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Today, the most revealing search query may be the follow-up. The question people ask after they learn, compare, or reconsider often reveals what they actually need.
For brands, that next question represents a valuable opportunity to earn visibility, secure trust, and support a meaningful action. The objective isn’t to predict every possible prompt. It’s to understand the audience well enough that your content, evidence, and experience remain useful as the conversation evolves.
Traditional SEO strategy has focused primarily on the query that brings someone to a result. But what happens after the first answer is delivered?
A user can now begin with a broad question, narrow the request, add a photo, ask for a comparison, and move toward an action without restarting the search process. The follow-up query is the connective tissue in that journey.
This dynamic more closely mirrors how people actually think. We rarely begin with a perfectly formed question. We learn, reconsider what matters, add context, and proceed down the rabbit hole.
The biggest change isn’t that people suddenly began asking related questions. It’s that the interface can now remember the relationship between them.
Traditional SEO best practices still matter. The strategic workflow has simply expanded. Instead of treating a single query and landing page as the whole assignment, brands must consider the first question, likely follow-ups, credible proof, and the next useful action.
The goal is simple: Help people answer the question they have now and make it easy to answer the question they’re likely to have next.
See where your brand appears in AI search, where competitors are winning, and what it takes to become the answer AI recommends.
Conversational search allows people to ask natural questions, carry context from one request to the next, and refine their needs along the way. It can happen in a search engine, AI assistant, voice interface, on-site chatbot, shopping assistant, or visual search tool.
Three qualities distinguish conversational search from conventional search:
Not every voice query or AI summary is conversational. The defining question is: Can the person continue the task without rebuilding the context?
Conversational search also overlaps with personalization, but the two aren’t identical. Personalization changes an answer based on what a system knows about the individual.
Conversational search changes an answer based on what the individual reveals during the exchange. Increasingly, search systems combine both forms of context to deliver more individualized responses.
Here’s a simple example:
Traditional keyword research might stop at “best carry-on luggage.” Conversational strategy follows the full decision arc. Specifications, comparisons, images, policies, inventory, delivery data, and expert guidance all have a role to play.
Conversational search didn’t begin with ChatGPT or other AI platforms. Its building blocks developed over decades alongside changing user behavior.
Early web search encouraged short noun phrases that were often stripped of natural grammar. If the results missed the central need, users manually reformulated the search: “running shoes,” then “running shoes flat feet,” then “best stability running shoes women.”
The user carried the context between searches. SEO centered on keyword matching and landing pages built around primary phrases.
Search engines gradually improved at understanding entities, relationships, intent, and natural phrasing. Google’s 2019 BERT announcement emphasized the context and relationships among words in a query. Small terms such as “to,” “for,” and “no” could materially change intent.
For SEO, that shift reduced the value of repetitive exact-match language and increased the importance of satisfying underlying needs.
Voice assistants normalized complete questions and concise spoken answers. They also introduced local, immediate, and hands-free situations. Related searches could have been “Where is the nearest pharmacy open now?” or “How long do I bake salmon at 400 degrees?”
Many voice interactions remained single-turn, but the lasting lesson was clear: Provide concise answers, accurate facts, and content that works when heard rather than read on a page.
Google’s 2021 MUM announcement framed complex tasks as journeys that could require multiple searches. In 2022, Lens multisearch enabled people to combine an image with text such as a color, attribute, or question.
The direction was already clear: Let people authentically express their needs while the system performs more of the work behind the scenes.
Generative AI can interpret natural language, retrieve up-to-date information, combine sources, and retain context within a single interface. That dynamic changes both how people express their needs and how platforms search on their behalf.
Google says AI Overviews and AI Mode may use query fan-out, running multiple related searches across subtopics and data sources. For example, a lawn care question may prompt research into treatment, prevention, safety, cost, climate, and timing.
The visible prompt doesn’t tell the whole story. A page can support part of an answer even when it doesn’t mirror the wording of the initial question. Keyword datasets still reveal demand, but they represent only part of the picture. Support questions, on-site searches, reviews, sales conversations, and prompt testing can expose the needs that come next.
Dig deeper: Query fan-out optimization guide: How to rank in AI searches
Google has made the shift visible by connecting follow-up questions in AI Overviews to a continuing conversation in AI Mode. ChatGPT search similarly blends conversational responses with timely web information and source links.
On a practical level, people can reveal more information with every turn:
Each question changes the best answer. A page that handles only the definition may support the opening request and disappear from the rest of the journey. The initial query identifies the topic. The follow-ups reveal what truly matters: budget, risk, location, use case, or deadline.
This shift is already affecting the search interface. In January, Barry Schwartz documented how follow-up questions can move users directly from AI Overviews into AI Mode, a feature that Google later pushed worldwide across desktop and mobile.
People don’t need to translate everything they see into keywords. They can show a system an object, screen, plant, product, room, or broken part and ask a direct question.
In May 2025, Google reported that Lens handles more than 25 billion queries per month. Search Live has further expanded this behavior by allowing people to discuss a live camera view and ask free-flowing follow-ups.
For brands, visual SEO can’t stop at filenames and alt text. The image or video must be useful. Clear angles, close-ups, scale, labels, captions, demonstrations, and transcripts can help a person and a search system understand what the visual is trying to prove.
A photograph showing exactly where a reset button sits on an appliance is more useful than a polished lifestyle image of the same appliance.
Dig deeper: How multimodal discovery is redefining SEO in the AI era
Conversational systems increasingly connect research with execution. Search experiences can already assist with tickets, reservations, local appointments, shopping, and forms.
As agentic capabilities develop, accurate availability, pricing, policies, product details, and accessible conversion paths become an increasingly important part of discoverability.
A great article can’t rescue inaccurate inventory or a broken booking flow.
Visits containing a Google AI summary resulted in a traditional result click 8% of the time, compared with 15% when no AI summary appeared, per a Pew Research Center study. Links inside the summaries received clicks in only 1% of visits.
One study can’t provide a universal CTR forecast, but the direction matters. A brand can influence a decision without receiving a visit. The clicks that remain may also represent a later, more qualified need.
When an AI response covers the basics, people need a stronger reason to click. They may want to verify a claim, see the original demonstration, use a tool, join a community, or check current availability. The click increasingly means: “Give me the proof, experience, or utility the summary can’t.”
That dynamic elevates:
If an AI answer can repeat everything on the page, the page needs to offer something more.
Increased automation can make human judgment feel more valuable. People don’t require a person to handle every interaction, but they do want to feel understood rather than processed — especially when nuance, emotion, or risk enters the conversation.
Good self-service respects people’s time. It should answer routine questions clearly, admit uncertainty, and provide a direct path to a knowledgeable human when empathy or accountability matters.
Search isn’t only an acquisition channel delivering anonymous traffic. It can be the beginning of a relationship that continues through a useful tool, newsletter, expert response, or community. That relationship can begin before the click and deepen after it.
This principle has guided my work in news SEO. The first search often identifies the event, while the next wave of questions reveals what readers actually need. For instance:
The opportunity isn’t to publish a thin article for every variation. It’s to build a connected resource that can serve the evolving story.
This change can feel bigger than it needs to be. We must always remember: Simple isn’t stupid when it comes to audience engagement.
We don’t need to predict every prompt or rebuild an entire content program overnight. We need to listen more closely, connect related needs, and make our best information easier to find, understand, trust, and use.
Start with the audience journey, not the newest AI feature. Platforms will change. The need to be useful, credible, and genuinely responsive won’t.
Choose a high-value audience need and brainstorm what a person would naturally ask next. Map at least five types of follow-up:
Use search data, support tickets, sales calls, reviews, community discussions, and prompt testing to your advantage, but keep the order straight. AI can suggest questions. Human behavior should tell us whether those questions matter.
Conversation maps should also reflect how different audiences express the same need. Test important journeys with native-language experts, accounting for regional phrasing, cultural context, and moments when people switch languages during an exchange. Translation alone may not reveal the same follow-up intent.
Not sure where to begin? Take five real user questions and write the most likely follow-up under each one. With that framework, you already have the foundation of a conversation map.
Create a strong hub around the main decision and connect it to a small number of useful supporting resources. These might include:
Don’t publish 30 weak articles that say nearly the same thing. Create one excellent explanation and support it with assets that serve distinct needs.
Internal links should follow the user’s questions, not just mirror the site’s organizational chart. On the anchor text front, “compare plans,” “check compatibility,” and “understand the risks” provide more direction than “learn more.”
Decide whether each follow-up requires a section, separate page, reusable component, or no new asset at all. Let the task (not a minor keyword variation) drive that decision.
Dig deeper: How to build topic clusters that AI will cite
State the answer first, then provide the evidence behind it. When the answer depends on specific circumstances, explain those conditions and include the relevant source, date, test method, or limitation.
The stronger version gives a search system a clear, well-supported answer to assess and potentially cite. More importantly, it tells a person what the claim means and when it may not apply.
Before publishing, always ask: “What evidence would a skeptical reader need to believe this?”
Keep important information in crawlable text even when a video, graphic, or app provides the richer experience. Use descriptive headings, concise definitions, and logical sections to help users extract information more easily.
Maintain the fundamentals:
There’s no secret AI markup. Google says AI Overviews and AI Mode don’t require special optimization. Search systems can’t confidently use what they can’t access or understand.
Test whether people can continue the journey across voice, keyboard, screen-reader, and visual interfaces. Clear headings, descriptive alt text, captions, transcripts, accessible forms, and understandable error messages do more than satisfy technical requirements. They determine who can participate in the conversation.
For every important visual, ask: “What can this answer that text can’t?”
A product image can show scale and fit. A repair video can demonstrate motion and sequence. A safety graphic can clarify a warning sign. Captions, transcripts, and expert reviews can provide context the visual can’t carry alone.
Avoid decorative imagery that adds no information. Use descriptive filenames and alt text for accessibility without keyword stuffing. Place visuals near the relevant explanation and provide stable pages where they can be discovered.
Could someone learn, compare, or complete a step from this visual? If not, reconsider whether it’s necessary.
Dig deeper: Your images have a new job in AI search
When someone arrives from an AI answer, assume they may already know the basics. Provide the next layer immediately:
On-site search and chat should preserve useful context, cite source material, offer escalation routes, and admit uncertainty. Don’t send a well-informed visitor back to the beginning of the discovery process.
Preserve only the context needed to reduce repetition, not every detail a person shares. Sensitive information requires clear consent, limited retention, and an obvious path to human help.
AI systems often synthesize multiple sources. Generic prose is easy to replace, but distinctive evidence and recognizable expertise aren’t.
Build assets people can associate with your brand: a named dataset, annual benchmark, expert rubric, original test, or trusted decision tool. Newsletters, alerts, memberships, proprietary tools, events, and communities can then reduce dependence on any single interface.
Search value increasingly incorporates visibility, trust, and direct audience relationships — not only the immediate click.
Dig deeper: Utility news content: How to win beyond clicks in AI search
SEO and content teams can’t deliver the full journey alone. Conversational search touches:
The strongest strategy synthesizes these functions around the audience’s decision-making process rather than forcing each individual to navigate the organization’s internal silos.
Follow this practical workflow for teams:
No single metric tells the whole story of conversational search. Begin with four questions:
Treat AI citation testing as directional, not as definitive market share. Outputs vary by interface, conversation history, time, location, account context, and model.
Document your definitions and maintain a consistent prompt sample before comparing periods.
Dig deeper: How to measure prompt-level visibility in AI search
The next evolution is likely to move from conversational answers toward conversational action. A person may have a broken appliance, troubleshoot it, compare replacements, check local stock, and book installation within one search session.
As AI agents compare, filter, schedule, and transact under user direction, accurate prices, clear policies, consistent product information, accessible workflows, and source transparency will become even more important.
Original evidence, named expertise, clear methods, and current source pages will help people verify what they receive. Don’t chase every interface change or create thin content for every prompt variation. The goal is to add value, not volume.
Discovery will remain distributed across search engines, AI assistants, social video, communities, commerce platforms, and brand-owned experiences. The goal isn’t to put all of your search strategy eggs into one chatbot’s basket. It’s to become the clearest, most useful, and verifiable source for the decisions your audience needs to make.
Track your visibility across AI search, uncover missed opportunities, and grow your presence where customers are asking questions.
The follow-up query changes SEO because it exposes the full shape of intent. People don’t experience their needs as isolated keywords. They learn, revise, compare, validate, and act.
We don’t need to predict every sentence someone might type. We need to understand the decision journey well enough that our content, evidence, visuals, data, and experience remain useful as the conversation changes. That means creating useful content rather than commodity AI-generated drafts, keeping important information accessible, and never fabricating firsthand experience, reviews, or citations.
The brands that stand out won’t simply answer the first question. They’ll listen, adapt, and earn the next one — proving that even as search becomes more automated, lasting authority is still built through useful information, credible evidence, and genuine human connection.
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Prospective patients no longer stop at a Google results page. They ask ChatGPT, Gemini, Perplexity, and Google AI Overviews for […]
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A LASIK surgeon becomes visible in AI search by doing four things well: making the practice a machine-resolvable entity, publishing […]
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Type your own name and specialty into ChatGPT. If a competitor’s name comes back and yours doesn’t, you’ve already met […]
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Search isn’t shrinking, but its role in the purchase funnel is changing.
People are searching in fundamentally different ways, using cameras, conversations, and keywords to find what they want. At the same time, discovery is happening before many searches, as recommendation engines curate content and help people develop preferences before they ever type a query.
The searches marketers see may be less about discovering what to buy and more about confirming a decision that’s already taking shape. Those earlier stages are difficult to observe, so the activity that helped someone develop a preference can disappear from the purchase path.
A few weeks ago, I was on a walk in my neighborhood, enjoying a Pacific Northwest summer day. Along my usual route, I noticed a beautiful plant that I loved but didn’t recognize. Just a few years ago, I would have had to return home, type a clumsy description like “purple flower Northwest beautiful” into a search engine, and then scroll through dozens of pictures before finding the plant’s name.
This time, I simply pointed my phone camera at the plant, had Google Lens identify it for me, and then bought one at my local plant store the next day. No ad. No search bar. Just a camera and an answer.
Nothing about my experience is unusual anymore, and it should change how every digital marketer views search’s role in the purchase funnel.
The demand generation funnel has become flatter, noisier, and more complicated. The touchpoints have sorted themselves into a consistent sequence.
The traditional funnel was often described as a straight line from awareness → interest → desire → action, with search activity doing the most work in the interest stage, when people were still figuring out what they wanted.
Now the funnel looks something like passive exposure → preference development → confirmation searches → purchase. Search is still a critical component of a buying decision, but it plays a smaller role in discovery.
That job has shifted to recommendation engines that can predict user intent and curate personalized content before the first query. By the time searches happen, they’re confirming a decision that’s already been made.
I see this play out in client accounts. The queries that are closest to purchase look more like caveman language, i.e., “brand name product model,” indicating that consumers know exactly what they’re looking for when it’s finally time to buy.
The catch here is that the first two stages of the new funnel are ones we can’t really observe. If someone watches a series of videos on social media, develops an interest in a product, and then conducts a Google search before purchase, that activity will appear to be a branded search conversion. The content that helped them form their decision never appears in the purchase path.
Consequently, most marketers still budget as if search does the discovery work because that’s what their data tells them.
Track, grow, and measure your visibility across Google, AI search, social, local, and every channel that influences buying decisions.
As search plays a different role in the funnel, YouTube is taking on a larger role in discovery. Two things make it different: machine trust and human trust.
Creators on YouTube also supply social proof, which now plays a critical role in purchasing decisions, particularly among younger buyers. Up to 70% of Gen Z follow an influencer, and 44% made a purchase based on an influencer’s recommendation.
YouTube enjoys the unique position of being highly trusted by both consumers and the machines making recommendations, and Google has taken steps to ensure the platform gets credit for its role in discovery. In January, it rolled out the Attributed Branded Search metric, which enables marketers to link YouTube ad exposure to a branded search within a specific timeframe.
ABS only captures exposure to YouTube advertising. The organic layer featuring review videos, product unboxings, and comparison videos remains invisible. If the measurable portion is driving tangible results, imagine what the rest of the channel is doing.
Dig deeper: YouTube is no longer optional for SEO in the age of AI Overviews
This huge invisible discovery layer extends well beyond YouTube. Much of that discovery happens outside the channels brands control.
AI tools don’t repeat your version of the brand. They repeat what the entire web says about you. Earned media drove 84% of AI citations, according to a Muck Rack review of 25 million ChatGPT, Claude, and Gemini responses across multiple industries.
Those third-party mentions, YouTube review videos, and Reddit threads form the raw material for the AI answers and feed recommendations that drive today’s purchasing decisions.
The practical outcome of this shift is that brand reputation management and search visibility have merged into a single role.
Dig deeper: Why social search visibility is the next evolution of discoverability
Brands still have an enormous role to play in driving discovery. However, those activities must extend beyond paid advertising. Here are three ways you can influence discovery:
Providing consistent and complete information across channels will give algorithms and AI tools something reliable to work with.
A reshaped funnel shouldn’t be the reason you pull back from search. Google query volume reached an all-time high in early 2026 even as search’s position in the funnel changed.
The mistake won’t come from overinvesting in search. Instead, the mistake will come from expecting search to accomplish something it’s no longer suited for.
Rather than retreating from search, this is an opportunity to reimagine your marketing mix within a new framework. If search now occupies a different funnel position, how can you allocate your marketing resources to cover the upper-funnel work that still must be done?
The answer to this question will be different for every marketing team, but it’s a conversation that everyone should be having right now.
A few years ago, I never would have imagined that I could search for a plant’s name with a camera built into my phone, much less have a back-and-forth conversation with my computer. But here we are.
Marketers are working on the cutting edge, watching new technology change user behavior right before our eyes. Everything could shift again by this time next year. Nobody knows for sure. But the larger trend is clear: people are increasingly searching to confirm what they’ve already decided.
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After working on my hreflang XML sitemap project with Gemini, my company decided to invest in paid Claude accounts. I took the opportunity to see what else Claude could do through its desktop app and found some great uses for it.
Here are five ways I’ve been using it in my everyday SEO work.
The first thing I set up was a daily intelligence brief. Using lessons learned from other AI tools, I crafted a detailed outline of the types of news I wanted to see, including industry updates, competitor news, mergers, and acquisitions.
The key to getting more useful information and less fluff is to be specific and use the output as a guide for improvement. Don’t accept schlock — you’re paying for the tool, so teach it to do what you need.
For instance, I have Gemini checking atmospheric conditions for radio wave propagation in my amateur radio hobby, and it took several adjustments to get useful output.
Dig deeper: How to turn Claude Code into your SEO command center
See where your brand appears in AI search, where competitors are winning, and what it takes to become the answer AI recommends.
Yes, I can dig into Google Analytics and analyze the data, but how much faster would it be if I could just ask Claude to look at the data and answer questions for me? It turns out it’s much faster.
The folks at Rank Math shared instructions on how to make a direct connection without using third-party middleware. This tip has saved me a lot of time by allowing me to ask for what I need and quickly get an answer.
I decided to see how Claude would tackle the hreflang XML sitemap project. Like I did in my Gemini example, I told it what I wanted, fed it the latest versions of the sitemaps, and asked how it would approach the problem.
Claude surprised me by asking only for links to XML sitemaps as input. Using its own resources, Claude gathered all the pages and built the sitemap for me without any further intervention.
I double-checked the output and found it to be excellent on the first go, avoiding the need for the back and forth I experienced with Gemini — a huge time saver.
Our team supports three main businesses across several regions. This requires content updates to be replicated across websites, translated, and localized as appropriate. We need to track this work as it goes through the pipeline so nothing falls through the cracks.
This is where Claude has been a tremendous benefit. I took an example of content that had been generated, vetted, and approved by one of our in-house experts and decided to see if Claude could handle automating some of the international SEO process. The results were far beyond my expectations.
As usual, beginning with the end in mind, I uploaded examples of previous translation and localization work to show what I needed done. Next, I uploaded a new item and gave it specific instructions:
I was pleasantly surprised by how quickly Claude was able to do this. It accomplished everything I needed except uploading the document to the ticket. The translations were on par with what we were getting with Google Translate, as verified by our in-country experts.
Strike another blow against inefficiency.
Dig deeper: Turn your SEO process into AI-powered tools
While setting up a new WordPress website for one of our businesses, I found that one of the plugins we were using hadn’t been updated in several years and had known security issues. Thankfully, other security measures in place significantly mitigated the risk, but it’s always better to fix the problem than rely on other means.
I found a suitable substitute and thought about how long it would take my team and me to go through the 20+ websites we support and update the plugins. Even if it only took an hour per website, that’s more than 20 hours of dedicated, hands-on, fingers-to-keys work.
Before starting the process, I asked Claude if it could do it for me. Again, I was pleasantly surprised.
Using Claude’s Chrome extension, I logged into a staging environment for one of our smaller websites and told Claude what to do. It found all the places the plugin was being used, swapped it out with the new plugin, and did a quick visual check. There was one small discrepancy with the first attempt, which was quickly fixed when I pointed out the problem to Claude.
“Would you like me to check the other plugins for any security issues?” it asked when completed. Of course I would. It found an additional plugin that was unused and no longer supported. That was quickly deactivated and deleted.
My only time requirement was about five minutes per site of hands-on work as I:
Everything went on in the background as I worked on other tasks, and I didn’t have to get anyone else from my team involved.
I bet you’d be willing to trade getting the same result from five minutes of work vs. an hour any day of the week.
Track your visibility across AI search, uncover missed opportunities, and grow your presence where customers are asking questions.
AI tools can feel overwhelming, but they’re an incredibly useful asset when used properly. Follow these practical guidelines when implementing AI, and you’ll be blazing through those tedious SEO tasks in no time.
Don’t just demand a final product. Discuss the architecture, the edge cases, and the logic first, and be specific. Treat the AI like an intern by showing examples of final products when possible.
Not all AIs are alike, and some are better at some tasks than others. Read, learn, and experiment. There are a lot of us out there using AI tools, and people have found some amazing results using one platform that completely failed on another.
Much like embracing the iterative process I referred to in my last article about using AI tools, don’t be afraid to check thoroughly and correct when needed.
The reality that AI is really still in its infancy becomes readily apparent when it fails. You may have to spoon-feed information at first to get the result you want. Remember, you don’t have to be polite — be direct and clear for faster results.
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AI Overviews reshaped the search results page, yet most paid search playbooks still run as if nothing happened.
You still hear the same default rule: bid up where performance looks strong, bid down where it looks weak. That assumes every query is the same kind of opportunity, a small auction for the same kind of click. In B2B and industrial accounts, where sales cycles run for months, conversion data is thin, and much of the demand is informational, that assumption falls apart.
When Google answers a question directly on the results page, the value of a paid click varies across your keywords.
Those are three different problems, and a single bid lever can’t solve all three. Trying to is how accounts spend budget on the wrong surface. I separate them into three buckets: fight, influence, and generate demand.
Each bucket describes a different mechanism on the results page, so each one calls for a different approach.
Fight is for bottom-of-funnel queries where intent is clearly commercial, and a click still turns into direct leads and sales: product + modifier, supplier shortlists, “buy,” “quote,” “distributor,” and brand terms combined with buying intent. Here, the job hasn’t changed much. You want to win the auction that sits above the answer, and you want the ad itself to answer the buying question.
This is where paid competition concentrates, and the data show it. The likelihood of an ad appearing rose steadily with cost per click, according to a July SE Ranking study of commercial queries in Google’s U.S. AI Mode:
AI Mode isn’t the same surface as AI Overviews, but what they share is the pattern: paid inventory follows commercial weight. Where the market already pays more for a click, AI-era ad surfaces appear more often. You rarely compete alone, since about seven in 10 answers with an ad in that study showed two advertisers in the block.
Fight is expensive for a reason, so treat it that way.
What you shouldn’t do is pour Fight budget into informational queries the Overview has already answered.
Uncover the keywords, ads, landing pages, and strategies driving your competitors’ paid search success—and find your next opportunity to outperform them.
Influence is for queries where the Overview, or a longer conversational answer, does the educating. Very often, the user won’t click.
The goal here isn’t to gain a cheap visit anymore. It’s to be part of how the answer gets built: cited as a source or presented as one of the brands named inside the answer.
As Shashi Thakur put it at Google Marketing Live EMEA 2026, the best ads must be answers. In an Influence query, your ad brief starts from the question the user is really asking, rather than from a keyword list, and it means accepting that SEO and PPC address the same challenge, not competing against each other.
In B2B and industrial accounts, the queries that trigger AI answer blocks are still mostly informational. In one of our accounts, the intent split of the terms surfacing these blocks looked like this:
| Intent | Share |
| Informational | 83.3% |
| Learn and solve | 10.0% |
| Commercial | 6.7% |
For “learn and solve” and some “informational” terms, plan for share of the answer instead of maximum CPC.
There is a budget consequence worth stating plainly. On influence queries, we work very closely with SEO, and we’re usually not interested in paying to appear where organic is already winning. Being visible twice, in paid and in the answer, barely happens anyway.
When the data pushes us to move money into the Fight and Generate demand buckets, we deliberately avoid being aggressive on terms where SEO already earns the visibility. That keeps paid spend pointed at the gaps we’re interested in covering.
Generate demand is for queries where zero-click has already won and paid search alone won’t rebuild the top of the funnel. Informational demand that used to create assisted journeys now often ends on Google, with no visit to anyone.
You have two options. You can:
This work is about recognizing that some keywords have stopped being capture channels and have become a signal of a demand gap. The capture still happens later, in Fight, once the person is ready to act.
Generate demand fills the pool that Fight then fishes from. Measured only on last-click Search ROAS, this work always looks weak because its payoff appears as brand searches and better-qualified pipeline weeks later.
| Bucket | The job | The paid response |
| Fight | Clear purchase intent, where the click still feeds the sales pipeline directly | Pay for absolute top when your brand isn’t already winning the answer box |
| Influence | Top and mid-funnel, where the answer shapes consideration | Win share of the answer through citation and SEO coordination, not raw CPC |
| Generate demand | Zero-click is winning and Search alone can’t rebuild the funnel | Build demand before the search, through Demand Gen, YouTube, and awareness |
Dig deeper: What happens when AI Overviews contradict paid search ads?
AI Overviews sit at the top of the page, sharing space that used to belong to ads and organic results. The answer often satisfies the user before they scroll, changing the economics of paid search in three ways.
When users get a usable answer without leaving Google, both organic and paid lose clicks. The effect is stronger when your brand isn’t mentioned in the AI Overview.
The clicks that remain tend to be better qualified, but the drop in volume usually exceeds the lift in quality, so total conversions decline.
There are fewer premium positions at the top. More advertisers are shifting their budgets to paid search to offset lost visibility.
The pressure is strongest in B2B and services, and on comparative queries such as “best X” or “X reviews.” Pure transactional queries hold up better because users still need to click through to buy or request a quote.
There are often only a few useful slots above the AI Overview or AI Mode answer, and falling below the Overview can sharply reduce click-through, especially on mobile. Absolute Top impression share and impression share lost to rank become especially useful for understanding that pressure.
Underneath these changes is a shift in behavior. People can research and decide within a single answer experience, shortening the window to capture and persuade them. Intent still lives in your account, but the economics attached to each type of intent have changed. That’s why you should classify a query before you touch the bid.
Dig deeper: 4 strategic paid search pivots to survive Google’s AI Overviews
The framework only pays off if your account can distinguish between valuable and low-value traffic, so two foundations come first.
One is semantic coherence: keyword, ad, and landing page as a single unit of meaning, with each ad group built around one clear concept. Mixing concepts teaches the system the wrong matches, and that gets worse the moment you hand control to broad match, Performance Max, or AI Max.
The other is conversion quality: primary conversions that reflect real business value, lead scoring, and offline or CRM signals wherever you can pass them back. Smart Bidding and the AI surfaces amplify whatever you give them, so thin data makes every bucket noisier.
With those in place, classification becomes a routine you can repeat:
| Step | Action | Data Sources | Output |
| 1. Map intent | Pull search terms and classify by intent type. | Google Ads keywords and search terms (+ Search Console). | Terms grouped by: commercial, research, informational. |
| 2. Identify AI presence | Check where AI Overviews appear and whether your brand is cited. | AI Overview reports, manual SERP checks. | Map of AI Overview presence by term. |
| 3. Sort into buckets | Assign each term to a strategic bucket. | Intent mapping + AI presence data. | Fight, Influence, or Generate demand list. |
| 4. Revisit Regularly | Re-sort terms as SERP landscape changes. | Ongoing SERP monitoring, AI Overview reports. | Update bucket assignments and review budget assigned to each. |
On Fight terms, accept a position premium only where absolute top still wins your incremental cost per acquisition.
On Influence terms, align ad copy with the question the Overview answers, coordinate with SEO on content and citation, and pull back paid pressure where organic already wins.
On Generate demand terms, move budget into YouTube, Demand Gen or content when the Search clicks are gone but the query still matters and measure through brand search and assisted pipeline.
AI Max and broad match sit on top of this classification. They don’t replace it. Google needs broad match or keywordless campaigns for your ads to appear around AI answer surfaces, which expands reach while reducing control over what you show.
Use them where you already know the bucket and your tracking is clean, and introduce them through small, gradual tests.
Dig deeper: How to get your Google Ads seen in AI Overviews
See where competitors are investing, which keywords drive their results, and how to capture more of the market.
Accounts that apply one rule to every query will keep funding the wrong mechanism, seeing it as rising costs they can’t explain. Accounts that re-segment and move money between buckets and into channels outside the auction spend less time arguing with a results page that has already answered the user.
In B2B and industrial search especially, shifting from optimizing bids to reallocating budget across the three buckets is the difference between defending last year’s setup and building for how people search now.
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It’s been a pretty crazy few weeks in AI safety. We’ve watched agents go off the rails and do things nobody expected them to do, in production, on other people’s systems. So it’s a reasonable moment to ask how you use AI safely when the thing it’s touching is a live ad account with real money moving through it every hour.
Almost every conversation I have about agents in ad accounts opens the same way.
Do you trust the AI?
While it’s a good question, it’s often asked to shirk responsibility and conclude that AI shouldn’t be used. A better question is: How can we trust AI?
Then the conversation turns to what we can build around the AI model to make it safe for our business to use.
To explain, it’s always helpful to frame it the way you’re already evaluating collaborators. Nobody asks whether they trust a new PPC agency in the abstract. They ask what the team has access to, what they’re allowed to change without checking first, and who reviews the work. Three different questions with three different answers, and you’d never accept “I simply trust them” as a substitute.
Same three questions for an agent:
Most agentic PPC setups I look at have a decent answer to the first question, but there’s a lot of benefit in becoming more stringent with the answers for the other two.
We’ve been thinking about safe AI for PPC a lot at Optmyzr, so let’s take a look at what we’ve learned so you can get yourself to safer AI faster.
The reason I’d rather write this as three techniques than as one system: you don’t have to do all three to get value. Each one closes a different failure mode, and each one is useful the day you turn it on.
Then these layers compound, which is the part I find genuinely satisfying. Grounding makes the agent’s proposals worth reviewing, so the review step feels like leverage instead of homework. Policies filter the obvious non-starters before a human ever sees them, so the queue stays short enough that people keep opening it. Each layer makes the next one work better than it would on its own.
So, if you’re starting from zero, start with grounding. It has the fastest payoff and the least process to stand up. If you’re already letting an agent make changes, add policies this week. If you have both, the review queue is what turns it from something you use into something your team uses.
Order matters less than accumulation. Any one of these leaves you better off than you were yesterday, and each one you add compounds with the last.
A blind agent is a dangerous agent.
Ask an agent connected to a thin data layer why your CPA went up last month. It will answer you. Fluently. Immediately. Based on whatever slice of your account it could actually reach.
What it couldn’t see might have been the entire answer. It’ll make do with what it has.
Realize that grounding is a safety feature, not a convenience feature. Every gap in what an agent can see is a place where it will make something up, and it will do it in exactly the same confident tone as everything else it says. There is no tonal tell.
So here’s what I think you should expect from an MCP, or any data layer, that you’re going to let an agent reason over for PPC.
The full query layer for Google Ads. Real GAQL. Any resource, field, segment or metric the API exposes, including the ones no packaged report covers. Not a curated summary of what a product manager thought was interesting. The moment your data layer is a curated subset, you’ve limited which questions the agent can answer well, and you haven’t told anyone which ones those are.
Optmyzr’s MCP has all of the above today, and it’s a one-click install from the Claude directory rather than an API console, a developer token, or an engineer on speed dial.
Grounding raises the quality of everything the agent says. The next layer decides what it’s allowed to do about it.
How do you stop an AI from blowing through a month of ad budget by mistake?
Not by asking it nicely. Not in the prompt.
You use a policy layer that controls what an AI can and cannot do, and you separate it from the AI itself, so the AI can never change the rules.
Set the rules once. Enforce them everywhere, always.
This is automation layering, which I’ve been writing about across my books for years, applied to a new first layer. The original idea is simple: one system does the work, like Google’s own bidding and budget automations, and a second system, your own automations, scripts or rule engine strategies, validates that what the first system did actually makes sense for your business before it sticks.
AI just took over the first job. It’s now the thing making the recommendation. The second layer didn’t become less necessary; it became more necessary because the first layer is more creative and sometimes unpredictable.
Account policies are how you write down the “never do this.” Rules you set once, on the account itself, about what is allowed to happen there. No bid increase above 10% in a single move. No budget change beyond a set threshold. These campaigns don’t get touched. No competitor brand terms added. Whatever your version of “absolutely not” happens to be.
And the policy doesn’t care who’s asking.
An agent proposing a 20% bid increase gets blocked. A hallucination gets blocked. An instruction hidden in a document gets blocked. A junior with a misplaced decimal gets blocked. You, at 11pm on a Friday, in a hurry, on your phone, get blocked.
Same rule, same verdict, no exemption for good intentions or seniority.
Want it through anyway? Override it deliberately. It goes on the record with your name attached. An override you can perform without noticing is not a guardrail but a speed bump made of paint.
This structural detail matters: this isn’t a setting for the AI. It’s a setting on the account, and the AI is one more thing subject to it. Same as a script. Same as a person.
A rule that lives in the prompt is a rule the model can be talked out of by a clever user, by an injected instruction sitting in a document it was asked to read, or by its own drift over a long session. A rule that lives on the account holds under every path into the account.
Everyone says they’re in the loop.
Few people can tell you which screen, which person, or which queue enforces being in the loop. Ask, and you usually get “we check the change history afterward,” which means nobody’s checking.
And good luck getting a quick answer when someone asks you to produce the reasoning behind an AI-assisted decision from three months ago.
So when we built the write path into our MCP, we didn’t leave the loop to good intentions. There is no route from the agent to your ad account that doesn’t first stop at a human.
We took a pattern from engineering, where it’s been settled practice for decades: nobody pushes code to production without a change request that another person reviews. Why the account where you spend six figures a month deserves less process than a CSS tweak is a question our industry has never really answered.
Here’s the actual sequence.
Whether it’s an AI or a colleague suggesting the change, everything goes through the same pipe. In the case of a colleague, the change request is a second pair of eyes. In the case of an AI, it’s the first pair.
The change request queue turned out to be more valuable than just the safety it was built for.
Because it’s not just a list you go to in order to be the human in the loop. It’s a complete record of intent.
When a client asks in November why their target CPA was moved in March, you have the proposal, the rationale, the policy verdicts, who approved it, and when. Was it a human? Was it an AI? What was the data behind the recommendation? Who made the final call?
Try assembling that from a chat transcript six months later. Try assembling it from change history, which tells you what changed but never why.
We built this for safety, and it turned into the best account documentation we’ve ever had. If you’re an agency, that’s a credibility argument as much as a safety one.
Put the three layers together, and you get something I’d describe, approvingly, as boring.
Not underpowered. Boring.
As in: you know what it can see, you know what it structurally cannot do, and you know nothing reaches the account without you. The excitement belongs in the findings, not in wondering what it got up to while you were at lunch.
That’s the bar I’d hold any agentic PPC setup to, ours included, and it’s a bar you climb one rung at a time:
You don’t have to arrive there in one move. Pick the layer that closes your biggest gap, ship it, then add the next one.
We’ve been building this against real accounts and genuinely stupid edge cases since well before MCP was a term marketers used. It’s a one-click install from the Claude directory now.
If you tried agentic PPC once, got a confidently wrong answer, and quietly shelved it, that’s the failure I’d most like you to come back and retest.
Having safe AI for our ad accounts can’t be something we expect to get just from picking the right model. It’s something we achieve by layering in processes and technologies we control.
Try Optmyzr’s safe AI for PPC free for 14 days.
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