Not long ago, broad match was positioned as the future of paid search. Today, that role belongs to AI Max.
Over the last few months, I’ve heard repeated recommendations to enable AI Max on brand campaigns, even when those campaigns are already performing exactly as intended.
The problem is that many accounts still lack the foundations AI Max needs to work well. Conversion tracking is unreliable, offline conversion imports are missing, and generic campaigns remain constrained by budget or structure.
AI Max depends on strong conversion signals, sufficient volume, and enough variation for the system to learn effectively. In many accounts, brand campaigns provide most of that signal.
But using AI Max on brand means introducing additional automation into your most predictable and efficient traffic source.
The promise and limitations of AI Max
AI Max expands search targeting beyond your existing keyword list by using keywords, landing pages, and site content as signals rather than strict targeting parameters.
Like dynamic search ads (DSA), AI Max can match to queries you didn’t explicitly target. But it goes further, reaching beyond the intent boundaries defined by your keyword set.
Google has positioned AI Max as the next step in Search automation, with DSA, automatically created assets, and campaign-level broad match settings scheduled to transition into AI Max in September.
The platform includes controls such as brand exclusions, URL exclusions, text guidelines, and location targeting. In accounts with strong conversion tracking, sufficient search volume, and reliable performance signals, AI Max may uncover incremental growth opportunities.
Many accounts haven’t reached that stage yet.
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Why AI surface eligibility isn’t a reason to rush into AI Max
Much of the recent interest in AI Max stems from Google’s push toward AI-powered search experiences.
AI Overviews now reach 2.5 billion monthly users, according to Google. Ads appear in 25.6% of AI Overview results, Semrush data shows.
As Google continues expanding AI-driven search experiences, advertisers are understandably focused on maintaining visibility across those surfaces.
That concern is reasonable. The problem is that AI Max is often presented as the solution before advertisers address the measurement, conversion, and account structure issues that determine whether the automation can succeed.
Google Ads representatives typically pitch AI Max for brand campaigns by claiming it’s necessary for eligibility in AI Mode and AI Overviews on brand searches. But this isn’t accurate.
Ginny Marvin, Google Ads liaison, confirmed that three campaign types are eligible to serve in AI Overviews: broad match with Smart Bidding, Performance Max (PMax), and AI Max for Search.
However, exact match keywords aren’t eligible to serve in AI Overviews at all, even when identical broad match keywords exist in the same account.
So, the eligibility picture looks like this:
Campaign type
AI Overview eligible
Query control
Best use case
Exact match
No
Highest
Defensive brand
Phrase match
No
Medium
Controlled intent expansion
Broad match
Yes
Lower
Generic scaling
Performance Max
Yes
Low
Cross-network automation
AI Max
Yes
Lowest
Mature accounts with strong signals
PMax and AI Max do broadly the same job in terms of AI surface eligibility. So if you run PMax brand campaigns, you’re already covered. Adding AI Max won’t unlock anything new, as it’ll only add another automation layer to a setup that’s already eligible.
So, when reps position AI Max on brand as the answer to AI surface eligibility, advertisers should stop and ask why this feature takes priority over fixing the account’s foundation.
Test data doesn’t support Google’s AI Max claims
When AI Max was in beta, Google stated that advertisers who activate the feature would see 14% more conversions, and those running exact and phrase match keywords would likely see a 27% increase in conversions.
Google also indicated that advertisers who enable the full AI Max feature suite see 7% more conversions on average. Independent testing has produced more mixed results.
The evidence for AI Max remains mixed
Across 600 accounts, Smarter Ecommerce found that AI Max delivered a 35% lower return on ad spend (ROAS) than traditional match types. AI Max accounted for just 0.57% of total ad spend in those accounts, indicating that advertisers kept the budget to a minimum.
After running a four-month test, Xavier Mantica found that AI Max had the most expensive conversions. While AI Max cost $100.37 per conversion, phrase match cost $43.97 per conversion, and exact match cost $52.69 per conversion. And Ezra Sackett tested 30,000 search terms with AI Max, only to find that 99% of impressions delivered zero conversions.
After a 23-test analysis of 16 advertisers, Andy Goodwin noted improved Quality Score and ROAS when advertisers used the AI Max full feature suite. But he tested mature advertisers and used text customization in only 50% of tests and URL optimization in just 44%. This suggests advertisers were cautious about enabling every AI Max feature.
However, none of this data is brand-specific. AI Max may deliver value in the right context, but an exact match defensive brand campaign that already performs well isn’t the ideal place to test a new automation product that depends heavily on signal quality. This is especially true for accounts that haven’t solved the underlying data problems feeding the automation.
AI Max doesn’t always find genuinely new search terms, according to Adalysis. In some cases, it simply takes credit for the queries that exact and phrase campaigns were already winning.
Because AI Max treats keywords as signals rather than targeting parameters, impressions that would previously have been attributed to your exact match keyword can end up attributed to AI Max instead.
This reporting issue can be significant for brand campaigns. Brand traffic is already the highest converting traffic in most accounts.
Flip on AI Max, and suddenly you see an uplift. But it’s difficult to tell if it’s incremental or if preexisting branded performance simply appears in a different automation bucket.
Brand controls don’t work consistently
Google’s pitch leans heavily on brand controls. AI Max offers inclusions, exclusions, and guardrails that supposedly keep the match type tightly focused. In practice though, these controls don’t always work well.
Adalysis notes that competitor terms occasionally slip through and brand terms sometimes match to non-brand queries. DAC reports overlap between brand and non-brand terms as well as unintended language matching. And LBBOnline finds relevance hovering around 50% in some campaigns.
Brand controls could improve over time. But the available evidence doesn’t support treating AI Max as a low-risk switch for tightly controlled defensive brand campaigns.
What to consider before testing AI Max on brand
Before expanding automation into a defensive brand campaign, ask these questions.
1. Are the conversion signals trustworthy?
Have you separated macro and micro conversions? Do offline imports work correctly? Does lead quality feed back into the platform, or does Google still optimize equally toward every form fill?
If the signal quality underneath the account is poor, AI Max will amplify it instead of fixing it.
2. Have you already explored generic growth?
In many of the accounts I audit, budget, weak landing page alignment, poor structure, and outdated query management limit generic campaigns. This is where you usually find incremental growth, not inside an already dominant brand campaign.
3. Does the account give automation enough useful learning data?
AI Max isn’t magic. It reflects the quality of the signals underneath it.
If most of the account’s meaningful conversion volume comes from brand, then turning AI Max on in a brand campaign may reinforce existing dependency on branded traffic rather than helping the account grow beyond it.
4. Are brand + modifier searches already structured properly?
“Brand + reviews,” “Brand + pricing,” “Brand + near me,” and product intent variations often deserve their own campaign strategy entirely. AI Max shouldn’t become a substitute for good account architecture.
5. Do you have a strategic reason to expand the brand campaign?
If so, test carefully using experiments. That’s a business decision, not a checkbox recommendation from a rep who hasn’t looked deeply enough at the account to understand where the real opportunities actually are.
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AI Max only works as well as the signals feeding it
AI Max may grow into something genuinely useful over time. Remember, PMax went through a similar evolution and is in a much stronger place now than it was early on.
But automation only works as well as the signals feeding it. Right now, the issue is that the foundations underneath the automation still aren’t strong enough. Better conversion frameworks, measurement, account structure, and feedback loops make automation smarter.
If brand remains the best-performing campaign in the account, the bigger question is why the rest of the account hasn’t caught up yet.
Above all else, don’t confuse Google’s automation priorities with your account priorities.
https://i0.wp.com/dubadosolutions.com/wp-content/uploads/2021/12/web-design-creative-services.jpg?fit=1500%2C600&ssl=16001500Dubado Solutionshttp://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.pngDubado Solutions2026-06-09 12:00:002026-06-09 12:00:00Why your brand campaign may not be ready for AI Max
You can be a strong brand, publish high-quality content, and still not have topical authority.
Just look at Great Jones, a kitchenware company.
Their Dutch oven (called The Dutchess) is beautiful, well-reviewed, and featured in industry-leading sites like Vogue, the New York Times, Bon Appétit, and The Kitchn.
But search “best Dutch ovens” on Google or ask an LLM for recommendations, and the brand rarely appears.
It’s not that Great Jones lacks content or press.
What’s missing is the pattern — a consistent, positive framing that ties the brand to Dutch ovens across its own site and third parties.
Without this, search engines and large language models (LLMs) can’t confidently connect the brand to the topic, so they default to the names with stronger signals.
Many brands have some version of this gap. And AI search has only made it more visible.
The good news: You can build this pattern.
In this guide, I’ll show you how using the Topical Authority Pyramid, a framework I created to turn your brand into the go-to name in your niche.
This framework builds on conversations with Amanda Milligan, Content and Growth Manager at Semrush, and my work in brand positioning across ecommerce, SaaS, and finance.
What Is Topical Authority?
Topical authority is your site’s earned reputation for expertise on a specific subject. It forms when your brand and topic appear together repeatedly across the sources that buyers, search engines, and LLMs trust.
Think about the brands you automatically connect with certain topics.
Like these:
You didn’t consciously decide to make those associations.
They formed because those brands kept showing up with the same message, in the same spaces, around the same topic.
That’s topical authority — and it’s also how search engines and LLMs learn which brands are most strongly associated with a topic.
The Topical Authority Pyramid Framework
Topical authority has traditionally been defined by content volume and breadth of coverage.
Publish comprehensively on a subject, and you’d own it.
That’s no longer enough.
As Amanda explains:
The phrase “topical authority” has been around for a long time, but the thinking around it has evolved significantly. At its core, it’s always been about your brand becoming associated with specific topics. What’s changed is how we try to build that association.
Today, search engines and LLMs look for more than coverage. They look for a clear position on the topic and external evidence that supports it.
To address this, I created the Topical Authority Pyramid:
The Pyramid breaks topical authority into three layers:
Foundational authority: On-site content and credibility signals that demonstrate experience, expertise, authoritativeness, and trustworthiness (E-E-A-T), and category fit. (Think category pages, about pages, author bios, comparison content, FAQs, customer reviews, case studies, and more.) Still important, but not enough on its own.
Point of view (POV-led authority): A specific, consistent angle that separates you from every other brand covering the same ground. It gives buyers a reason to choose you and AI systems the confidence to recommend you over competitors.
Proof-backed authority: Third-party signals (mentions, reviews, citations, and data) that back up your POV across the wider web. It turns your POV from self-declared to independently verified.
Each layer works alongside the others to establish your brand as the expert in your niche and earn more visibility in search engines and LLMs.
Many brands, including Great Jones, have strong foundational authority and scattered proof, but no consistent POV tying it all together.
Here’s how to build all three.
Free resource: Download our free Topical Authority Audit template to audit your topics, score competitor authority, and track your progress. Fill it out as you work through each step below or at your own pace.
Step 1: Audit Your Topic Reputation
Your brand likely already has a topical reputation, whether you’ve shaped it intentionally or not.
Audit it before deciding what to build.
Research Your Current On-Site Associations
The gap between what you publish and what you want to be known for may be wider than you expect.
This is something Amanda has experienced firsthand:
When I did content audits, I’d inventory every piece of content by topic. You might find you have dozens of pieces on something that isn’t even your priority, and only five on the topic you actually want to own. That mismatch is exactly what a topic audit is designed to surface because what you’ve published is what you’re telling Google and buyers your priorities are.
The fastest way to assess this is with Semrush’s Organic Rankings tool.
Enter your domain to automatically see your brand’s strongest topic associations, organized by the topics getting visibility.
When I did this for Great Jones, their strongest topical associations were “recipes” and “celebrity chefs.”
Dutch ovens barely registered.
Yet, the Dutchess is their primary product.
And “Dutch oven” alone gets over 200,000 monthly Google searches.
Great Jones has a big opportunity to increase their topical authority for Dutch ovens and convert some of this search interest into sales.
These are the kind of topical association gaps you want to surface in this step.
Two more places to look:
Google Search Console: Go to “Performance” > Queries and sort by clicks or impressions. You’ll see the topics that attract users to your site.
Branded queries on Google and LLMs: Search “[your brand] + your topic” and “what is [your brand] known for” to see how search engines and LLMs describe you
Audit Your Off-Site Presence
Next, review your third-party coverage: mentions, reviews, roundups, and editorial press.
This is where many brands have the biggest gap, and it’s the one AI systems appear to weigh most heavily.
Run these checks:
Search “[your brand] + [topic]” and look beyond your own site: What’s showing? Industry blogs? Reddit? Editorial coverage? Or nothing?
Ask an LLM: “What are the best [topic] brands?” and “Where would you recommend buying [topic]?” See whether your brand surfaces and what it’s associated with.
Check “best of” lists, roundups, and comparison articles for your topic: Are you in them? If so, where do you rank and how are you described? If not, who is?
A quick off-site audit for Great Jones showed me they’ve earned coverage any kitchenware brand would envy: features in major lifestyle publications and partnerships with prominent chefs and influencers.
But when you look specifically at Dutch oven coverage, the off-site gap is obvious.
Most of the top-ranking articles are a few years old (or older):
And the overall sentiment is inconsistent.
For example, in Food & Wine’s Dutch oven roundup, the Dutchess appears under the “Other” section (rather than “Top Picks”) with a caveat about heating issues.
In this Bon Appétit roundup of the best Dutch ovens, Great Jones is categorized under “Dutch ovens we don’t recommend.”
They’re also notably missing from some use-case roundups, like this one from Serious Eats:
In Reddit threads where buyers are actively looking for Dutch oven recommendations, Great Jones rarely comes up.
When it does, many of the threads are from years ago:
Great Jones has real brand equity to build on.
But it’s just not adding up to a solid reputation in Dutch ovens — yet.
Step 2: Choose the Topic You’ll Build Authority Around
You can’t build authority on everything at once.
This step narrows your focus to one topic worth owning based on a few crucial factors:
What drives revenue
Where competitors are weak
Where your brand has room to claim a position
Build and Prioritize Your Topic List
Start by listing the topics you want buyers, search engines, and LLMs to associate with your brand.
Begin with the obvious ones: the products, categories, use cases, and problems you want to be known for.
Then expand with adjacent topics buyers already care about.
For Great Jones, that might include slow cooking, one-pot meals, kitchen gifting, or cookware care.
Look especially for topics where you already have traction, competitors are weak, or your brand should be associated but currently isn’t.
Once you’ve identified 10 to 15 topics, add them to the “Topic Audit & Scoring” tab in your spreadsheet.
Next, narrow the list down.
Not every topic on your list is worth building a reputation around right now.
For each one, ask two questions:
Do you want to own it? Does it drive revenue, support a product you sell, or build a reputation that brings buyers to you?
How urgent is it?
High: Directly tied to revenue and an opportunity you can act on now
Medium: Tied to revenue, but the opportunity or timing isn’t right yet
Low: Worth tracking but not acting on yet, or no direct business connection
You should end up with three to five high-priority topics to investigate next.
Run a Query Audit
Now test each shortlisted topic to see who already owns the space and where there’s room for your brand to carve out a position.
For each topic, run four queries on Google and LLMs:
Query type
What to search
What it tells you
Head term
The topic as-is (“Dutch ovens”)
Who owns the broad topic; what AI defaults to
Best query
Add “best” or a qualifier (“best Dutch ovens under $200”)
Where buyer intent lives; which brands AI recommends
Brand query
Your brand + the topic (“Great Jones Dutch oven”)
Where you specifically stand; how AI currently describes you
Specific angle
A query tied to an association you might want to own (“Dutch oven for gifting”)
Whether that territory is already claimed or still open
As you run each query, note:
Which formats show up most: editorial lists, reviews, Reddit threads, brand pages
Whether AI systems name specific brands without being asked (unprompted)
Whether community results show buyers asking for recommendations or comparing options
Record this in the “Query Audit” tab of your spreadsheet.
If a query shows buying intent but the top results barely address it, that’s a topical authority opportunity.
For example, when I search “Dutch ovens” and “best Dutch ovens,” the same brands consistently come up: Le Creuset, Staub, Lodge, and Caraway.
But rarely Great Jones.
And for “Dutch oven for gifting,” ChatGPT didn’t mention Great Jones at all.
Great Jones only appears when buyers already know to look for them.
More importantly, some topics, such as gifting, aesthetics, and non-toxic coating, are not clearly owned by any brand.
That’s where the opportunity is.
Score by Association Strength
After the Query Audit, score your presence on each topic against three competitors on a 0 to 3-point scale.
The score reflects your overall standing across the Topical Authority Pyramid: foundational, POV, and proof combined:
Score
What it means
0
Not present anywhere for this topic
1
Present but weak or negative
2
Present and positive but inconsistent
3
Consistently prominent across high-authority sources and AI
Note: This isn’t a precise measurement. Use your observations, priorities, and market knowledge to guide the score.
Score your brand first, then each competitor.
After your scoring is complete, look for high-priority topics where you scored a 1 or 2 and at least one competitor scored a 0 or 1.
Those are topics where buyer demand is real, you have some footing, and no competitor has locked it down — the conditions for a winnable position.
For Great Jones, “Dutch ovens for gifting” fits the pattern: high priority, room to claim it, and no clear leader.
By the end, you should have one topic to focus on.
Have more than one? Choose the one closest to revenue or where the gap between your current and desired reputation is smallest.
Have none? Go niche. Instead of “Dutch ovens,” try “enameled cast iron Dutch ovens.” A narrower topic is easier to own and still builds toward the bigger one.
Step 3: Identify Your Topic POV
You’ve identified one viable topic. Next, decide what reputation to build around it.
Your POV is the specific angle you own inside that space.
It’s what makes your brand distinct to buyers, search engines, and AI systems.
Like these brands — same topic, completely different associations:
Research What’s Already Owned
Before identifying your POV, map what dominant brands in your space are already known for.
These are the POVs to avoid. Going after any of them directly means competing for territory another brand has spent years building.
Start with your notes from the Query Audit. The patterns there tell you a lot about which competitors own what.
To run your audit, go through each belief in the table and identify which proof assets you already have and which are missing.
Use the POV Proof Planner in your template to record your findings:
For Great Jones’s gifting POV, a quick proof audit surfaces:
Consideration proof exists: The brand has features in the New York Times, Good Housekeeping, and many others, but most aren’t connected to gifting or were published years ago
Comparison proof is sparse: Some decision-stage proof tied to gifting exists for Great Jones, but it’s not consistent enough to increase AI recommendations
Step 5: Build Your On-Site Foundation
Before search engines and LLMs can associate your brand with your POV, you need to establish it on your site.
This step is about building that foundation: the hub and supporting pages where your topic, POV, and early proof signals all come together.
Create a Hub Page for Your POV
Your hub page is the central authority document for your POV.
It defines the topic, explains why it matters, and routes buyers to supporting pages that go deeper.
Side note: If you’ve built pillar pages and topic clusters before, this will feel familiar. The structure is similar, but the organizing principle is proof and belief, not coverage and keywords.
For Great Jones, that could be a “Dutch oven gifting guide.”
It would link to the Dutch oven product page and explain why Dutch ovens make exceptional gifts.
Supporting pages, such as gift basket ideas, a gifting FAQ, and a report on cookware gifting would also be linked.
If you’ve been publishing for a while, you may already have a page that can serve as the hub: a category page, a subcategory page, or an industry-specific landing page.
Build Supporting Pages
Supporting pages go deeper than the hub.
Each one proves a specific aspect of your POV at a specific stage of the buyer journey.
Go back to the proof assets you mapped in Step 4 — they tell you what you need to prove and at which stage.
Your supporting pages are how you do it.
For Great Jones, the comparison stage is a clear gap.
To convince buyers the Dutchess is a better gift than the alternatives, they need dedicated comparison pages, backed by awards, endorsements from leading industry sites and public figures, and head-to-head data.
Other supporting pages might include:
Dutch oven gift basket ideas: What to pair it with and how to present it, backed by customer photos and a relevant publication feature
Gifting FAQ: Sizing, monogramming, return policies, with real customer questions pulled from reviews
The Gift-Worthy Dutch Oven Report: Proprietary survey data on how customers buy, give, and display the product
Pro tip: Strengthen your hub and cluster pages with on-site trust signals. Include author bios that show real niche experience in the topic, named expert sources or contributors, and an About or editorial page that clearly ties your brand and contributors to the category.
Identify what pages you need, and fill out the rest of the “On-Site Foundation Planner” tab in your template.
Structure Each Page for Readers and Machines
Lead with the most important information first — also known as the inverted pyramid.
It makes your pages easier for readers to scan and for machines to interpret.
Then, make sure each page has:
Clear section headings: Labeled so readers and machines immediately understand what each section covers
POV language: Reuse the same phrases and framing tied to your angle throughout
Schema markup: Structured data that helps search engines and AI systems understand your content and context
Semantic HTML: Proper use of HTML tags so machines can correctly interpret your page structure
Link Your Pages
Each hub and supporting page proves something on its own.
Link them together, and you create a proof system.
Link from the hub to your 5–10 most important supporting pages in the body. Not just in the nav, breadcrumbs, or footer.
Link every supporting page back to the hub. Keep key pages within 2–3 clicks of each other.
Use descriptive, relevant anchor text to help people and machines understand what the linked page is about
Step 6: Create an Off-Site Proof System
A strong POV and foundation won’t get you into AI answers if the association exists only on your site.
This is one of the biggest shifts in how topical authority works, as Amanda explains:
Topical authority isn’t just about what’s on your site anymore. You need third-party sources — coverage, mentions, appearances, even reviews — independently reinforcing the same association. If the only place your brand is tied to a topic is your own content, that’s often not enough to build the pattern that AI systems and search engines need to trust you on it.
This step reinforces your POV in the places buyers and AI systems already trust.
Start with One Signature Proof Point
A signature proof point is an original, specific story or finding about your topic.
Something others outside your brand would want to reference, share, or build on.
That could be:
Proprietary data from your own sales, customer behavior, or research
A trend you’ve spotted and named before anyone else
A contrarian observation backed by evidence
For Great Jones and the gifting POV, the insight has to tie Dutch ovens to gifting.
They might pull data from their own sales — say, a 4x spike in Dutch oven purchases in the two weeks before Mother’s Day — and turn it into a “State of Mother’s Day Gift-Giving” report.
That report becomes a press pitch to lifestyle publications, a video on their YouTube channel, and a thread on Reddit’s r/gifts.
One insight, multiple placements, all reinforcing the same association: Great Jones = gifting.
To find yours, start with your proof assets from Step 4.
Look for patterns in your data, reviews, industry trends, or customer behavior.
Distribute Your Proof Point
Once you have a signature insight, decide where and how to distribute it.
There are four main buckets:
Brand channels: Content you publish directly to audiences you’ve built: email newsletters, marketplaces, review sites, podcasts, social media, SMS or loyalty messaging, local profiles
Community: Discussions in spaces your buyers already trust, such as Reddit, niche forums and industry groups, social media comments and communities
Partners: Others who extend your reach into new audiences, including affiliates, influencers, retail partners, and integrations
Earned: Third-party coverage you pitch but don’t control, such as media mentions, press features, user-generated content, and editorial placements
For each bucket, identify the specific publications, platforms, or communities where your insight is most relevant.
Not sure where to start?
Run a search on Google or an LLM related to your proof point and look at the sites that rank and the sources that get cited.
Those are the places worth showing up in. List them in the “Off-Site Proof Planner” tab of your template.
For Great Jones, some of that infrastructure is already in place.
They already have the social media following, media clout, and collaborations with names like cookbook author Molly Baz.
What they need is a focused distribution of insights around their gifting POV.
That might look like:
Briefing partner creators on a gifting-specific collaboration, like pitching fresh coverage that ties the Molly Baz collab to gifting
Pitching their Mother’s Day gifting sales data to lifestyle publications already covering Dutch ovens
Reframing existing social content around the gifting angle
Now check whether it’s starting to influence how search engines and LLMs describe your brand.
Use the “Progress Tracker” tab in your spreadsheet to record what you find at 30, 60, and 90-day intervals.
Foundational Layer: Are You Showing Up More?
Coverage tracking tells you whether your topical footprint is growing:
Go back to your Step 2 notes. How many of your four query types surfaced your brand unprompted? Run them again and compare.
Also monitor pages ranking for queries you didn’t directly target, and rising impressions for queries related to your topic.
For Great Jones, the baseline visibility was weak for many non-brand Dutch oven queries.
Showing up in two or three queries at 90 days — especially “Dutch ovens for gifting” — would be a real sign of progress.
Tools that help:
Semrush’s Organic Rankings tool (the Topics report) for association trends
Semrush AI Visibility Toolkit: The Visibility Overview tool to see whether your AI Visibility score and mention count are climbing, and Prompt Tracking to re-run your query set on a set cadence
Google Search Console for impressions and queries by page
The POV layer tracks language. Specifically, whether mentions of your brand are increasingly paired with your POV.
Run POV-specific prompts monthly and check the wording.
For Great Jones, that’s searches like “Dutch oven wedding gift” or “best Dutch oven to give as a gift.”
And when the Dutchess shows up in reviews, comparisons, and “best of” listicles, watch for the language around it.
Is it being called “a great house-warming gift,” “splurge-worthy,” or “the kind of gift that gets displayed”?
That’s the POV landing.
Tools that help:
Brand24 to track web and social mentions
Semrush’s Perception tool for sentiment trends, and Narrative Drivers for the attributes and phrases AI ties to your brand
Proof Layer: Are Others Confirming Your POV?
The proof layer tracks third-party confirmation.
Are media mentions, third-party pages, and niche communities backing up the POV you want to own?
Start with your proof point.
Are others citing or referencing it? That’s a signal your off-site distribution is working.
Then, go broader.
Run [Your Brand] + [POV] queries on Google and an LLM.
Check whether you’re appearing in more third-party sources associated with your POV.
Are buyers recommending you unprompted in Reddit or niche communities? Are your hub pages attracting links from relevant sites?
When your brand appears, is it being described in relation to your POV?
For Great Jones, that might be a gift guide naming the Dutchess as the go-to Dutch oven for wedding gifts.
Tools that help:
Google Alerts for basic brand mention tracking, or Meltwater for a more robust option
Semrush’s Competitor Research tool to surface sites citing competitors but not you, and Narrative Drivers for the Top Cited Domains shaping your topic
Build the Pattern That Wins in AI Search
Great Jones proves that great press and a great product aren’t enough for topical authority.
If search engines and LLMs don’t have clear associations attached to your brand, showing up online will be a struggle — no matter what Vogue thinks of you.
But that’s fixable.
The Topical Authority Pyramid gives you the framework:
A strong foundation that proves you belong in the category
A POV that makes you distinct
Proof that backs it up across the web
Once your first topic takes shape, expand.
Follow the Topical Authority Pyramid for your next topic, claim more territory, and deepen your authority in adjacent spaces.
Do this well, and search engines and LLMs may just start recommending you by default.
Want a repeatable way to monitor your AI visibility over time? Our AI visibility audit guide walks you through it step by step.
http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png00Dubado Solutionshttp://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.pngDubado Solutions2026-06-08 21:32:122026-06-08 21:32:12How to Build Topical Authority in the AI Search Era (7 Steps)
Google is expanding experimentation capabilities in Performance Max, giving advertisers more ways to test creative assets and measure campaign performance before making large-scale changes.
What’s happening. Google is rolling out new asset experiments for Performance Max campaigns, allowing advertisers to test how different creative assets affect results.
The feature enables marketers to compare entirely new asset groups, evaluate the impact of adding individual assets, or measure the performance of seasonal creative against evergreen content.
Advertisers will also be able to test assets generated through Google’s Asset Studio.
The big picture. Performance Max has long automated campaign optimization across Google’s inventory, but advertisers have had limited visibility into the impact of creative changes.
The new experiments aim to give marketers a more controlled way to evaluate creative decisions before applying them across campaigns.
Between the lines. The addition of a second success metric could be particularly valuable for advertisers balancing competing objectives, such as maximizing conversions while maintaining efficiency targets.
Rather than declaring a winner based on a single KPI, marketers will be able to evaluate how changes affect broader campaign performance.
What else is new:
Conversion lift studies and experiments are being brought together under one Experiments page.
Additional experiment and measurement capabilities are planned for future releases.
Expanded support for manager accounts (MCCs) and the Google Ads API is expected to begin rolling out in the coming weeks.
Why we care. Creative remains one of the biggest levers available to Performance Max advertisers, yet testing new assets often involves risk. The new experimentation tools provide a structured way to validate creative decisions with data before fully committing budget.
What to watch. As Google continues investing in automation and AI-generated creative, asset testing is becoming increasingly important. The ability to directly compare human-created, seasonal, evergreen, and AI-generated assets could offer advertisers deeper insight into what drives performance across Performance Max campaigns.
OpenAI is expanding its advertising ambitions inside ChatGPT, beginning an early test that allows multiple advertisers to appear within a single ad placement.
What’s happening. The company is testing multi-advertiser ad units across a small subset of ChatGPT ads, according to a product update sent to advertisers.
Rather than displaying a single sponsored result, the new format will group multiple relevant ads together in one placement. Eligible ads will be sold through a second-price auction model, a common pricing mechanism used across digital advertising platforms.
OpenAI says the goal is to improve product discovery for users while creating more opportunities for advertisers to engage with users during high-intent conversations.
Meanwhile, in Ads Manager Beta. OpenAI also announced several new campaign management features for advertisers:
Advertisers can now convert existing campaigns from lifetime budgets to daily budgets.
CPM campaigns can be cloned and converted to CPC bidding with one click.
Impression-based campaigns now support custom CPM max bids.
Bulk editing is available directly within the Ads Manager interface.
Daily budgets will transition to an average daily budget model with weekly pacing flexibility.
Geographic targeting is expanding beyond the U.S., Canada, Australia, and New Zealand to include the U.K., Japan, South Korea, Brazil, and Mexico.
Why we care. The updates bring OpenAI’s ad platform closer to the functionality marketers expect from mature advertising ecosystems, reducing campaign management friction while expanding targeting opportunities internationally.
What to watch. The multi-advertiser placement test could provide an early signal of how aggressively OpenAI intends to monetize ChatGPT. If successful, the format may become a larger part of the platform’s ad inventory strategy while offering advertisers more opportunities to reach users during purchase and research journeys.
The bottom line. OpenAI is steadily building out its advertising stack, but the biggest development may be its experiment with showing multiple advertisers in a single ChatGPT ad placement — a move that could reshape how sponsored content appears within AI conversations.
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Google is changing the rules framework that governs Local Services Ads, updating policy language and aligning advertiser requirements with its new badge system.
What’s happening. On July 6th Google will update its Local Services Ads policies to improve readability, revise terminology, and remove requirements that no longer apply to advertisers.
As part of the update, Google will rename “Local Services platform policies” as “Local Services Ads requirements.”
The changes build on the company’s recent overhaul of the Local Services Ads badge system, including updates to Google Guarantee badges and advertiser verification standards.
Why we care. While these changes are mostly administrative, advertisers should pay attention because the new “requirements” framework could make it easier for Google to tie compliance standards directly to badge status in the future. For agencies and local businesses, it’s another indication that maintaining verification credentials and meeting platform standards will remain critical for competing in LSAs.
The big picture. Google says the policy refresh is intended to better align advertiser requirements with the new badge framework while making compliance guidance easier to understand.
The company is not positioning the update as a major policy crackdown. Instead, the focus appears to be on simplifying existing rules and modernizing the way requirements are communicated to businesses.
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Traditional share of voice (SOV) is effectively obsolete, yet many organizations have replaced it with an equally flawed successor: AI share of voice.
Software vendors now claim to measure brand visibility across ChatGPT, Gemini, Claude, Perplexity, and other AI platforms using a single percentage score. The problem is that these metrics rely on a hidden denominator.
Unlike traditional search, where visibility could be measured against a known keyword set, the universe of possible AI prompts is effectively infinite.
Traditional SOV had limitations, but at least its assumptions were transparent. Marketers defined a fixed keyword set, tracked visibility against competitors, and used that list as a stable denominator. Everyone understood the measurement’s boundaries.
That model no longer exists. Search results are dynamic and personalized, and are increasingly being replaced by conversational interfaces. Yet many AI visibility platforms continue to present precise-looking percentages that can’t be audited or validated.
To stop presenting fictional metrics to leadership teams, we must rethink how we define and measure visibility in AI search.
Why traditional SOV metrics now fail
The basic assumptions of search engine optimization and digital brand tracking have been broken by two major shifts: the disappearance of the static results page and the rapid rise of personalized, conversational answers.
Search engines have become highly dynamic, personalized landscapes that change shape continuously based on real-time data.
Between AI-generated summaries, localized results, continuous scrolling, interactive merchant grids, and real-time social feeds, no two users will encounter the same interface, even when entering the exact same query at the exact same moment.
Because the search environment changes constantly, attempting to calculate a precise “share” of that screen has become a mathematical impossibility.
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The new volatile normality of rankings
Securing the top ranking position in the older marketing model meant capturing a highly predictable percentage of user click-through rates.
In the modern search landscape, however, ranking first organically might place a brand below several sponsored listings, an AI-generated overview, interactive question accordions, and featured discussions from community platforms.
Because search engines now construct layouts dynamically in response to immediate user intent and past search history, rankings fluctuate by the hour.
Measuring share of voice based on static positions is as unproductive as trying to measure the volume of an ocean wave with a wooden ruler.
The modern AI share of voice
When marketing teams realized that traditional rank tracking was losing its utility, software vendors quickly introduced alternative metrics, branded as LLM Visibility or AI share of voice.
These dashboards present highly polished, authoritative percentage scores that suggest a brand’s footprint has been successfully mapped across platforms like ChatGPT, Claude, Gemini, and Perplexity.
These tools fail to deliver on this promise, exposing a fundamental methodology problem that we must address directly.
Legacy tracking (transparent)
LLM visibility (black box)
– Define fixed keyword list (known). – Measure rank on static SERPAuditable denominator.
– Infinite possible user prompts. – Vendor runs small, arbitrary subset. – Subjective denominator.
The infinite tail
Legacy SEO tools relied on a user-defined keyword list that served as a transparent denominator, whereas modern conversational engines present an entirely different mathematical reality where the universe of possible user prompts is effectively infinite.
Buyers no longer search for solutions using simple, two-word phrases. Instead, they enter highly specific, conversational queries that describe their exact organizational context, integration needs, and feature requirements.
Because no marketing tool can realistically sample this infinite universe of natural language, software vendors instead select a small, arbitrary subset of static prompts, run them through AI models behind the scenes, and aggregate those limited outputs into a representative global percentage.
This process creates a metric that only measures share of voice within a contrived and artificial environment, presenting a closed sandbox as if it were the open web.
Marketers maintained full visibility into the data they were analyzing with legacy tracking tools, which meant that if a system reported a specific percentage of visibility, the underlying keyword list could be audited and adjusted. Modern LLM visibility tools obscure their denominator within proprietary, vendor-defined systems that are almost certainly incomplete.
This structural flaw became incredibly clear in September 2025, when OpenAI updated to its ChatGPT 5.0 model. Following this release, the platform-wide volume of outbound citations and source links dropped.
For marketing teams relying on LLM tracking dashboards, this model change resulted in a sudden, sharp decline in their reported visibility metrics. The decline had nothing to do with a loss of brand relevance or a failure in marketing strategy. ChatGPT had simply changed how it presented source data to users.
This update demonstrates that modern AI metrics are highly volatile and largely out of your control. While software vendors are genuinely trying to solve an incredibly complex engineering problem, the underlying methodology simply cannot support the high-confidence dashboards they deliver, meaning these metrics should be treated as directional signals rather than hard numbers.
Beyond AI share of voice: 3 metrics that matter more
We must shift our focus from measuring pure search volume to measuring how effectively a brand is integrated into the broader context of digital discussions.
As search queries morph into conversational discovery, a brand’s visibility is no longer defined by the keywords it owns, but by how deeply it is embedded in the conceptual models used by AI.
1. Share of mentions
AI models synthesize relationships between concepts rather than simply indexing pages, meaning a brand must exist within the model’s training data, fine-tuning datasets, or real-time retrieval sources to be surfaced at all.
Share of mentions tracks how frequently your brand name, products, or key executives are naturally included in the responses generated across the broader information ecosystem.
This metric shifts the operational focus from ranking positions to vocabulary inclusion, ensuring that a brand is recognized by the model even when it is not explicitly prompted for a vendor list.
To influence this metric, organizations must focus on securing organic mentions across high-trust forums, developer communities, and authoritative industry publications where AI models actively gather and update their information.
2. Share of recommendations
When buyers use conversational engines to make purchasing decisions, they regularly ask for direct comparisons, shortlists, and product recommendations to simplify their research process.
Share of recommendations measures how often your product or service is explicitly featured when a user asks an AI engine to act as an advisor on a specific business challenge.
This approach shifts our focus from raw traffic acquisition to winning the buyer’s consideration set, which is critical because conversational engines filter out the noise of the web to deliver a highly curated list of options.
If your product positioning is overly generic, the model will struggle to categorize your offering and will default to recommending competitors that have established a much clearer, highly documented use case.
3. Share of narrative
Merely securing a mention in an AI response is insufficient if the context of that mention portrays your brand poorly, as high visibility within a negative framework can quickly become a strategic liability.
Share of narrative measures the qualitative attributes, adjectives, and associations linked to your brand name in conversational outputs, allowing you to understand how your business is being framed.
Narrative
What it tracks
The core strategic question
The “best” narrative
How often you are framed as the premium, gold-standard market leader.
Is the model positioning our brand as the most capable solution available?
The “popular” narrative
How often you are cited as the default, widely adopted industry standard.
Is the model identifying our brand as the most commonly used option?
The “budget” narrative
How often you are categorized as the cost-effective, value, or entry-level alternative.
Is the model framing our brand primarily as a low-cost, entry-level alternative?
If an AI engine includes your brand frequently but consistently describes your product as a complex, legacy system, your high share of voice may actually be damaging your sales pipeline.
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Reframing your success metrics
Leadership teams require competitive benchmarks to evaluate market performance, meaning you cannot simply stop reporting on share of voice without offering a viable alternative.
Transitioning your executive reporting smoothly requires a structured, three-step plan.
Reframing the executive narrative involves educating your leadership team on the limitations of modern AI dashboards.
This means explaining the hidden denominator problem and demonstrating why treating these figures as absolute metrics introduces unnecessary risk.
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People have been calling the keyword dead since at least 2010. Yet here we are in 2026, still using keywords to show ads on Google.
Advertisers weren’t wrong to equate the loss of control with the death of the keyword. The keyword simply couldn’t disappear until Google had something better to replace it.
At Google Marketing Live (GML) last month, we may have seen that replacement. AI Brief is a Gemini-powered control layer that lets you steer AI Max using prompts-first language.
At first glance, AI Brief may seem like just another AI Max feature. AI Max is still trying to gain traction among advertisers. So couldn’t advertisers simply ignore it and stick with keywords?
Probably not.
When users shifted to mobile, Google eventually pushed advertisers toward Enhanced Campaigns. The conditions may now be in place for a similar transition, this time from keywords to prompts.
Consider the other announcements from GML. AI Mode surpassed 1 billion monthly users. The search box is getting its biggest redesign in 25 years. Users in AI Mode are also submitting queries that are, on average, three times as long as traditional searches.
Whether advertisers like it or not, people are increasingly using prompts instead of keywords to find information.
With AI Brief, the replacement for the keyword finally exists. We can now target prompts with prompts. Combined with the consumer-driven shift away from keyword-based searches, that makes the keyword’s obituary much easier to believe.
The keyword is dying because users stopped using it
Most “keywords are dead” arguments over the past decade were supply-side stories. Google reduced broad match’s control, made RSAs decide the best ad variation, and let Smart Bidding set bids to help any keyword deliver on its underlying financial goals. They also stopped showing every query in search terms reports, all steps framed as Google taking the keyword away.
Now it’s different. The pressure is coming from the demand side.
People are asking Google longer, more conversational questions because Google built a search experience that invites them to. The new search box, the biggest upgrade in 25 years, dynamically expands as you type. You no longer pick a “mode” before you ask. The interface itself is telling consumers that “running shoes” is no longer the only way to ask for what they really want.
If you’re an advertiser, the question stops being “Do I want to use keywords?” It becomes “How do I show up in a query a keyword can’t possibly match?” Trying to capture a paragraph of context with three positive match types and one negative is, let’s be real, increasingly absurd.
Optmyzr’s 2026 Match Type Study shows the same pattern from the spend side. We analyzed 30,000 Google Ads accounts in February 2026 across all Search campaigns with active keyword spend. (Disclosure: I’m the cofounder and CEO of Optmyzr.)
Exact match has lost nearly 10 percentage points of spend share since 2022, while broad match has climbed steadily to become the dominant match type by budget.
Phrase match, meanwhile, consistently punches above its weight, holding the largest share of non-branded spend and leading on conversion rate in both ecommerce and lead-gen segments.
Advertisers are clearly growing more comfortable trusting Google’s AI with broader targeting, a shift attributed to Smart Bidding’s maturation rather than exact match losing its performance edge.
The other tell is that Google isn’t alone here. We recently started managing ads on ChatGPT, and OpenAI’s ad surface is keyword-optional from day one.
When the company that invented keyword advertising and the company reinventing search both ship a keyword-optional product, that means something. At this point, we’re just arguing about how fast the keyword is dying.
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AI Brief is a technical replacement for keywords
Unsurprisingly, AI was the topic that drove nearly every announcement at GML 2026. At I/O the day before, Sundar Pichai, Google’s CEO, even said that Google’s migration to become an AI-first company was nearing completion, with AI agents providing the final push and rewriting the last remaining code. Downstream from all the talk about AI is the realization that consumers now prompt rather than search with keywords.
AI Brief is one way to operationalize the required evolution for advertisers to keep up with consumer behavior. Powered by Gemini, it lets you describe, in your own words, what your business is, what your messaging should and shouldn’t say, the searches you want to capture or avoid, and the audience you’re trying to reach.
Google calls these messaging guidelines, matching guidelines, and audience guidelines. Internally, I think of it as: tell the model what you’d tell a new media buyer on their first day.
Then AI Brief echoes back how it understood your requests and shows preview samples of the assets and queries it thinks you meant. You push back if it’s off. You iterate. When you’re happy, you lock in the brief.
That’s a meaningfully different interaction model than a keyword list. A keyword list is a static artifact. A brief is a negotiation. It can adapt as your business changes without you reuploading hundreds of new keywords.
There’s a parallel in the world of coding, where AI has arguably had the biggest impact with agentic code writers and vibe-coding systems like Lovable.dev. The idea is that the code we write to have software achieve an outcome should be merely a temporary artifact reflecting the current abilities of the tech.
Coders should focus on writing the prompts that describe the goals of the web page rather than the code needed to achieve those goals. The prompt instructs the software what it should do and how to do it safely. AI can then write the code that executes the task on demand, using the latest capabilities while staying grounded in the prompts that define its purpose.
This is what Sam Altman called “software on demand” at the GPT-5 launch, the idea that AI can “instantaneously create an entire piece of computer software for you.”
Google echoed the same vision at I/O 2026, where Pichai described Search using Gemini and Antigravity to build custom experiences, dynamic layouts, and persistent mini apps on the fly. Software generated in response to what each user needs, in the moment they need it.
People need to be purposeful about work. Your purpose at work isn’t to write emails and work with spreadsheets. It’s to achieve certain outcomes, and writing emails and using spreadsheets is how that gets done. Stop worrying about how and start thinking about the real goal: growing your ad revenue by 10% while maintaining similar margins.
Keywords are the “how,” not the “why.” AI Brief is actually closer to letting us manage the “why” while letting AI figure out the “how.”
How to try AI brief now
AI Brief is rolling out in English for AI Max for Search first, then Performance Max and AI Max for Shopping. Existing text guidelines will migrate into AI Brief automatically as messaging guidelines.
So yes, this is starting as an AI Max feature, and you may not be using AI Max because several practitioners note that AI Max can pull in junk traffic on lead-gen accounts, competitor-heavy verticals, and new campaigns with thin signal. Some veteran marketers have been turning AI Max off in those situations.
The practical playbook shared during a recent PPC Town Hall is solid: start new campaigns in Phrase, promote the winners to Exact, and layer Broad and Smart Bidding on top once you have data.
With the advent of AI Brief’s matching guidelines, advertisers can further tweak their targeting by saying, “prioritize searches for X, avoid Y.” But this strategy still requires a human who knows the account to pull that lever. So don’t unplug your keyboard just yet.
The new funnel, and why short keywords still have a job
Andrew Lolk and Kirk Williams pushed me on a real edge case in the LinkedIn discussion that led to this piece: the newborn photographer whose entire business depends on someone in their city typing “newborn photographer” and converting on the first ad that shows.
Short, transactional queries won’t disappear. So why not keep traditional search campaigns with keywords around to handle these types of queries? I think it’s reasonable to have two campaign types for different jobs. But their relationship is a funnel, not a parallel.
Here’s how I see it shaping up:
AI prompts for discovery: “I just had a baby and I want to remember this period. What are some ideas?”
AI prompts for research: “Compare lifestyle newborn photographers to studio newborn photographers in the Bay Area.”
Short keyword to buy: “Newborn photographer Los Altos.”
If you only show up at the bottom of that funnel, you’re betting your entire business on being the first short-keyword click. If you’re not present in the discovery and research prompts above it, you’re not in the consideration mix when the short query happens.
The reason a user may do that short query is that they already know more or less who they’d buy from, and they’re now looking for the best offer from a shortlisted set of options. The conversational layer feeds the transactional layer. Ignore it, and the transactional volume eventually stops coming to you.
This is also why I don’t think Google maintains two parallel systems forever. The short-keyword volume will keep shrinking relative to AI prompt volume, and at some point, the economics of supporting both stop working.
Further, AI-first campaign types will soon be great at converting agentically, using the Universal Commerce Protocol and other new methods being developed to allow agents to transact for their humans.
What AI Brief does to the four human PPC roles
I’ve argued for years that PPC pros take on four roles in an automated world:
Teacher.
Doctor.
Pilot.
Restaurateur.
These roles continue to explain the PPC manager’s world quite well, but with some new nuance.
The teacher
This role is the most direct analogy. You used to teach the machine what to target by handing it the end result: a keyword.
The funny part is that for many of us, that keyword was already generated by feeding an LLM a prompt and cleaning up the output.
AI Brief lets you skip the lossy translation step. Hand the machine the prompt itself, not the artifact it produced. The teaching gets richer because nothing gets lost.
The doctor
The shift is from “prescribe Drug X” to writing down, in structured language, what the patient actually needs.
The treatment can then evolve as the patient’s condition and the available solutions change. Keywords were restrictive: one symptom, one prescription.
Briefs and prompts allow freedom and evolution. That’s what good medicine looks like, and that’s what good targeting looks like now.
The pilot
We need a new instrument panel. If we’re not aiming at keywords anymore, the search query report stops being the right gauge of how well Google is matching intent.
We’ll probably see more search themes (buckets of intent that AI Brief is mapping into) replacing the line-by-line query list.
The restaurateur
You write the menu and the concept brief so the chef (the AI) cooks. AI Brief is almost literally the concept brief.
You define the cuisine, the values, the things the chef must never serve, and the kind of guest you’re cooking for. Then you taste, correct, and iterate. The kitchen runs.
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Why AI Brief feels different
The keyword isn’t dying because Google decided to kill it. It’s dying because consumers stopped phrasing their needs in a couple of words.
AI Brief is the first structural replacement that seems to allow advertisers to express their intent in as rich a manner as consumers can now express theirs to a chatbot. That’s why this GML announcement felt like a more serious nail in the coffin of the keyword than the last several.
Control was about dictating keywords to Google. Leverage is about feeding the engine the right brief and letting the auction execute at a scale no human team can match.
We don’t have to escape automation. We have to coexist with it on better terms. AI Brief is a great eventual replacement for the keyword. Hand it your prompt. Watch what it does.
Push back. Lock it in. Then you can move on to the parts of the job a machine can’t do, like knowing your customers and working on the goals that move their business in the direction they want.
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Most PPCbudget discussions focus on finding the right split between brand awareness and conversion-focused campaigns. That’s usually the wrong goal.
The optimal balance changes constantly based on business stage, market saturation, seasonality, competitive pressure, and revenue objectives.
Yet many teams still treat the funnel split as a fixed decision: 40% upper funnel, 60% lower funnel, set it and forget it. That might be the right ratio today and completely wrong in six months.
Every budget conversation eventually comes down to the same argument. Someone wants to cut brand awareness spend because it doesn’t convert directly. Someone else warns that if you only chase conversions, the pipeline dries up in 12 months.
Both are right, which is what makes this so difficult.
The lower-funnel case is easy to make
When most PPC managers talk about the lower funnel, they mean Shopping, Performance Max, and high-intent Search.
Someone typing “buy running shoes new york” has already decided they want the product. Shopping shows the right SKU at the right price. PMax chases the conversion signal across every Google surface. The attribution is clean, the ROAS is visible, and the CFO is happy.
The problem is that this demand already exists. These campaign types harvest intent. They don’t create it. Every conversion you get from a high-intent search term or a Shopping click is the result of awareness that was built somewhere else:
A YouTube pre-roll.
A friend’s recommendation.
A social post.
Years of brand presence in the market.
You’re collecting fruit from a tree you didn’t plant.
Search is worth treating separately here because it doesn’t sit neatly at the bottom of the funnel. A query like “best running shoes for marathon training” is informational.
The person is researching, not buying. AI Max and broad match expansion in Google Ads are pushing Search campaigns further into this territory, meaning Search can serve both ends of the funnel depending on how it’s configured and which queries it actually captures.
It’s worth auditing your Search terms regularly through this lens: How much of your Search spend is closing existing demand versus reaching people earlier in their decision-making process?
This works until it stops working. And the signal that it’s stopping usually arrives too late.
When branded search volume flatlines, CPCs on your core terms keep climbing because the same pool of high-intent users is getting more expensive to reach, and new customer acquisition starts to plateau while retention holds steady. These are the symptoms of a brand that’s been living off existing demand without replenishing it.
Lower-funnel efficiency is real. But it’s also borrowing against the future.
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The reseller trap: When your lower funnel depends on someone else’s brand
There is a version of this problem that’s specific to resellers and multi-brand ecommerce, and it doesn’t get discussed enough.
If you sell branded products you don’t own, your lower funnel can work extremely well in the short term.
Shopping and Search campaigns for established brands convert efficiently because the brand owner has already done the awareness work. You’re harvesting demand that Nike, Adidas, or whoever else has spent years and significant budgets building.
The structural risk is that you have no control over that demand. If the brand owner reduces its marketing investment, pulls out of a market, or simply fades in relevance, your Shopping and Search volume follows.
You can’t counter it with your own PPC spend because the underlying interest isn’t there to harvest. The tree stops producing fruit, and you never owned it.
This creates two strategic imperatives that are easy to deprioritize when the lower funnel is performing well.
Own-brand development: products or lines that you control, where you own the brand equity and can invest in awareness independently.
Reseller brand building: investing in the upper funnel to make your own name well known, so customers think of you as the destination regardless of which brands you carry. A consumer who searches for your store name rather than a specific brand is much more resilient than one who only finds you through a branded product query.
Both require some form of upper-funnel investment. Own-brand development needs awareness campaigns to build product recognition from scratch. Reseller brand building needs a consistent presence across Demand Gen, YouTube, and Display to make your name synonymous with the category, not just the brands within it. That’s only within Google’s ecosystem.
To complete the picture, you might also include SEO, word of mouth, pop-up events, local advertising, and more. Brand building has no limits.
Neither of these investments shows up in this month’s ROAS report. Both show up in next year’s business resilience.
Brand awareness spend is often framed as the soft, hard-to-measure part of the budget. The part you do when you have money left over. That framing gets it exactly backward.
Upper-funnel investment is how you build the pool of future converters. Every person who sees a Demand Gen ad on YouTube or Google Display today and doesn’t click isn’t a failed impression. They’re a potential high-intent searcher in three weeks. You’re filling the top of the pipeline that your Shopping and Search campaigns will harvest later.
Google’s Demand Gen campaigns make this dynamic particularly visible within a single platform. You can run Demand Gen to reach in-market audiences who don’t yet know your brand, then watch Search impression share and branded query volume respond over the following weeks. The lag is real and measurable.
Upper-funnel spend today shows up in lower-funnel performance next month, not this week. That delay is why it gets cut first when budgets tighten, and why cutting it tends to hurt six to eight weeks later rather than immediately.
Teams that manage this well think of Demand Gen not as brand spend, but as pipeline investment. The question isn’t “What is the ROAS on this campaign?” It’s “How much qualified demand am I creating for my Shopping and Search campaigns to close?”
The 70/30 or 60/40 rules you read about are averages across many businesses in many contexts. They’re useful as a starting point and useless as a long-term policy.
Consider what changes the optimal split.
A new product launch needs heavy upper-funnel investment upfront because awareness is zero.
A mature product in a saturated category needs it, too, because every competitor is also harvesting the same pool of high-intent searchers, and the only way to grow is to expand the pool.
A seasonal business approaching peak needs to have already done its upper-funnel work before the peak hits because awareness doesn’t respond fast enough to be built in-season.
Equally, a business in financial distress or facing a short-term revenue target can’t afford to wait eight weeks for upper-funnel investment to mature. The right answer in that moment is to focus on the lower funnel, accept the trade-off consciously, and plan to reinvest in awareness as soon as the pressure lifts.
The point is that both of these decisions are correct in context. A fixed split ignores context entirely.
Building a dynamic split logic
Rather than a fixed ratio, the most useful framework is a set of conditions that trigger a shift in either direction.
Shift budget toward upper funnel when:
Branded search volume is flat or declining quarter over quarter.
New customer acquisition cost is rising while retention metrics hold.
You’re entering a new market or launching a new product.
Competitors are visibly increasing their brand presence.
You’re approaching a peak season with at least six to eight weeks of runway.
You’re a reseller whose top brands are showing declining search interest or reduced marketing activity.
Shift budget toward lower funnel when:
You have a short-term revenue target that can’t wait.
Upper-funnel campaigns have been running long enough to build measurable awareness, and the conversion window is now.
Cost per acquisition on Shopping or Search is below target, and scaling makes sense.
Audience saturation on Demand Gen is high, meaning you’re reaching the same people repeatedly without expanding reach.
Within Google Ads, the data to monitor this is available without external tools. Branded query volume in Search Terms, impression share trends on non-branded terms, Demand Gen reach and frequency metrics, and new versus returning customer segmentation in conversion data together give you a reasonable picture of where the funnel is healthy and where it isn’t.
The review cadence matters as much as the metrics. Monthly is the minimum for a funnel split review. Quarterly is too slow. By the time a quarterly review catches a declining branded search trend, you’ve already lost several weeks of pipeline-building time.
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The conversation nobody wants to have
The reason funnel balance stays broken in most organizations isn’t analytical. It’s political.
Lower-funnel spend is easy to defend in a meeting. The ROAS is there, the conversion numbers are there, and the CFO can see a direct line between spend and revenue.
Upper-funnel spend requires a different kind of argument: “This investment will make our Shopping and Search campaigns work better in six weeks.” That argument is harder to make, easier to cut, and almost impossible to defend when someone asks for a quick win.
The answer isn’t to stop making the argument. It’s to change the evidence you bring to it.
Track branded search volume as a leading indicator.
Build a view that shows Demand Gen reach in month one and Search conversion volume in month two alongside each other.
Make the lag visible and the relationship concrete. Once the data tells the story, the conversation gets easier.
Budget allocation isn’t a one-time decision. It’s an ongoing signal about what kind of growth you’re building.
Optimizing purely for this month’s ROAS is a choice. So is investing in the demand that will drive next quarter’s revenue.
And if you’re a reseller, it’s also a decision about whether your business is built on a foundation you control or one you’re renting from brand owners who have their own priorities.
The best PPC teams do both, and they know when to lean in each direction.
Commerce has undergone several major shifts over the past few decades. What started with localized physical stores evolved into borderless, internet-driven ecommerce experiences.
Now, with the rise of AI, it is believed that commerce could be heading toward another transformation: agentic commerce, where AI agents help consumers discover products, compare options, and even complete purchases on their behalf.
Yet despite the excitement, many questions remain. Will consumers trust AI agents with buying decisions? Will businesses see enough return on investment to justify the costs? And does autonomous shopping solve a real problem, or simply add another layer of complexity to the buying journey?
Still, the technology is advancing rapidly. Imagine a shopping experience where consumers no longer jump between tabs, compare dozens of products on different websites, or manually research every purchase. Instead, AI agents understand intent, evaluate options, compare prices, and act within predefined rules to help users make purchasing decisions. What once sounded futuristic is already beginning to take shape.
In this article, we’ll explore what agentic commerce is, how it works, the technological developments driving it forward, and some of the challenges that could shape its future adoption.
Key takeaways
Agentic commerce represents a shift where AI agents assist consumers in product discovery, comparisons, and purchases
AI agents execute tasks based on user intent, simplifying the shopping journey and enhancing efficiency
Consumer interest is growing, with over 60% expecting to use AI in their shopping experiences by 2026
Technological developments like the Agentic Commerce Protocol (ACP) and Universal Commerce Protocol (UCP) are crucial for enabling agentic commerce
Despite its potential, agentic commerce faces challenges related to consumer trust, security, and the need for business investments.
What is agentic commerce?
In simple terms, agentic commerce refers to a commerce model where AI agents act as decision-makers on behalf of customers.
Instead of manually searching for products, comparing options, filtering results, and completing purchases, users can rely on AI agents to handle these tasks based on their intent, preferences, constraints, and buying goals.
I could literally say into the into a phone to my agent, go and buy me some new shoes with that jacket I bought last week, and that’s it.
And it would go away.
It would do the research.
And of course, you can have a say in an approval in terms of part of the journey.
At its core, agentic commerce works like a digital shopping proxy. Humans define the intent or goal, while AI agents execute the process behind the scenes. While the AI handles the heavy lifting, users still remain in control of the final decision-making process.
Agentic commerce is the next big thing in ecommerce
The concept of agentic commerce may still sound futuristic, but the shift has already started. Consumer behavior, AI adoption, and industry forecasts all point to a future in which AI agents become an active part of the buying journey.
Here are some numbers that highlight why agentic commerce is emerging as the next major evolution in ecommerce.
Consumers already use AI in their buying journey
Consumers have already started relying on AI-powered tools to discover products and make purchasing decisions. According to a McKinsey & Company report, more than 70% of AI-powered search users ask top-of-the-funnel questions about categories, brands, products, or services.
Example of a TOFU research performed on Claude
The same report also found that nearly 50% of consumers already use AI-powered search experiences today. As AI increasingly becomes part of product discovery, traditional search-driven traffic may face growing disruption. In fact, the study suggests that businesses could see 20–50% of their traffic shift away from traditional search experiences over time.
This highlights an important shift: consumers are no longer just searching; they are increasingly asking AI systems to guide their decisions.
Shoppers are expecting agentic commerce
Consumer interest in AI-assisted shopping is also growing rapidly. The 2025 report titled “Agentic Commerce: From Brand Loyalty to Bot Logic” explored how shoppers feel about AI agents in retail experiences.
The report found that more than 60% of shoppers expect to use agentic AI in 2026. The findings also revealed a major behavioral shift: consumers increasingly prioritize convenience, speed, pricing, and trust over platform loyalty.
Instead of browsing individual retailer apps, shoppers may rely on AI agents that can compare products across multiple platforms, evaluate reviews, identify the best deals, and complete purchases more efficiently. This changes the competitive landscape from retailer-versus-retailer competition to AI-driven discovery ecosystems.
Analysts predict explosive growth for agentic commerce
Industry analysts also expect agentic commerce to become a massive economic opportunity over the next few years. Another McKinsey report suggests that agentic commerce could fundamentally reshape the shopping experience.
Based on the growing adoption of AI-powered discovery tools and increasing merchant readiness, the report estimates that by 2030, the US B2C retail market alone could unlock an orchestrated revenue opportunity of $900 billion to $1 trillion. Globally, that opportunity could range from $3 trillion to $5 trillion.
How does agentic commerce work?
At its core, agentic commerce combines human intent with AI-driven execution. Instead of manually browsing websites, comparing products, and completing purchases, users can delegate much of the shopping journey to AI agents. These agents understand goals, evaluate options, make decisions within defined constraints, and even complete transactions on behalf of users.
What makes this different from traditional AI assistants is the ability to act. While assistive AI tools mainly provide information or recommendations, agentic AI can independently execute tasks across the shopping journey.
Here’s a step-by-step look at how agentic commerce works:
Step 1: Capturing the intent
Every agentic commerce journey begins with intent. Instead of typing short keywords into a search bar, users interact with AI agents conversationally.
For example, a shopper might say:
“Find me a durable pair of running shoes under $150.”
“Restock groceries for a vegetarian dinner party.”
“Buy a formal shirt that matches the trousers I purchased last month.”
At this stage, the AI agent focuses on understanding the user’s goals, preferences, budget, delivery expectations, and constraints. If the request feels too broad, the agent may ask follow-up questions to refine the intent before moving forward.
Step 2: Autonomous instruction execution and brand discovery
Once the intent becomes clear, the AI agent begins executing the task autonomously. Instead of searching a single website, it scans multiple ecommerce platforms, marketplaces, product catalogs, reviews, pricing databases, and inventory systems simultaneously.
This is where agentic commerce begins to change traditional product discovery. Rather than showing endless product pages, the agent narrows down the most relevant options based on the shopper’s needs.
At the same time, brands with better-structured product data, accurate inventory information, transparent pricing, and machine-readable content are more likely to be discovered by AI agents.
After gathering options, the AI agent starts evaluating products and comparing tradeoffs. It may analyze factors such as:
Price and discounts
Product specifications
Customer reviews and ratings
Shipping timelines
Return policies
Brand trust and reputation
Instead of simply listing products, the agent reasons through the options and explains why certain products better meet the shopper’s requirements than others.
Users can also refine the decision-making process further by adding conditions such as:
“Only show products with free returns.”
“Prioritize faster delivery.”
“Exclude refurbished products.”
This creates a feedback loop where the AI agent continuously improves its recommendations based on user preferences.
Step 4: Purchase
Once the shopper approves a product or sets predefined rules, the AI agent can move forward with the transaction. Using APIs, commerce protocols, and secure payment systems, the agent can add items to carts, apply discounts, authenticate payments, and complete purchases.
In some cases, the purchase may happen instantly. In others, the AI agent may wait for specific conditions, such as a price drop, stock availability, or faster delivery options, before completing the transaction.
Even though the AI handles execution, users still remain in control through permissions, approval settings, and spending limits.
Step 5: Post-purchase support
The role of AI agents does not end after checkout. Agentic commerce also extends into post-purchase experiences.
AI agents can continue assisting users by:
Tracking deliveries
Managing returns or exchanges
Monitoring refunds
Sending delivery updates
Reordering recurring products
Recommending complementary products or accessories
This turns shopping into an ongoing and intelligent experience rather than a one-time transaction.
Technological developments
Agentic commerce is not powered solely by AI models. Behind the scenes, it depends on a growing ecosystem of protocols, frameworks, APIs, and payment systems that help AI agents interact with digital commerce platforms securely and efficiently.
One important concept shaping agentic AI is the Model Context Protocol (MCP). In agentic AI, MCP enables AI models to connect with external systems, tools, databases, and applications via a standardized communication layer.
Instead of building separate integrations for every AI model and every software platform, MCP creates a common framework that allows AI agents to access information and execute actions more consistently. Think of it like creating a shared operating language between AI systems and digital tools, so they can work together without requiring completely custom connections every time.
As agentic commerce evolves as a use case of agentic AI, similar commerce-focused protocols are now emerging specifically for shopping ecosystems. These protocols help AI agents understand product information, communicate with merchants, compare inventory, and securely complete transactions on behalf of users.
Here are some important developments supporting agentic commerce:
Agentic Commerce Protocol (ACP)
One of the most important developments in this space is the Agentic Commerce Protocol (ACP), an open standard introduced by Stripe in collaboration with OpenAI.
ACP is designed to help AI agents interact more naturally with ecommerce systems by creating a standardized framework for product discovery, checkout, and payment execution. In simple terms, it provides the infrastructure that allows AI agents to move beyond simply recommending products and actually complete purchases securely on behalf of users.
The protocol is still in its early stages, but its first real-world implementations are already emerging. For example, ChatGPT users in the United States can already purchase products from Etsy merchants directly within the chat experience through Stripe-powered checkout. Shopify integrations are also expected to follow.
This matters because it signals a shift from AI-assisted discovery to AI-enabled transactions happening inside conversational interfaces themselves. Instead of redirecting users across multiple websites and checkout flows, ACP aims to make the entire shopping journey more seamless and agent-friendly.
Another important aspect of ACP is its open-standard approach. Rather than creating a closed ecosystem tied to a single platform, Stripe and OpenAI position ACP as a framework that developers, merchants, and ecommerce platforms can adopt more broadly as agentic commerce evolves.
Looking ahead, protocols like ACP could become foundational infrastructure for AI-driven shopping experiences, especially as more businesses begin to optimize their product catalogs, payment systems, and checkout experiences for AI agents rather than only human users.
As more AI agents enter the shopping journey, a new challenge emerges: how can these agents communicate with thousands of retailers, marketplaces, payment providers, and service platforms without requiring a custom integration for each one?
Introduced by Google, UCP is an open standard designed to create a common language for agentic commerce. Rather than building separate connections between every AI agent and every commerce platform, UCP provides a shared framework that allows them to communicate more efficiently throughout the shopping journey.
Think of it this way: if agentic commerce becomes mainstream, millions of AI agents could research products, check inventory, compare prices, place orders, and manage returns every day. Without a standardized framework, retailers and AI platforms would need to create and maintain countless one-to-one integrations. UCP aims to remove this complexity by providing a common set of rules for all participants to exchange commercial information.
What makes UCP particularly interesting is its broad scope. Unlike protocols that focus mainly on purchasing, UCP is designed to support the entire commerce lifecycle, including:
Product discovery
Product comparison
Purchasing and checkout
Order tracking
Returns and post-purchase support
Google has also designed UCP to work alongside other emerging AI standards, including Agent2Agent (A2A), Agent Payments Protocol (AP2), and Model Context Protocol (MCP). This allows businesses to adopt agentic commerce without completely replacing their existing systems.
The initiative already has significant industry backing. Google co-developed UCP with major commerce companies, including Shopify, Etsy, Wayfair, Target, and Walmart. It has also received support from companies such as Mastercard, Visa, Stripe, and American Express.
Platforms that support Universal Commerce Protocol
While agentic commerce is still in its early stages, UCP represents an important step toward a future in which AI agents, merchants, and payment providers can operate within a single ecosystem rather than through isolated platforms. In many ways, it provides the foundational infrastructure needed to make agentic commerce scalable across the broader digital economy.
Mastercard Agent Pay
While protocols like ACP and UCP focus on communication and interoperability, Mastercard Agent Pay focuses on one of the most critical challenges in agentic commerce: trust and secure payment execution.
As AI agents gain the ability to discover products, compare options, and make purchasing decisions, they also need a secure way to complete transactions on behalf of users. Mastercard Agent Pay was introduced to provide the infrastructure for exactly that.
The platform is designed to allow AI agents to execute payments while operating within user-defined permissions, authentication requirements, and spending controls. Rather than giving AI systems unrestricted access to payment credentials, Agent Pay focuses on creating verified, traceable, and authorized payment flows for agent-driven commerce.
One of the most significant developments came through its collaboration with PayPal, where Mastercard Agent Pay is being integrated into PayPal’s wallet infrastructure. It allows AI agents to securely complete transactions on behalf of PayPal users while maintaining the security and trust mechanisms that consumers already expect from digital payments.
This partnership is particularly important because it moves agentic commerce closer to real-world adoption. Instead of existing only within experimental AI environments, agent-driven payments can potentially operate across a much larger ecosystem of merchants, consumers, and payment networks.
Together, ACP, UCP, and Agent Pay are helping lay the foundation for agentic commerce. While ACP focuses on enabling AI agents to interact with merchants and complete purchases, UCP creates a common language that allows agents, retailers, and platforms to work together at scale. Agent Pay adds the trust layer by enabling secure, authorized payments, bringing AI-driven shopping one step closer to reality.
FAQs: What is agentic commerce?
What are the benefits of agentic commerce for enterprises and users?
Agentic commerce benefits both businesses and consumers by making shopping more efficient and personalized.
For users AI agents can reduce research time, provide tailored recommendations, monitor prices, and automate routine purchases.
For enterprises Agentic commerce can streamline operations, improve personalization, automate repetitive workflows, support faster decision-making, and help products reach customers more quickly. Together, these benefits create a more convenient shopping experience while improving operational efficiency.
Are agentic AI and agentic commerce the same?
No, they are not the same. Agentic AI is the underlying technology that enables AI systems to understand goals, make decisions, and complete tasks autonomously. Agentic commerce is a specific application of agentic AI in shopping and commerce. In other words, agentic AI is the foundation, while agentic commerce is one of its real-world use cases.
What’s the difference between traditional commerce and agentic commerce?
In traditional commerce, the shopper remains the primary decision-maker and executor throughout the buying journey. Even when AI is present, its role is largely limited to recommending products or improving search experiences. In agentic commerce, AI agents actively participate in the shopping process by researching products, comparing options, and executing tasks on behalf of users, guided by predefined goals and preferences.
Can you share some practical, real-world use cases for agentic commerce?
Several companies are already experimenting with agentic commerce. For example, Amazon has introduced its “Buy for Me” feature, which allows AI agents to purchase products from third-party websites when items are unavailable on Amazon.
Similarly, Google is testing AI-powered shopping experiences that can monitor prices and automatically purchase products when they meet user-defined conditions. Beyond consumer shopping, businesses are also using AI agents to monitor inventory levels and automatically reorder supplies when stock runs low.
Agentic commerce still faces important questions
While the technology behind agentic commerce is advancing quickly, widespread adoption is far from guaranteed. Many consumers may not feel comfortable giving AI agents the authority to make purchasing decisions or access payment methods on their behalf. Others may question whether autonomous shopping solves a real problem or simply makes it easier to buy more things, more often.
Businesses face their own uncertainties. Supporting agentic commerce may require investments in new protocols, structured data, integrations, and AI-ready commerce experiences. Whether those investments yield measurable returns remains unclear, especially given that consumer adoption is still in its early stages.
There are also broader challenges to solve, including security, fraud prevention, AI bias, platform dependency, and the potential loss of direct relationships between brands and customers. Agentic commerce may represent an exciting new direction for digital shopping, but its long-term success will depend on whether it can create value for consumers, merchants, and the broader ecommerce ecosystem, not just the AI platforms powering it.
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