I’m watching Google add a new layer of AI transparency to ads across Search, YouTube, and Discover. The company said its new How this ad was made section will appear inside My Ad Center, giving people a clearer view of whether AI played a role in the ad creative they see.
The panel will show whether an ad was created or modified with AI. I see this as a meaningful expansion of Google’s ad transparency tools, especially as more advertisers rely on generative AI to produce images, copy, and other campaign assets at scale.
What it looks like. I’ll be able to access the disclosure from the three-dot menu or the info icon on an ad. In the screenshot Google shared with Search Engine Land, the My Ad Center panel includes a dedicated section explaining how the ad was made.
Google will handle some disclosures. When advertisers use Google’s own generative AI ad tools, Google will automatically add the disclosure inside My Ad Center.
Google’s My Ad Center adds a clear AI disclosure, helping users see when ad creative may have been created or edited with generative AI.
For advertisers using third-party AI tools, Google said they will have control over whether to disclose AI use. Depending on local requirements, an AI label may also appear directly on the ad, either automatically or after the advertiser uses that control.
Why I care. AI-generated ads are getting easier and faster to create, so disclosure matters more than ever. I want to know when creative was made or changed with AI because requirements can vary by market, platform, and ad format.
Existing ad rules still apply. Google said its ad policies still prohibit misleading or deceptive advertising, whether AI was involved or not. This update adds more visibility into how an ad was made, but it does not change the requirement that advertisers clearly identify who they are and what they are promoting.
A bold Google logo sits atop layered, colorful digital documents, evoking the fast-moving world of search marketing, ad formats, campaign assets, and platform updates.
Earlier AI safeguards. Google already embeds imperceptible signals, including SynthID, into content created with its generative AI tools. Election advertisers are also required to disclose synthetic or digitally altered content in political ads, under a policy Google introduced in 2023.
I have watched paid search change into something far faster and less forgiving than the old reporting rhythm was built to handle. Auction dynamics shift by the hour, competitor bids move in real time, and search behavior changes across devices, times of day, and audience segments before a monthly report can even catch up.
For me, the real cost has always lived in the gap between a performance signal and the moment a person can respond. groas is built to close that gap every hour of every day, and the data shows what can happen when that response loop gets dramatically shorter.
When I sign up with groas, the process starts with a human account manager auditing the existing Google Ads account in detail. This is not a quick surface check. Campaign structure, keyword strategy, bidding logic, budget allocation, conversion tracking, quality scores, search term reports, and auction insights all get reviewed.
I see that audit as the foundation for everything that follows. groas optimizes toward the goals and account structure defined in the roadmap, so a clean conversion hierarchy, accurate tracking, and a well-organized account give the system stronger signals to work with. That early human judgment matters because it shapes the machine’s operating environment.
From there, I like that the rollout is paced across the first 60 days. The system does not start moving aggressively before it understands the account it is working in.
Weeks 1 to 2, observation: groas ingests historical performance data, establishes baselines, and maps patterns across search terms, device performance, time-of-day variance, and audience behavior. During this stage, no changes are made while the system learns the account.
Weeks 3 to 4, calibration: The system starts making targeted optimizations, including bid adjustments, negative keyword additions, match type refinements, and budget reallocations between campaigns. These are deliberate campaign-by-campaign changes, so each move can build on the last.
Weeks 5 to 6, traction: I begin to see early changes show up in the data. Performance shifts become visible across ROAS, conversion value, and wasted spend as the optimizations compound.
Weeks 7 to 8, scaling: Around the 60-day mark, the account has usually stabilized enough for groas to scale. More budget moves into the campaigns and keywords with the strongest conversion history, expanding from a proven base instead of guessing.
A Google Ads performance snapshot tracks April 2026 shifts in conversions, ROAS, conversion value and cost, highlighting the volatility behind paid search optimization.
Once groas is running, I see it work across the full account the way a skilled team would, except it does not stop. It writes and tests ad copy, deploys dynamic landing pages that adjust around each search, turns ad groups on and off when performance calls for it, moves budget where it earns the most, and adjusts bidding strategies in response to live signals.
Anything a person can do inside Google Ads, groas can do too, around the clock.
Capability matters, but results matter more.
The clearest way I can explain the value of continuous, full-surface management is through a real account groas took over. It was a high-spend search account in a tough paid search category: a U.S.-based online mobile recharge platform that lets people instantly top up prepaid mobile phones across major U.S. carriers without creating an account or paying added transaction fees.
This business operates in prepaid wireless, serving many pay-as-you-go and underbanked customers who recharge monthly or even more often, usually right when their balance runs out. That model puts Google Ads at the center of growth.
Demand is intensely intent-driven. When someone’s credit runs out, they search for a way to recharge and often buy within minutes. Capturing that moment is the whole game. But it is also a punishing channel to manage profitably because transactions are low-value and high-volume, margins are thin, and the auction is crowded with carrier brand terms and generic “recharge” and “top up” searches.
In an account like this, a few cents of wasted CPC multiplied across hundreds of daily conversions can decide whether the account is profitable or quietly leaking money.
In this account, a conversion meant a completed recharge. So the numbers are not abstract to me. Every point of ROAS and every additional daily conversion means more recharges processed and more revenue generated on the same budget base.
A Google Ads reporting view tracks PPC performance after optimization, with conversions, ROAS, conversion value and spend moving across a month of campaign activity.
The comparison looked at two account reporting periods: before groas assumed optimization and after.
Spend: up 18% to $164,000.
ROAS: up 30%.
Average CPC: down 15%.
Conversions per day: up 29%.
Conversion value: up 44%.
Cost per conversion: down 14%.
The clearest improvement was return on ad spend. ROAS rose from 1.02x to 1.32x, which is roughly a 30% improvement in value returned for each dollar spent.
A Google Ads trend chart marks the moment groas was connected, with conversion, cost, ROAS and value lines tracking performance shifts through spring 2026.
At the same time, average cost per click fell from $2.34 to $2. But the more important point is what the account did with the clicks it paid for. Conversions and conversion value both grew faster than spend, which means each dollar worked harder than it had under the previous setup.
Daily conversions rose from 571 to 739, about 29%. Daily conversion value rose even faster, from $4,702 to $6,772, or roughly 44%.
What stands out to me is that these gains came through consolidation, not expansion. groas focused spend into 10 active search campaigns, down from 17.
Budget that had been spread thinly across underperforming campaigns was redirected into the keywords and campaigns with the strongest conversion history. Fewer campaigns, lower click costs, and more value returned created a cleaner, more focused account.
That is what an account looks like when waste is removed and budget is concentrated where it can compound.
The mechanism behind results like these is speed plus breadth of attention. Under traditional management tied to weekly or monthly reporting cycles, an underperforming search term might run for 7 to 14 days before anyone acts. A target CPA can drift far from its goal between reviews. An autonomous system narrows the time between signal and response to hours while watching every campaign at once.
As groas gathers more data on audience behavior, search patterns, and conversion value, its decisions become more precise. Budget can then concentrate further into the campaigns that return the most value.
That is the structural difference I see between autonomous management and periodic manual review. Each optimization creates new data, and that data informs the next decision. A system running continuous observe-and-optimize cycles can draw more signal from the same account over time.
Rows of illuminated data cabinets and paper files stretch into the distance, capturing the pressure on marketers to turn fragmented customer data into a smarter performance engine.
Business context still belongs with the people who understand the business. When a client launches a new product line, changes pricing, or redefines which conversions matter most, that direction has to come from a person. groas optimizes toward the goal it is given, and setting that goal is strategic work.
Creative is where I see the human and machine layers working together most clearly. groas writes and tests ad copy and landing page variations at a pace no human team could match, while the people on the account define brand voice, positioning, and creative direction. The strategist shapes the message, and groas finds the specific wording and layout combinations that convert.
For businesses ready to see better results
If I am looking at a current setup that runs on monthly reports and weekly changes, I expect to find a steady gap between what the data says and what actually happens in the account. That gap is where budget gets wasted and opportunities close. In the account above, it showed up as more than 15 active search campaigns, many spending inefficiently, with budget spread too thin to compound.
groas’s onboarding is structured to keep the transition low-risk. The first two weeks are analysis only, measured changes follow, and meaningful performance shifts usually appear within the first month or two, with scaling beginning around day 60. Live campaigns keep running throughout calibration, and the initial audit grounds changes in context from the start.
For businesses that have stayed with the same agency for a long time without material improvement, I would expect the audit alone to surface issues that have gone unaddressed.
I do not think execution-layer account management scales well on its own.
Continuous optimization, bid management, negative keyword maintenance, and budget pacing take a lot of time at volume. As an agency adds clients, it usually has to add headcount or accept that some accounts get less attention than others. Most agencies know exactly which accounts are underserved.
With groas handling execution autonomously across a client portfolio, I can see the team shifting toward strategy, client relationships, and new business.
The work that differentiates an agency is also the hardest to automate. Clients see stronger results, and team capacity moves toward the work that creates the most value.
I’m adjusting how I refer to Google’s shopping platform now that Google has dropped “Next” from Merchant Center Next. Going forward, the product is simply called Google Merchant Center.
Google made the change official in a Merchant Center announcement, saying, “The platform you use today will simply be referred to as Google Merchant Center.” For anyone managing product feeds, shopping campaigns, or merchant accounts, this is mainly a naming update rather than a product change.
I remember when Google Merchant Center Next was introduced in 2023 as the newer version of the old Google Merchant Center. Over the past few years, more merchants, site owners, and advertisers moved into that updated experience.
At this point, it appears that Merchant Center Next has effectively become the standard experience. So Google is removing the “Next” branding and returning to the simpler name: Google Merchant Center.
Rows of illuminated data cabinets and paper files stretch into the distance, capturing the pressure on marketers to turn fragmented customer data into a smarter performance engine.
Google said users will start seeing the “Next” branding removed from Help Center articles, email communications, and the Merchant Center interface.
Google also clarified that no action is required and that the name change does not affect existing accounts. In other words, I do not need to update settings, migrate anything, or make account-level changes because of this rebrand.
Why does this matter? When I talk about Google’s merchant tools now, I can leave off “Next” and just call the platform Google Merchant Center. Honestly, that is what many of us were already calling it anyway.
When I’m running Google Ads in 2026, one setting I always check carefully is “Search Partners.” It often appears in campaign settings as a simple way to extend reach beyond Google Search, and on the surface, that sounds useful.
But more reach does not automatically mean better reach. In my experience, Search Partners can bring traffic, but the quality of that traffic is usually the problem.
For most advertisers, I would not leave Search Partners enabled by default. I’d rather start with the main Google Search results page, prove the campaign can convert, and only then consider whether extra volume is worth testing.
What are Google Search Partners?
Google Search Partners are third-party sites that use Google-powered search results. When someone searches on those sites, your ad may be eligible to show there. This network can include YouTube, directories, other search experiences, and even parked domains.
That sounds like a broader opportunity, but I usually see a familiar pattern: lots of impressions, plenty of clicks, and cheaper CPCs than Google Search. The issue is that cheaper clicks are not always useful clicks. Real conversions and meaningful business value from these placements are often limited.
If I’m using conversion-focused Smart Bidding, I often see Search Partner spend fall naturally over time. The bidding system eventually learns that those placements are not producing the conversions it wants, so it stops pushing budget there.
How Search Partners differ from the Google Display Network
I see advertisers confuse Search Partners with the Google Display Network all the time. Some websites can be involved in both, but the intent and placement logic are different.
The Google Display Network is made up of websites and apps that use AdSense, where ads can appear while people browse content. It can show up as a placement option in Demand Gen, Video campaigns where it is called “Video Partners,” and Performance Max campaigns.
Search Partners are tied to search-based activity. That is why they apply to Search, Shopping, and Performance Max campaigns rather than standard Display placements.
How I audit Search Partner performance
I do not recommend taking anyone’s word for it, including mine. The better move is to check what Search Partners are actually doing inside your own Google Ads account.
Rows of illuminated data cabinets and paper files stretch into the distance, capturing the pressure on marketers to turn fragmented customer data into a smarter performance engine.
For Search or Shopping campaigns
In Google Ads, I go to the campaign view, select Segment, and choose Network (with search partners). This splits performance into separate rows for Google Search and Search Partners, which makes the difference much easier to see.
What I usually find is a lot of Search Partner impressions and clicks, often at lower CPCs than Google Search. But when I look for true conversions, the results are usually weak unless the account is tracking something shallow or easy to manipulate, such as a page view or a low-friction form fill.
For Performance Max campaigns
Performance Max works differently. Search Partners are required for this campaign type, so I cannot simply opt out. What I can do is monitor the activity through the Channel Performance report.
If I see heavy Search Partner spend in a Performance Max campaign, I treat it as a signal to review conversion tracking, bid strategy settings, and the quality of the conversion actions being used for optimization.
Check the Content Suitability report
For more transparency, I also check the Content Suitability report under Insights and reports. This report can show the actual websites or YouTube channels where ads appeared on the Search Partner network.
That list is often enough to make the decision clear. Once I see where the ads have been running, I usually find many placements that look low quality, irrelevant, or simply not worth the spend.
In Google Ads, many decisions really do depend on the account, the market, and the goal. This is one of the few areas where my starting recommendation is straightforward.
If I’m building a new Search or Shopping campaign, I leave Search Partners unchecked. After the campaign is performing well and has strong conversion data, I may test Search Partners for added volume. Until then, I keep the budget focused on the main Google SERP.
This article is part of the ongoing Search Engine Land series, Everything you need to know about Google Ads in less than 3 minutes. In each edition, Jyll highlights a different Google Ads feature and explains what advertisers need to know to get better results in a quick 3-minute read.
I no longer think of branded search protection as simply bidding on my own brand name. Competitors can position themselves against my brand through landing pages, ad copy, modifier keywords, and Google Ads automation, often in ways that look completely legitimate.
The real pressure often goes beyond keyword bids. Comparison pages that pass review, dynamic keyword insertion that pulls brand names into headlines, and policy gaps that allow competitors to appear beside my brand can quietly weaken performance without clearly breaking Google’s rules.
By the time I notice the pattern, the damage may already be visible in branded CPCs, impression share, or conversion rate. That is why I pay close attention to how these tactics work, how to spot them early, and how to respond without overreacting.
1. Dynamic keyword insertion
Dynamic keyword insertion, or DKI, is designed to make ads feel more relevant by automatically inserting a user’s search query into the headline. In competitive brand auctions, I see it as a tactic that can create a meaningful loophole.
If a competitor bids on my branded terms and uses DKI, Google can dynamically place my brand name in the ad headline in real time, even if the competitor never typed my trademark into the ad copy.
That distinction matters. The competitor is not explicitly writing my trademark into the ad. Google is inserting the searcher’s query. To the user, the ad may look like it directly references my brand. Inside Google’s system, it is treated as standard query matching.
The result is frustrating: an ad can appear to reference my brand, capture high-intent traffic, and send that user to a competing offer without obviously violating policy.
I have seen this happen from both sides. Sometimes competitors use it intentionally. Sometimes brands trigger it in their own accounts without realizing what is happening. In one case, a competitor’s name started appearing in a brand’s ad headlines because of DKI. No one had written that name into the ad; Google inserted it based on the query.
The bigger challenge is that I cannot reliably detect this from inside Google Ads alone. I have to audit the search results page directly. Otherwise, I may only notice the problem after branded CPCs rise or conversion rates start to slip.
Comparison landing pages sit in a gray area. Google does not evaluate landing page content the same way it reviews ad copy. If a competitor creates a page such as “[Your Company] alternatives” or “[Competitor vs. Your Company]” and bids on my branded terms, the ad can still run as long as the ad itself stays neutral.
The ad does not have to mention my brand at all. It can use broad language like “Find the right solution,” “Compare top tools,” or “See your options.” The competitive positioning happens after the click.
Once the user lands on the page, the comparison does the work. The page may include feature charts, pricing callouts, benefit comparisons, and carefully framed language such as “Why teams choose us over [Your Company].” The page may not be misleading or technically noncompliant, but the intent is obvious.
Google’s review process tends to focus on the ad rather than the full post-click experience. As long as the ad copy does not make explicit competitive claims, the system may treat it as compliant, even when the landing page is built entirely around positioning against my brand.
This works because landing page relevance can reinforce auction strength. A page built around my brand and the keywords in the ad group may align closely with the searcher’s intent. Even if the ad copy stays generic, the post-click experience can help the ad compete because it matches what the searcher is trying to evaluate.
When I respond, I do not focus only on one advertiser. If competitors are using comparison-driven experiences to intercept branded demand, I look at the broader search ecosystem around my brand.
I strengthen my presence across the full search results page, not just my own ads.
I invest in publishers, review platforms, directories, analysts, and affiliates that influence comparison and alternative searches.
I work to build a search results page where credible third-party sources reinforce my positioning when prospects search for alternatives, comparisons, reviews, or competitor evaluations.
The brands that win these moments do not rely only on their own landing pages. They shape the narrative across the entire search results page.
Brand keyword bidding is not new, but I see competitors using it in more strategic ways. Instead of bidding only on my exact brand name, they target brand-and-modifier combinations that give them more flexibility.
For example, if my brand were “Acme Project Manager,” a competitor might bid on searches like “Acme Project Manager alternative,” “Acme vs. competitors,” or “Acme pricing review.” Their ad copy can avoid mentioning Acme by name while still using the search context to position itself as the alternative.
Google allows this because the ad itself does not explicitly mention my brand. The searcher does. Modifier keywords provide enough context for the ad to compete without directly referencing a trademark in the copy.
When competitors bid on terms like “[Your Brand] alternative” or “[Your Brand] vs.,” they are targeting lower-funnel research queries. These searchers may not convert at the same rate as people searching only for my brand, but they can still change the auction dynamics.
That pressure can increase branded CPCs, force me to spend more to maintain visibility, and raise the cost of my core brand terms, even if competitors convert relatively few of those modifier searches.
I treat brand modifier queries as a separate audience. I segment them by intent, including pricing, reviews, alternatives, competitors, and comparisons, and I monitor Auction Insights for each group. Exact brand searches and comparison-driven searches need different strategies.
I also build dedicated landing pages and messaging for each modifier intent. That helps me control high-intent research moments without overpaying for every branded variation.
Manual SERP checks are useful, but they do not scale. If I have meaningful branded spend or active competitors targeting my terms, I use automated brand monitoring tools to identify activity across devices, geographies, and browsers that manual checks can miss.
This is especially important when competitors use geotargeting, dayparting, or other tactics designed to limit visibility. A competitor may not appear every time I check manually, but that does not mean the activity is not happening.
I also use a clear escalation framework. If a competitor uses my trademarked term directly in ad copy, I start with Google’s trademark complaint process. If the behavior continues after enforcement action, I document the pattern and involve legal counsel.
Most other scenarios, including modifier bidding, comparison pages, and competitive positioning, are usually better handled through PPC strategy than legal action.
Before I decide how aggressively to respond, I measure the economics. I estimate the monthly cost of competitor activity by calculating the increase in branded CPCs and the additional spend required to maintain visibility.
Then I compare that number with the cost of my response, whether that means higher bids, new landing pages, expanded monitoring, or more investment in third-party visibility. My goal is to keep the cost of defending the brand lower than the value I am protecting.
Build a proportionate response
Competitors use modifier keywords, comparison landing pages, dynamic keyword insertion, and other policy-compliant tactics to influence buyers during critical research moments. Often, they can do this while staying within Google’s policies.
The strongest defense I can build combines continuous monitoring, thoughtful audience segmentation, proportionate responses, and disciplined budget decisions.
Competitive PPC success comes from understanding the auction, shaping the narrative across search results, and investing where my defensive efforts deliver the greatest return.
Advertisers are projected to lose $172 billion a year to ad fraud by 2028. I have seen how quickly that problem can move from an abstract industry statistic to a very real performance issue inside a Google Ads account.
The risk is especially high in competitive industries where CPCs are expensive and every wasted click hurts. One client I worked with was in exactly that situation, and invalid click activity was dragging campaign performance below any profitable level.
After testing the usual defenses, I adjusted Google Ads audience targeting in a way that reduced invalid-click activity by 50% and brought the campaigns back to profitable performance.
Case study: How I cut invalid clicks by 50%
The client sold book editing and ghostwriting services. The search terms triggering the ads were relevant and high intent, but the traffic was not converting anywhere close to the level needed for profitability.
The warning signs appeared quickly. Google was reporting a 60% to 80% invalid click rate. Microsoft Clarity recordings showed bot-like behavior from Google Ads traffic. Many search terms had click-through rates above 80%, and some were even above 100%. GA4 and other analytics tools also showed far fewer sessions than the number of clicks reported in Google Ads.
I tested third-party click fraud tools first, but they did not produce any measurable improvement in performance.
Next, I filed an investigation with Google. Google agreed that suspicious activity existed, but said it had already caught all of it and had not charged the account for those clicks.
I was still confident that Google was not filtering out all of the invalid activity, so I decided to use the targeting controls inside the account more aggressively.
I added 540 Google-defined audiences to the Google Search campaigns and set them to Targeting.
The result was immediate. The invalid click rate dropped by 50%, and the conversion rate rose back to profitable levels.
Here is why I tested this approach, why I believe it worked, and what advertisers should understand before trying it in their own accounts.
What click fraud and invalid clicks actually are
Google defines invalid clicks as clicks on ads that do not come from genuine user interest. That includes intentionally fraudulent activity, accidental clicks, and duplicate clicks.
In practice, this can include actual fraud, such as competitors clicking ads, as well as less malicious behavior like accidental double-taps.
Google does not charge advertisers for clicks it determines are invalid. If Google initially charges for a click and later classifies it as invalid, it credits the advertiser back for that activity.
Why the usual defenses can fall short
Google catches a lot of invalid click activity, but this account showed me that the system is not perfect.
That gap is why so many third-party click fraud tools exist. Most of them try to identify suspicious IP addresses and block them before they can keep costing advertisers money.
The challenge is that fraudsters understand how these tools work. They can cycle through IP addresses with VPNs and avoid being stopped by a system that only blocks previously identified addresses.
If a tool blocks an IP address after suspicious activity occurs, that may help only if the same IP address is used again. When the source keeps changing, old IP exclusions lose much of their value.
There is also a platform limit to consider: Google allows a maximum of 500 IP address exclusions per campaign.
The tactic: Add audiences set to Targeting
I started thinking about what might separate fraudulent traffic from legitimate traffic. Google’s predefined audiences stood out because Google builds hundreds of audience segments from demographics, search behavior, and browsing behavior.
For example, someone researching private jet companies and Rolex watches might be classified by Google as a luxury shopper and added to that audience.
My hypothesis was simple: fraudsters who constantly rotate IP addresses may not also be building normal-looking online behavior profiles that fit neatly into Google’s predefined audiences.
So I added most of the available audiences to the Search campaigns. I was not trying to target only audiences that matched the ideal customer. I was using the audiences as a filter for users who carried enough Google audience signals to look more like real people.
The key detail is that I chose Targeting, not Observation.
When I use Targeting, Google limits ads to people who trigger the keywords and also belong to the selected audiences.
When I use Observation, Google simply reports how people in those audiences engage with the ads compared with everyone else. The ads can still show to anyone who triggers the keywords.
I would only test this tactic in accounts with unusually high invalid click rates. It can create real downside, including the risk of blocking legitimate searchers who do not fit inside Google’s predefined audience segments.
How to test this in your own account
In a Search campaign, go to Audiences > Edit audience segments > Targeting > Browse. Then select the audiences you want to add and click Save.
Common questions about fighting click fraud
Will Google refund clicks it identifies as invalid?
If Google identifies a click as invalid when it happens, I am not charged for that click. If Google identifies the click as invalid later, the account receives a credit toward future advertising.
How do I see how many invalid clicks I am getting?
The Invalid activity credit report in Report Editor inside the Google Ads UI provides the most detailed reporting.
I look at two key metrics there: Invalid clicks, which are clicks I was not charged for, and Credited clicks, which are clicks I was originally charged for but later credited back.
I can also add the Invalid clicks and Invalid click rate columns at the campaign level, though not at the ad group or keyword level.
What is a normal invalid click rate?
A February study found an 11.4% invalid click rate across 43,700 accounts.
Industry makes a major difference. While the average invalid click rate for StubGroup clients is very close to that study’s finding, I have seen advertisers in competitive industries with invalid click rates above 40%.
Should I file an investigation with Google?
If I have reason to believe Google is charging for invalid clicks, I consider filing an investigation here.
Why this approach worked best
Using Google’s predefined audiences as a filter cut this account’s reported invalid click rate in half. More importantly, it blocked activity that Google had said it was already catching, which turned failing campaigns into profitable ones.
I’m watching a new Google Search ad test that could change how people understand sponsored results. Google appears to be experimenting with AI-generated summaries beneath paid search ads, giving its own AI more influence over how advertiser messaging is framed.
What’s happening. Some advertisers are seeing AI-generated summaries appear directly below Google Ads descriptions in Search results. These summaries include a warning from Google that says: “Google AI responses are generated independently and can make mistakes, so double-check responses.”
I first saw this test surface through digital marketer Darcy Burk, who shared a screenshot of the experience on X. The placement is notable because the AI-generated text appears close enough to the ad that users may treat it as part of the paid result, even though Google says the response is generated independently.
Why I care. If Google expands this more broadly, these summaries could shape how users interpret ads by emphasizing the details Google considers most relevant, not necessarily the exact message the advertiser intended to highlight. That raises real questions about accuracy, brand control, and whether click-through rates could be helped or hurt by AI-written context.
Between the lines. Google has already tested AI-generated summaries for organic search listings, so seeing similar functionality move into paid ads feels like another step in bringing generative AI deeper into the Search experience. What I still do not know is how these summaries are created, what sources they rely on, or whether advertisers will get any say in the copy.
What I’m watching. Google has not publicly announced this feature or responded to requests for comment, so it is unclear whether this is a small experiment or the beginning of a wider rollout. Until Google explains the mechanics, advertisers are left guessing how much control they may have over AI-generated text attached to their ads.
The bottom line. Google is testing AI-generated summaries inside Search ads, and I see that as a sign that generative AI could soon play a larger role in paid search presentation, even when advertisers are not writing that extra copy themselves.
First spotted. Darcy Burk, understandably, was not pleased with this update.
When I audit Google Ads accounts, it is easy to focus first on the obvious issues: keywords, bids, ad copy, and Quality Scores. But one of the biggest performance barriers I see is not buried inside a single campaign setting. It is the way the account was structured from the start.
Campaign structure shapes how Google’s machine learning reads the account, how budget moves across goals, and whether useful data is collected in one place or scattered across too many campaigns. When the structure is wrong, I am not just leaving performance on the table. I am making the algorithms work harder with weaker signals.
That is why I look closely at structure across standard Search campaigns, Performance Max, and Smart Bidding. The account architecture often determines whether optimization efforts can actually work.
How campaign structure shapes Google’s learning
I used to see advertisers treat campaign structure mainly as a cleanup exercise: tidy ad groups, logical naming, and campaigns separated by product line or geography. To Google’s systems, though, structure means something much more important.
Every campaign is a data container. The way I segment campaigns determines which signals Google can pool together for bidding and targeting decisions. When the structure is scattered, the learning is scattered too, and optimization becomes slower and less accurate.
Smart Bidding and automation usually perform better when more data is concentrated in fewer campaigns. Google’s algorithm needs meaningful volume, often around 30 to 50 conversions per campaign per month, to move beyond the learning phase and make reliable predictions. If I spread conversions across too many campaigns, each campaign can end up starved of the data it needs.
A common example is an ecommerce account with 12 separate Search campaigns, one for each product category. Each campaign averages 8 to 12 conversions per month. Smart Bidding is active, but no campaign consistently exits the learning phase.
In that situation, the fix is usually consolidation.
Over-segmentation breaks Smart Bidding
Smart Bidding strategies such as Target CPA, Target ROAS, Maximize Conversions, and Maximize Conversion Value depend on real-time signals like device, location, time of day, audience, search query, and more. Google weighs those signals together to predict which auctions are worth entering and how much to bid.
When I see campaigns that are over-segmented, I usually see the same problems appear. First, conversion volume is too low, so each campaign operates below the level Google needs for confident bidding decisions. That often leads to unstable CPAs and CPCs.
Second, learning phases last too long. Every budget change, bid strategy switch, or structural edit can trigger a new learning period. Over-segmented accounts can feel permanently stuck there, never reaching their full potential.
Third, signal consolidation is missed. Bidding signals do not freely transfer across campaigns. A branded campaign cannot teach the algorithm inside a non-branded campaign, even when both campaigns share the same conversion goal.
Finally, bid cannibalization becomes a real risk. When multiple campaigns compete in the same or overlapping auctions, I can end up driving up my own costs and creating avoidable inefficiency.
The result is an account that looks optimized on the surface, with Smart Bidding enabled, audiences attached, and conversion tracking active, but still underperforms because the structure underneath is working against every optimization layered on top of it.
The impact of Performance Max
Performance Max adds another layer to campaign structure. Unlike Search campaigns, PMax runs across Google inventory, including Search, Display, YouTube, Gmail, Discover, and Maps. It uses asset groups and audience signals to guide automation, which makes setup more important and more complicated.
Asset group segmentation
I think of asset groups inside PMax as mini-campaigns. Google uses them to understand context, match creative to searches, and optimize delivery. When asset groups are too broad, mixing unrelated products, audiences, or themes, the algorithm has a harder time matching the right creative to the right situation.
I prefer to segment asset groups by product category or service line, audience intent level such as prospecting versus retargeting, and creative theme or offer type.
This gives Google clearer signals about what each group is meant to accomplish, which can improve both creative matching and bidding efficiency.
PMax and Search campaign overlap
One of the most damaging mistakes I see in accounts running both Search and Performance Max is failing to set clear boundaries between them. PMax can serve across all placements, including branded and non-branded searches, so it can compete with Search campaigns if I do not define where each campaign type should operate.
Without proper segmentation, PMax can cannibalize high-intent branded search traffic and inflate costs on terms I might have won more cheaply through Search. Search campaigns can lose impression share they otherwise would have captured, and attribution becomes harder to interpret because it is less clear which campaign is truly driving performance.
My preferred solution is to use campaign-level negative keywords, brand exclusions, and clear audience segmentation. PMax should complement Search campaigns, not compete with them.
Budget allocation and automation conflict
PMax runs as a single campaign with a single budget, but because it delivers across multiple channels, budget allocation happens dynamically. When PMax and Search campaigns are not organized around clear goals, Google may spend on the easiest placements rather than the best ones.
Structural choices, such as whether I run one PMax campaign or split campaigns by product line, directly affect how budget is distributed and how well automation can support business goals.
Match type strategy and its structural implications
Match types are often treated as a keyword-level decision, but I see them as a structural decision too. Running broad match, phrase match, and exact match across separate campaigns, or even separate ad groups, without a coherent strategy can create overlap and wasted budget.
Google Ads looks very different than it did a few years ago. Broad match now casts a much wider net, and Google increasingly pushes advertisers to pair it with Smart Bidding. That combination can work, but only when the campaign structure gives the algorithm enough support.
Broad match with Smart Bidding works best when there is enough conversion data, a clear goal, and enough traffic for Google to learn from. In a fragmented account, broad match can make the problem worse. It brings in more searches, but the algorithm does not have enough clean data to make good use of them.
The safer approach is to keep match types within fewer campaigns, use negative keywords to prevent campaigns from bidding against each other, and review search term reports regularly so I can tighten boundaries where needed.
Keyword and ad group architecture: When granularity becomes an obstacle
Single Keyword Ad Groups, or SKAGs, are mostly a thing of the past, but many accounts still carry their legacy: hundreds of tiny ad groups with one or two keywords and nearly identical ads. That level of detail made sense when advertisers managed bids manually. Today, it often works against Smart Bidding.
Too many ad groups create the same data problem at a smaller scale. Responsive search ads perform better when they have more to learn from, including which headlines get clicked, which asset combinations work, and how auctions behave. That learning happens faster when ad groups are consolidated around broader themes.
I usually aim for three to five tightly themed ad groups per campaign instead of dozens of micro-segmented groups. Each ad group should include enough keyword variation to generate useful data while staying focused enough to preserve message relevance.
The goal is maximum signal quality. If structural granularity does not improve data consolidation, it is usually unnecessary complexity.
Conversion goals and campaign alignment
Structure also determines which conversion actions each campaign optimizes toward, and I consider goal misalignment one of the quietest performance killers in Google Ads.
If multiple campaigns share a poorly defined conversion goal, or if different campaigns optimize toward different actions without a clear hierarchy, Smart Bidding receives conflicting instructions. It may optimize toward micro-conversions like page views or add-to-carts when the real objective is form fills or phone calls. It may also treat goals as equal when one is clearly more valuable than another.
A structurally sound account connects campaign goals to business objectives, not just platform metrics. It separates primary conversions from secondary tracking actions, and it uses accurate conversion values when campaigns rely on value-based bidding.
Performance Max is especially sensitive to conversion goal quality. Because PMax controls its own bidding and placement decisions, it will optimize aggressively toward whatever I tell it matters most. If that signal is wrong, the campaign may optimize efficiently toward the wrong outcome.
Signs your structure is hurting performance
Structural problems rarely announce themselves clearly. I usually see them show up as issues that are easy to blame on ads, bids, or audiences.
Persistent learning phase warnings are one sign. Campaigns may be frequently flagged as limited by learning even when budgets are consistent. Unstable CPAs or ROAS are another warning, especially when performance swings do not settle over time.
I also watch for high impression share lost to budget when total budgets seem adequate, disproportionate spend flowing into a small number of campaigns, limited visibility into PMax search terms, and declining Quality Scores as the account grows across too many ad groups.
When two or more of these symptoms appear at the same time, I treat structure as a likely root cause. Bid adjustments and creative testing will not fix the problem until the foundation is corrected.
A framework for structural audits and consolidation
Restructuring an active account carries risk. Any major structural change can trigger learning phases and temporary performance disruption, so I consolidate carefully and use data as the guide.
Step 1: Assess conversion volume by campaign
I start by identifying which campaigns consistently generate 30 or more conversions per month and which fall below that threshold. Campaigns with low volume are usually candidates for consolidation.
Step 2: Map audience and intent overlap
Next, I look for campaigns that compete against each other for similar searches or audiences. Overlap creates waste, and structural waste is one of the most expensive forms of inefficiency.
Step 3: Evaluate PMax and Search boundaries
Then I audit how PMax and Search interact. I want to know whether brand terms are being captured by the right campaign type and whether negative keywords are in place to prevent cannibalization.
Step 4: Simplify ad group architecture
From there, I move away from SKAG-style granularity and toward theme-based groupings. Ad groups that serve overlapping intent should usually be consolidated into broader, cleaner themes.
Step 5: Align conversion goals
Finally, I audit conversion actions across all campaigns. Primary goals should match real business outcomes, and value-based bidding inputs should reflect actual revenue data whenever possible.
Important: I would not restructure everything at once. I would start with the highest-spend campaigns, monitor performance through the learning phase, and validate results before moving to the next round of consolidation.
Campaign structure comes first
I see campaign structure as the foundation of Google Ads performance. When it is right, Smart Bidding, Performance Max, and audience targeting can work with stronger signals, clearer goals, and more efficient budget allocation.
When it is wrong, no optimization layered above it can fully solve the problem. Bids cannot fix fragmented data. Creative cannot correct misaligned conversion goals. Performance Max cannot prioritize efficiently when its boundaries with Search are unclear.
The biggest performance improvements in Google Ads often do not come from a new bid strategy or a sharper headline. They come from stepping back, auditing the account architecture, and rebuilding the foundation everything else depends on.
I’m seeing Google add a new Channel Diagnostics feature to Performance Max, and it gives advertisers a more centralized way to understand asset issues that may be holding back campaign delivery across Google’s channels.
The new Channel Diagnostics section is available inside Insights & Reports > Channel Performance for Performance Max campaigns. For me, the value is that advertisers no longer have to dig as deeply to figure out whether missing or disapproved assets are limiting where a campaign can serve.
With this update, I can review diagnostics across all Performance Max channels or drill into a specific channel when I need more detail. I can also identify missing or disapproved assets that affect campaign eligibility and see which asset types, such as headlines, descriptions, or images, need attention.
This matters because Performance Max has often been criticized for limited visibility into campaign issues. I see Channel Diagnostics as a useful step toward making those issues easier to spot, especially when missing creative assets may prevent campaigns from serving across Search, Display, YouTube, Discover, Gmail, and Maps.
By surfacing channel-specific asset gaps in one place, Google is giving advertisers more actionable insight without forcing them to manually audit every asset group. That can make troubleshooting faster and help teams prioritize the fixes most likely to restore eligibility or improve delivery.
The bottom line is that Channel Diagnostics gives Performance Max advertisers a quicker way to identify and fix missing assets. I see it as a practical improvement for keeping campaigns eligible across Google’s full range of inventory.
This update was spotted by a Google Ads Specialist who shared it on LinkedIn.
I’m seeing Google expand its measurement capabilities for YouTube brand campaigns, and the goal is clear: advertisers are getting better visibility into how video ads influence engagement, brand interest, and downstream business outcomes.
What’s new: I’m paying attention to two updates in particular: Shorts Ad Actions for Video View Campaigns and Attributed Branded Searches.
Shorts Ad Actions for Video View Campaigns: When advertisers run Video View Campaigns that are opted into YouTube Shorts, they will now automatically benefit from Shorts Ad Actions in budget optimization. Google is also adding new reporting columns so advertisers can measure these interactions more clearly.
Attributed Branded Searches: Now available globally in Google Ads, this reporting metric measures branded Google searches that happen after someone sees or views a YouTube ad. I see this as a useful way to understand how awareness campaigns may influence purchase intent before a direct conversion takes place.
Why I care: It has always been difficult to connect upper-funnel YouTube campaigns with measurable business outcomes. These updates give marketers stronger signals that link brand advertising to engagement and search intent, which can make it easier to justify brand investment and improve campaign decisions.
By the numbers: According to Google, YouTube Shorts ads that generated more than 10 seconds of watch time and a like delivered 15% higher brand consideration and 20% higher brand favourability.
Google also says every additional branded search generated is associated with an average $31 increase in sales, which gives advertisers another way to connect brand activity with business impact.
Between the lines: I see Google continuing to blur the distinction between brand and performance marketing by introducing metrics that connect awareness campaigns with downstream actions. Attributed Branded Searches, especially, gives advertisers another way to show that YouTube campaigns can influence high-intent behaviour before a conversion happens.
The bottom line:Google’s latest measurement updates help advertisers better prove the value of YouTube brand campaigns by linking video engagement and branded search activity to business outcomes. For me, the bigger story is that upper-funnel advertising is becoming easier to measure in ways that matter to performance-focused teams.
