader shift toward surfacing content that’s useful to the user.
Further Reading
In an AI-powered ad environment, landing pages do more than convert, they provide the context Google needs to evaluate relevance and match your ads to high-intent users. When landing page content reinforces the same themes as your PMAX intent assets, Google has more signals to determine when your ad is likely to perform well and which users are the best fit. That alignment improves your relevancy score and increases the likelihood of better placements, higher engagement, and more efficient spend.
Today’s landing pages should still guide users toward a clear next step, but they also need to educate, inform, and provide structured, machine-readable content that reinforces your authority on the topic.
Think of your landing pages as comprehensive content destinations, incorporating:
This content-rich approach both improves the user experience, as well as increases the chances that your page will be featured in AI Overviews, featured snippets, and other high-visibility placements across the SERP.
For example, rather than a generic page titled “Visit Vermont,” a smarter approach for the page title would be: “Best Holiday Destinations in New England for Families.”
This page might include:
This type of structured, intent-focused content helps Google’s AI connect your page to relevant searches, even when the query isn’t a perfect match.
CTAs are evolving too. Generic prompts such as “Learn More” or “Buy Now” are being replaced by contextual, intent-driven language, such as:
These calls-to-action reflect the actual questions users are asking, which means they perform better because of it. New AI tools make it easier to generate and test these variations at scale.
Even as AI-powered campaign formats unlock new capabilities, they introduce a different set of challenges for marketers. Success now hinges less on manual controls and more on strategic inputs, creative depth, and ongoing oversight.
The shift to automation doesn’t eliminate complexity, it just changes where that complexity lives. Below are some of the most common pitfalls brands and agencies encounter in today’s AI-driven paid search landscape.
Even in AI-driven campaigns, keywords still matter a great deal; they just play a different role than they do in traditional campaigns. Tools such as Search Themes and Responsive Search Ads let you guide Google’s understanding of what’s important to your brand. If you ignore keyword intent completely, you’re missing one of the clearest signals you can give the system. While AI can go beyond exact matches, it still relies on context to connect user queries to your content. The strongest campaigns strike a balance between automated flexibility and smart keyword strategy.
Sometimes your paid search content fuels Google's AI Overviews without driving any traffic to your site. These zero-click scenarios occur when Google's systems extract key information from a landing page or creative assets and display it directly in the search results. Users get their answer without the need to click, which means your content does the important work of answering a question, but you don’t get the credit. In some cases, your own investment in high-quality, structured content is what makes it easier for Google to answer the query, but just not in a way that benefits your campaign. This is one of the more difficult challenges facing the industry today, but you do have some strategies you can deploy (see the next chapter).
"Set It and Forget It" is a Myth
PMAX and AI Max may feel fully automated, but they require ongoing human guidance. Without oversight, your ads can show in irrelevant placements or waste budget on the wrong audiences. Or, they can appear in placements that violate your brand safety and suitability guidelines. The trick is to provide strong inputs (audience signals, asset quality, exclusions) and actively monitor performance.
PMAX is built to drive performance at scale, but it’s also built to serve Google’s broader goals. The system prioritizes what works for the platform, sometimes at the expense of advertiser nuance.
Google's Best Practices is a one-size-fits-all approach, which is helpful of course. But many optimization levers are either buried deep or entirely hidden from the average user. Advertisers who follow guidance blindly may end up defaulting to settings that maximize spend, not necessarily outcomes.
AI formats need far more creative inputs—headlines, descriptions, videos, images—than traditional campaigns. Many advertisers underestimate how much content is needed, or how quickly creative fatigue sets in.
Speaking of creative fatigue, if advertisers don’t supply enough distinct, intent-aligned assets, performance will suffer. Google won’t show ads that don’t meet relevance thresholds, and stale content gets deprioritized quickly. This especially affects PMAX, which cycles through combinations rapidly.
Search ads have traditionally been focused on the bottom of the funnel, i.e. capturing users who are ready to convert. But campaign types like Performance Max are built to reach users at every stage of the funnel, from initial awareness to final action. That means your asset mix matters more than ever.
If all your assets are conversion-focused (e.g. “Book Now” or “Get a Quote”) you’re missing the opportunity to engage users who are just discovering your brand or weighing their options. To succeed, you need a range of creative assets that align with each funnel stage:
As mentioned previously, AI-driven PPC campaigns will place your ads across the Google ecosystem, including YouTube, Discover, Gmail, Display, and Search. But without careful exclusions, advertisers risk showing up in news, opinion, or low-quality content environments. Many forget to use placement exclusions or negative keyword lists to protect brand integrity.
AI can automate delivery, but it can’t invent strategy. If your campaign goals are vague, your inputs misaligned, or your messaging disconnected from what users actually need, performance will suffer no matter how advanced the platform. AI works best when it has a clear sense of direction.
Success starts with human clarity: What are you trying to achieve? Who are you trying to reach? What action do you want them to take—and why should they care? AI can optimize, but only within the guardrails you set. Without a well-defined strategy, you're just automating waste.
Another common mistake is ending a campaign too early, before the AI has had time to learn and optimize. While traditional campaigns might show early signs of success or failure quickly, AI-driven formats such PMAX require a learning period to understand what works. Pulling the plug too soon can short-circuit performance just as the system is beginning to identify high-value signals. It's important to allow enough time, budget, and data volume for the algorithm to do its job.
Some advertisers struggle with PMAX’s limited visibility into performance breakdowns. The AI decides where and how your ads appear, but doesn't always tell you why. This black-box nature makes it harder to diagnose performance issues or optimize strategically. While Google provides some reporting views (e.g. asset-level, audience signals, campaign-level performance), granular data on placements, queries, and attribution paths is often obscured or aggregated. This can be frustrating for data-driven marketers who are used to full transparency and manual control. Google has also released Channel Reporting for PMAX as well, although it is limited in scope.
In an AI-powered environment, advertisers succeed not by controlling every variable, but by shaping the system’s learning. That means setting clear goals, offering strong signals, developing rich creative assets, and continually pressure-testing outcomes. These best practices help you stay in control, even when the algorithm is doing most of the driving. Here’s an analogy: self-driving cars are very high-tech, packed with the most advanced AI and computer science, but they’re useless until a human provides them with a destination.
Even in campaign formats such as PMAX, keywords still matter. Use Search Themes and supporting ad copy to reinforce key topics to help Google understand what your campaign is about. In short, don’t abandon keyword strategy, just realize how it must evolve.
AI Overviews often resolve the user’s question directly in the search results, reducing the need to visit your site in the process. But a strong brand voice and authority can still earn click-throughs to your site. The key to success is twofold:
So how do you accomplish these tasks? First, start with technical elements: e.g. clear FAQs, schema markup, strong headings, to help you get surfaced in the AI Overviews. Then go deeper. Reinforce your brand with compelling creative, confident messaging, and content that signals there’s more to learn. Think case studies, original insights, real answers. Even if the AI delivers the answer upfront, your brand should be the one users want to learn from next.
AI doesn’t mean hands-off. Regularly review placement reports, update creative, test exclusions, and steer performance. As a marketer, you will always remain in the driver’s seat, and the AI is still just your assistant.
While Google can operate without them, supplying audience signals, such as interests, behaviors, and demographics, helps the AI learn faster and perform better. It’s one of the most direct ways marketers can bring strategic intent into automated campaigns.
AI-powered campaigns need assets that reflect the full customer journey. If all your inputs are bottom-funnel CTAs, the system has nothing to work with at earlier stages. Build a mix of awareness, consideration, and conversion messaging, and organize assets into clear groups. This gives the algorithm the signals it needs to reach users at the right moment, and prevents your campaign from stalling out too soon.
And create more assets than you’ll think you’ll need. AI formats thrive on variety. To avoid creative fatigue, develop a deep bench of headlines, videos, images, and descriptions. Rotate often, refresh intentionally, and plan for high asset volume from the start.
AI-powered campaigns begin learning quickly, often within two to four weeks—but their evolution continues over time. Unlike traditional search, which can take months to fully calibrate, AI formats adapt faster upfront but require ongoing inputs and adjustments to perform at their best. That’s why it’s critical to let campaigns run long enough to stabilize, rather than pulling the plug too early. Set clear benchmarks, monitor for signs of optimization, and give the system room to learn and improve.
AI-powered campaigns cast a wide net across channels, including YouTube, Discover, Gmail, and the Display Network. This means there is a risk that ads could appear alongside content you consider unsuitable for your brand. But with proper guardrails you can avoid this risk. Use placement exclusions, negative keyword lists, and brand safety controls to steer ads away from inappropriate or off-brand content.
#8. Don’t Confuse Automation with Strategy
Automation can enhance execution, but it’s not a substitute for strategy. AI will optimize toward the signals it’s given, but it won’t decide your goals, craft your messaging, or align assets to audience needs. Without a clear strategic foundation, target outcomes, meaningful inputs, and well-structured campaigns, automation can easily veer off course. Great results still start with smart planning.
#9. Work With a Strategic Partner or Agency
Platform defaults are built to serve the widest range of advertisers, not the specific needs of your brand or industry. A strategic partner can uncover buried settings, fine-tune inputs, and align the campaign with your actual business goals. They’ll pressure-test recommendations, flag irrelevant placements, and adapt creative strategy based on how your audience behaves, not just how the platform performs in aggregate. What’s more, an experienced agency will be innovating strategy and developing “secret sauce” to help drive campaign performance. At the end of the day, an agency shouldn’t just launch campaigns, its teams will translate platform mechanics into brand advantage.
AI-driven formats like PMAX don’t always reveal why or where your ads are being served, but that doesn’t mean you should fly blindly. Push for asset-level performance, audience signal breakdowns, and conversion path insights. When standard reports fall short, layer on your own analysis or tools to fill the gaps. The more visibility you create, the smarter your optimizations will be.
Orange 142 helps brands and agencies succeed in today’s AI-powered search landscape by combining strategic oversight, industry expertise, and proprietary innovation.
We manage campaigns across Paid Search, Shopping, Performance Max, Demand Gen, and AI Max with a hands-on, strategic approach that adapts to the nuances of each format. Our team understands how automation works, as well as how to shape it, because we don’t just use the tools, we help influence them; in fact, Google has invited us to provide feedback on the direction of its evolving ad products.
We bring deep industry knowledge across verticals, applying insights from each sector to optimize each campaign’s structure, signals, and creative strategy. Our teams continuously test and refine proprietary approaches that help steer the algorithm toward outcomes that matter, not just clicks, but meaningful conversions and establishing brand authority.
In an era when creative fatigue sets in quickly, we’re built to keep up. Orange 142 has in-house teams dedicated to producing and refreshing campaign assets (headlines, descriptions, videos, images, CTAs) at the speed AI demands. We map those assets to the user journey, ensuring performance at every stage of the funnel.
We monitor campaign performance daily, adjust strategies in real time, and provide full transparency through real-time dashboards and wrap-up reporting. If a campaign underperforms, we troubleshoot fast. If it succeeds, we scale it with precision. Our focus is always the same: results, not just reach.
When you partner with Orange 142, you get more than execution. You get a team that understands how to guide AI with intent, align strategy with creative, and turn platform automation into performance you can trust.
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