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Tag: Google Ads

PPC Budget Mastery for 2026: Smart Adjustments and Data Optimization

In 2026, PPC budgeting goes beyond simply setting spending levels. It’s about understanding when to adjust budgets, scaling campaigns effectively, and how data informs Google’s automation in these decisions.

Over the years, Google’s automation has been driven by the signals supplied to it. In 2026, these signals are processed faster and more precisely, making clean signal architecture more crucial than ever.

While the fundamentals of budget management remain constant, the speed at which a poorly structured account can drain your budget has increased significantly.

Two Budget Mechanics You Must Grasp Now

Before tweaking targets, audiences, or bid strategies, it’s essential to comprehend how these two budget controls operate.

The Ad Scheduling Pacing Change

Google now paces campaigns with ad scheduling towards the full 30.4x monthly billing cap, regardless of how many days your ads run. Previously, a $100 daily budget targeted around $2,200 across 22 weekdays. Now, it targets $3,040 in the same period, and the billing ceiling remains unchanged.

If your campaigns utilize ad scheduling, you need to recalibrate your daily budget based on your total monthly spend rather than active days, setting it by dividing your monthly target by 30.4. For example, a $2,200 monthly target becomes a $72 per day budget if calculated this way. However, 24/7 campaigns remain unaffected.

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Campaign Total Budgets

Available for Demand Gen, Search, Standard Shopping, Performance Max, and YouTube campaigns, campaign total budgets let me set a fixed spending ceiling over a defined period instead of managing a daily limit. This window is from three to 90 days for some campaigns, while others can extend up to a year.

While there is no daily spend cap, allowing flexibility, it’s crucial to monitor these closely, especially when running alongside ongoing campaigns. Additionally, the budget type cannot be altered post-campaign creation, making committed decisions at setup vital.

What Actually Governs Google Ads Budget Spending

Efficiency Targets Usually Constrain Spend Before Budgets

In Smart Bidding strategies, efficiency targets often restrict spending before budget caps do. With a set tCPA of $50, if leads cost $80, the system reduces bids to avoid surpassing your target. It appears as if there’s a budget problem, but it’s actually a target problem.

I must initially set targets closer to the market conversion rates and then fine-tune them to align with my true goals. When close, the 10%-20% margin aids in navigating those final conversion opportunities effectively.

Performance Max Decides Where Your Budget Goes

Performance Max automatically allocates budget across various channels like Search, Shopping, and YouTube, with Google determining the split, not me. Excluding my brand can prevent paying for redundant conversions from Search campaigns.

Checking my negative keyword lists ensures clarity in branding and budget allocation. This helps avoid misallocation and focuses resources effectively.

AI Max Expands Ad Appearances

AI Max, available since April, expands query matching beyond my keyword list, generates ad copy from existing assets, and dynamically targets landing pages. Monitoring the initial spend distribution closely helps maintain alignment with intended strategies.

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The Signal Problem Impacting Budget Allocation

An insurance broker using Smart Bidding faced a disconnect: a 416% rise in conversion volume didn’t reflect in revenue due to form starts mistaken for completions. The system optimized for interactions, but the alignment with Cyrillic-language spam was costly without benefiting the pipeline.

This reflects a broader issue in lead generation: equal weight is assigned to all form fills, leaving Smart Bidding unable to distinguish high-value leads from irrelevant submissions.

Primary conversions must be meaningful actions that properly guide Smart Bidding. Secondary engagements belong in reports to avoid skewing bidding data.

For accounts outside the current beta, extending conversion windows to 90 days and assessing performance over these periods can help counteract issues arising from longer sales cycles.

Using First-Party Data for Budget Guidance

Customer Match, with a 540-day max membership duration, remains crucial in guiding automation toward valuable traffic. For effective budget allocation, I focus on exclusion before expansion, targeting acquisition budgets toward new prospects.

Retention strategies should be run separately to maintain consistency in conversion goals. It’s vital that exclusions, available from the start, streamline acquisition efforts effectively.

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Strategic Scaling in 2026

For ongoing daily budget campaigns, weekly increases of 10-20% are still relevant. For scheduled campaigns, I focus on monthly targets divided by 30.4 instead of daily adjustments.

Using Smart Bidding Exploration in open beta for Performance Max can increase unique conversions by exploring new queries. I evaluate results over 60-day windows to make informed decisions.

Demand-led pacing, complementing daily management, tracks predicted high demand periods to optimize spend within budgetary limits. For B2B accounts, longer evaluation periods safeguard against undervaluing long cycle campaigns.

Inspired by this post on Search Engine Land.

June 18, 2026
Unlocking Google’s Auto-Classification for Conversion Lists
Starting in August 2026, Google will begin to automatically categorize customer types in conversion-based lists, removing some of the control we advertisers once had. I must now provide Google’s systems with clearer signals on where audiences are in their customer journey.

As someone deeply involved in advertising, I know the importance of precise audience targeting. With these changes, I’m urged to review and update my classifications in the Google Audience Manager before they kick in.

What’s Changing? From August 2026, Google Ads will automatically classify customer lists into categories like:
- Existing customers
- New customers
- Other customer segments
Why Google’s Making This Shift. It appears that Google aims to enhance audience consistency across its tools for customer acquisition and retention. This standardization allows for better optimization decisions in Google’s automated bidding and targeting systems by clearly defining prospecting from retention audiences.

Why This Matters to Us. As an advertiser utilizing customer acquisition strategies, the precise classification of these lists is crucial. Any misclassification could impact Google’s optimization of users throughout their lifecycle, affecting campaign performance.

What We Should Do. It’s vital for us to audit our Customer Match lists—based on conversion data—before August. Consider these questions:
- Are my customer lists categorized correctly?
- Do they represent existing customers versus acquisition targets?
- Will Google’s automatic classification align with my internal definitions?
Reviewing these settings now could prevent unexpected changes when Google enforces these classifications.

The Bottom Line. Google is taking an active role in managing audiences, further streamlining the signals powering their automated advertising systems by assigning lifecycle labels to conversion-based lists.

First Spotted. This update was noticed by Google Ads expert Bia Camargo, who shared the alert on LinkedIn.

Inspired by this post on Search Engine Land.
June 17, 2026
How Google’s New Ad Policy Impacts Advertiser Reach

I’ve recently discovered that Google is expanding its Limited ad serving policy across its Search platform. This change gives Google more control to restrict ad impressions from advertisers deemed unqualified or who might create confusion for users.

The implication of this update is significant. For newcomers, brands receiving negative feedback, or those not clearly presenting their identity in ads, the frequency of ad appearances could be affected.

What’s changing? As of this month, Google is rolling out an expanded policy affecting more search scenarios, which it plans to continue implementing through 2028.

This updated policy allows Google to limit ads on searches they believe might lead to poor user experiences.

How Google decides: User feedback is becoming crucial. Advertisers with frequent complaints about misleading content or practices could face limits on where their ads appear.

Additionally, if an ad makes it challenging to recognize who the advertiser is, Google might also impose restrictions.

Why we care: It’s not just about policy compliance anymore. Google is placing more emphasis on advertiser trust signals and branding clarity. Advertisers who don’t make their brand identity clear or have negative feedback histories might see reduced reach.

This shift underscores the importance of brand transparency in Search ads. Advertisers should reevaluate their ad copy and branding to ensure it’s evident who they are and their ad’s purpose.

What advertisers should do: To align with this update, advertisers are encouraged to enhance brand visibility in ads and landing pages, avoid overly generic messages, and clarify any brand affiliations.

Including a domain headline in the first position of responsive search ads can also help in making the advertiser’s identity more apparent.

The bottom line: Google’s updated policy prioritizes advertiser trustworthiness and clarity, potentially limiting visibility for those creating confusion with their identity or practices.

First spotted: Anthony Higman, Founder of Adsquire, first noticed this update. He expressed his concerns on LinkedIn.

Inspired by this post on Search Engine Land.

June 12, 2026
Is Your Brand Campaign Truly Ready for AI’s Prime Time?

Not too long ago, I remember broad match being hailed as the future of paid search. Today, AI Max has taken on that mantle.

Over recent months, I’ve received plenty of suggestions to activate AI Max on brand campaigns, even when these campaigns are performing just as I want them to.

The reality is, many accounts still aren’t equipped with the essentials AI Max requires for optimum function. Conversion tracking issues, the lack of offline conversion imports, and budget-constrained generic campaigns are common hurdles.

AI Max thrives on robust conversion signals, adequate volume, and enough variation for effective learning. I often find that brand campaigns provide most of these signals.

However, applying AI Max to brand campaigns means layering additional automation over our most efficient and predictable traffic source.

The promise and limitations of AI Max

AI Max can broaden search targeting beyond your key phrases by using keywords, landing pages, and site content as signals instead of specific targeting criteria.

Much like dynamic search ads (DSA), AI Max can align with queries you didn’t explicitly target, and it ventures even further by transcending the intent limits set by your keyword arsenal.

Google portrays AI Max as the future of Search automation, preparing to merge DSA, automatically created assets, and broad match settings into AI Max this September.

With controls like brand exclusions, URL exclusions, text guidelines, and location targeting, AI Max might tap into growth opportunities in accounts rich with strong conversion signals and enough search volume.

Yet, many accounts haven’t reached that point.

With Google’s AI Surface eligibility expanding, it’s tempting to dive headfirst into AI Max. But it’s essential to focus on account fundamentals—measurement accuracy, conversion integrity, and solid account structures—before relying solely on AI Max.

Why AI surface eligibility isn’t reason enough to rush into AI Max

The growing interest in AI Max is fueled by Google’s push toward AI-powered search experiences. AI Overviews now engage approximately 2.5 billion users monthly, presenting ads in 25.6% of AI Overview results, according to Semrush data.

While maintaining visibility in these surprising new fields is advisable, rushing to apply AI Max without assessing your campaign structure and conversion strategies can be detrimental.

Typically, Google Ads representatives pitch AI Max for brand campaigns to ensure their eligibility in AI Mode and associated AI Overviews. However, this isn’t always the truth.

Ginny Marvin, a Google Ads liaison, confirmed that three campaign types are eligible for AI Overviews: broad match with Smart Bidding, Performance Max (PMax), and AI Max for Search. Meanwhile, exact match keywords never qualify for AI Overviews.

Thus, PMax and AI Max generally serve the same purpose concerning AI surface eligibility. Running PMax brand campaigns already gives you the necessary coverage, without the need for adding another layer of automation.

Before adding AI Max into your mix, examine whether it’s genuinely necessary over addressing your account’s foundational needs.

Test data doesn’t fully endorse Google’s AI Max assertions

Google claims that enabling AI Max could increase conversions by 14%, and those employing exact and phrase matches might experience a 27% increase. Nevertheless, independent tests have yielded a wide array of results.

The evidence for AI Max remains mixed

In tests covering 600 accounts, Smarter Ecommerce observed AI Max produced 35% lower ROAS than traditional match types. This outcome aligns with intentional budget minimization by advertisers.

Through a four-month examination, Xavier Mantica discovered AI Max resulted in the priciest conversions compared to phrase and exact match. While Mantica noted $100.37 per conversion with AI Max, phrase match was at $43.97, and exact match was at $52.69.

Moreover, 99% of impressions during Ezra Sackett’s 30,000 search term analysis returned zero conversions under AI Max.

Significantly, none of this data is brand-focused. AI Max may provide benefits in certain settings, but a successful, exact match defensive brand campaign may not be the right candidate for testing new automation.

If your brand is still the standout performer in your account, you may want to question why the rest of your campaigns haven’t met similar standards.

What to consider before testing AI Max on brand

Ask yourself these critical questions before branching AI Max into your brand campaigns:

1. Are the conversion signals trustworthy?

Does your setup cleanly distinguish between macro and micro conversions? Are offline imports running smoothly? Does the lead quality feedback enhance platform optimization?

If the underlying signals falter, AI Max will simply magnify those issues.

2. Have you already explored generic growth?

In the accounts I review, problems like budget constraints, misaligned landing pages, outdated queries, and suboptimal structure frequently hinder generic campaign growth.

Real growth is often found within these issues, rather than an already strong brand campaign.

3. Can the account provide AI with sufficient learning data?

Remember, AI Max is not some sorcery; it mirrors the quality of the signals it receives.

Relying heavily on brand conversions will only amplify these markers and obstruct other growth pathways.

4. Are brand + modifier searches already structured properly?

Search variations like “Brand + pricing” or “Brand + reviews” ought to be treated as separate strategic campaigns. AI Max should not substitute for robust account architecture.

5. Do you have a strategic reason to expand the brand campaign?

Consider testing strategically through experiments, rather than viewing AI Max as a straightforward switch to augment visibility.

AI Max only works as efficiently as the signals guiding it

AI Max might develop into a truly beneficial tool over time, much like PMax did. Automation effective at any level still requires strong foundational signals for success.

The existing issue remains with insufficient solid foundations supporting the automation. Improved conversions, precise measurement, sound account structures, and comprehensive feedback loops are vital to making automation wiser.

Above all, don’t conflate Google’s automation agenda with your campaign objectives.

Inspired by this post on Search Engine Land.

June 9, 2026
Evolving PPC Skills: Transforming from Keyword Manager to System Optimizer

I’ve noticed how AI-driven Google Ads has revolutionized the PPC landscape. My role has evolved from merely executing campaigns to designing signals and guiding the conversion system.

In the past, PPC was all about having control – managing keywords, match types, bids, crafting ad copy, and structuring campaigns to make the algorithm follow my lead.

Back then, proficiency in Excel and pivot tables distinguished the best ad managers. Agencies and PPC experts thrived on their execution skills. Greater control over variables meant better job execution, a strategy that worked well for PPC’s first decade.

However, Google Marketing Live (GML) 2026 heralded a significant shift for PPC. The focus moved from tactical control to system optimization, from managing keywords to signal design, and from setting up campaigns to aligning with machine strategy.

With AI-driven Google Ads, it’s evident that execution alone is no longer a competitive advantage. As Selin Song from Google Customer Solutions emphasized, execution has become a commodity.

Here’s what the new skill set involves.

I’ve learned to design inputs – the new keyword research. Knowing what inputs to provide the system helps it find the right audience on my behalf.

With AI Max for Search, I’m using a mix of broad match, keywordless targeting, text customization, and URL expansion. This strategy surfaces queries my keyword list wouldn’t catch, leading to an average of 7% more conversions or conversion value at a similar CPA/ROAS.

Feeding the system accurate conversion data is crucial. If conversion actions are irrelevant, the system solves the wrong problems, and that responsibility falls on me.

In terms of product and feed data, optimizing feeds with Conversational Attributes helps display products effectively in AI-generated responses. Ensuring audience signals are precise also shapes system operation, particularly with new prospects.

The days of relying solely on keyword lists are long gone; today’s system demands a strategic approach with the right inputs to automation.

Value signal architecture has replaced traditional bid management. My focus is now on providing robust signals like first-party data and accurate conversion values to Smart Bidding.

The advent of demand-led budget pacing means I set parameters rather than control pacing. Understanding product margins, inventory, lifetime value, and cash flow guides me in providing the right signals instead of merely setting bids.

Journey-aware bidding allows me to optimize the full conversion journey, not just the endpoint, requiring a well-instrumented conversion path connected back to the ad platform for effectiveness.

System prompting is today’s copywriting. AI Brief powered by Gemini helps me guide AI Max using brand-specific briefs to ensure it represents the brand accurately without over-constraining creativity.

I’ve learned to write briefs that effectively convey brand strategy, assisting AI in maintaining brand integrity in every campaign impression.

Budget architecture has taken precedence over daily budget adjustments. Campaign total budgets automate the process, and interpreting auction behavior in predictive systems has become my focus.

I rely on missed opportunity reporting to make informed decisions about budget constraints and optimize growth opportunities within the architecture I construct.

Measurement literacy has surpassed mere Quality Score management. Feeding the system quality signals helps it make informed decisions and optimizes bidding behavior through robust data integration.

It’s crucial now to ask business-relevant questions that the system can optimize toward meaningful outcomes. Communicating system behavior in business language is becoming a survival skill, alongside maintaining human oversight to ensure strategic alignment.

GML 2026 confirmed we’re already in this new phase. Thriving today means understanding the system’s needs and strategically providing those inputs to achieve business objectives.

Inspired by this post on Search Engine Land.

June 3, 2026
How Google Ads’ AI Updates Impact Advertisers in 2026

As I dig into Google’s latest updates to the Google Ads Terms of Service, I’m struck by the emphasis on AI-driven automation and what it means for us as advertisers. These changes raise some intriguing questions about control and oversight.

The big picture? These updates, effective from July 1st, apply solely to Google Ads accounts, leaving other Google products like Workspace untouched. Conveniently, we don’t need to take any immediate action.

What’s Changing? It seems Google aims to bolster the role of automation and AI in its advertising platform. Let’s unpack the key changes.

First, there’s updated language regarding how our inputs might be utilized across Google Ads features to enhance campaign performance. Additionally, there’s clarity on how Google’s systems may leverage information from conversational tools.

Importantly, provisions concerning the URLs and accounts we’ve authorized for Google’s automated campaign setups have been refreshed.

Why This Matters The broader implications here hint at Google assuming wider authority for using AI to craft and optimize ad elements for us. This doesn’t relieve us from our responsibility to review and approve campaigns, keeping us accountable.

For brands worrying about transparency and control, these revisions play a crucial role in compliance and performance accountability.

A Significant Shift There’s notable new language around automated campaign management. It suggests that unlike before, where opting in or out of automation features was straightforward, we’re now inherently authorizing Google to deploy automated processes for ads on our behalf.

Yet, we remain accountable for final campaign results, holding onto the reins of responsibility.

What Critics Say Some voices in the industry are skeptical. Anthony Higman, founder of AdSQUIRE, voices concerns that these updates dilute two core tenets of Google Ads: relevance and control. He particularly points to the nuances giving Google greater leeway in automated ad management while maintaining our accountability.

Higman feels this erodes our ability to opt into automation features, hinting at a shift in decision-making power towards AI systems.

Between the Lines We need to pay attention to our responsibilities. This includes ensuring we hold the necessary rights to any inputs shared with Google Ads and staying vigilant in overseeing auto-generated campaigns and assets.

Regional Updates Google isn’t stopping at universal terms. It’s also introducing region-specific changes in several markets involving arbitration agreements and legal compliance.

This includes adjustments reflecting recent legal practices and specifics on arbitration or fees that apply depending on location.

Advertisers from Brazil, for instance, face clarified language about Google BR’s authority in their transactions.

What’s Next Come July 1st, the updated Google Ads Terms of Service will be in place. While no immediate account actions are needed, reviewing these terms is advisable.

Inspired by this post on Search Engine Land.

June 2, 2026
Discover Google’s New Tool for Tracking Invalid Click Credits
Recently, I stumbled upon Google’s new documentation that sheds light on a handy tool called the Invalid Activity Credit Report. It’s designed to give us advertisers a clearer picture of refunds issued for those pesky invalid clicks and interactions.

The big picture. Although it’s not entirely clear if the report itself is brand new, it’s definitely showcasing some metrics we’ve seen before, like the invalid click rate. What’s exciting is that the documentation provides a detailed view of credits issued for invalid traffic in both Search and Performance Max campaigns.

How it works. Google mentions that they use automated systems to detect and filter out invalid traffic before it costs us anything. However, if any invalid activity slips through, these credits come to the rescue post-billing.

In such cases, Google might issue credits to cover the associated spend.

While I’ve seen these credits in billing and transaction histories before, this new report breaks down:
- Credited clicks
- Credited interactions
- Credited spend
- Campaign-level impact
- Adjusted performance metrics after credits are applied
Why we care. Having a transparent view of how much of our campaign budget is being refunded due to invalid activity helps me understand my true performance and costs much better. Plus, this new documentation is a gem for raising awareness about a tool many of us might not have known about.

Google’s goal with this report seems to be providing a better understanding of campaign performance after these adjustments.

They say that it helps us:
- See costs, clicks, and interactions post-credits.
- Reduce the hassle of manually reconciling billing credits with campaign performance.
- Gain insights into how Google’s invalid traffic protections affect each campaign.
How to access it. Finding this report is straightforward through the Report Editor in Google Ads.

Just go to the Template Gallery, and select “Invalid Activity Credit Report: Search & PMax” to generate a report with standard campaign metrics, alongside new columns for credited clicks, interactions, and amounts.

You can even add performance metrics to see how campaigns fare after credits adjustment.

What to watch. The report could soon become invaluable for those of us running large budgets, especially if we’re meticulously examining traffic quality and discrepancies in reported performance versus billing.

As AI-driven campaign automation grows, insights into invalid traffic and refunded spend will likely become critical in our campaign strategies.

Inspired by this post on Search Engine Land.
June 2, 2026
Google Unveils Enhanced Data Manager API for Seamless Ad Integration

I’ve recently discovered that Google is taking major strides in helping advertisers streamline their measurement workflows and enhance audience match rates throughout its advertising ecosystem. Exciting times lie ahead for us marketers!

By incorporating new capabilities into the Data Manager API, Google enables us to send offline conversion data seamlessly across multiple Google Marketing Platform destinations. This can significantly boost Customer Match performance through IP-based matching.

What’s happening. The enhanced Data Manager API now accepts offline conversion event uploads to platforms like Campaign Manager 360, Search Ads 360, and Display & Video 360. This represents an expanded role for the API as a central data ingestion layer in Google’s advertising universe.

We can now rely on a single schema to distribute conversion data across several Google products, which is a game-changer compared to our previously disjointed workflows requiring individual integrations. Additionally, this API supports encrypted user identifiers, including email and phone numbers, enabling event routing to multiple destinations with just one request.

Between the lines: Google is urging us who still use the Campaign Manager 360 API for conversions to transition to the Data Manager API. They assure us that the new framework not only simplifies implementation but also offers more flexibility in measurement and attribution capabilities.

What’s new and fascinating is the introduction of IP ingestion support for Google Ads Customer Match through a new CompositeData field. This means alongside traditional identifiers like email and postal addresses, we can now upload IP addresses as well.

Starting in Q3 2026, incorporating IP addresses with corresponding observation timestamps promises us enhanced Customer Match rates, potentially widening audience reach and elevating match precision.

Why we care. These updates simplify the unification of conversion measurement across Google’s ad products and improve audience matching. For those of us managing large-scale data programs, the benefits could include better attribution and more effective audience targeting.

The bottom line. With the Data Manager API being positioned as the ultimate hub for conversion and audience data, Google offers us a more cohesive system to manage measurement and improve Customer Match across its platforms. Check it out for yourself through Google’s official blog post.

Inspired by this post on Search Engine Land.

June 1, 2026
Unlock DV360 API’s New Demand Gen Features for Enhanced Campaigns

As a digital marketer, I’m thrilled about the new tools that will soon enhance the way I automate campaign management and targeting through existing DV360 workflows. The opportunities for advertisers and developers are about to expand significantly.

Demand Gen campaigns are now more intricately woven into Google’s advertising stack. From June 10th, I’ll have the chance to manage Demand Gen resources directly via the Display & Video 360 API, aligning campaign automation and management workflows more closely with other DV360 inventory types.

What’s happening. Google is set to roll out Demand Gen resource support to Display & Video 360 API partners beginning June 10, with the rollout expected to be fully available by June 24.

The upgrade introduces support for Demand Gen line items, ad groups, and ad formats. This expansion allows developers and advertisers like me to retrieve, create, update, and delete Demand Gen resources through the API. Once enabled, Demand Gen resources will seamlessly appear alongside standard line item and ad group list responses with existing DV360 campaign objects.

Between the lines. For those of us who depend on API integrations, a major immediate effect is that our existing list queries might start returning additional Demand Gen line items and ad groups. Google’s advice to developers is to update integrations before June 10 to ensure our systems can handle these new resource types.

Why I care. This update simplifies automating Demand Gen campaign management within my current DV360 workflows, diminishing the necessity for separate tools or manual processes as I explore YouTube and other discovery-focused inventory.

The bottom line. Demand Gen is transitioning from a beta feature to a standard component of the DV360 API, offering increased flexibility for advertisers and partners like me to programmatically manage campaigns at scale.

Inspired by this post on Search Engine Land.

June 1, 2026
Unlocking New Controls in Google AI Max for Branded Searches
I recently came across a fascinating development in Google Ads that’s really worth discussing. Google seems to be testing new branded search controls within AI Max campaigns, which might just give advertisers a better way to separate branded from non-branded traffic.

If you’re like me, you’ve probably faced challenges with AI Max campaigns capturing branded searches, especially since their launch. It seems Google might finally be addressing this common concern by offering more control over how these campaigns interact with branded queries.

What’s happening. Some advertisers have reported a fresh ‘Branded Searches’ control option within AI Max campaigns. This feature potentially allows us to dictate how the campaigns handle brand-associated searches.

The option includes three settings:
- Show ads on all relevant searches (default strategy)
- Manage branded searches via inclusions and exclusions
- Restrict ads to only appear on unbranded searches
Why we care. For those of us managing campaigns, one major critique of AI Max has been its tendency to capture branded traffic. This traffic is often already covered by dedicated brand campaigns, leading to complications.

Campaigns that pull in branded traffic can pose several issues:
- Increased costs for likely conversions
- Complexities in attribution across different types
- Diminished clarity on incremental gains
- Worries of AI Max overshadowing branded efforts
The ability to focus on purely unbranded searches, newly introduced, could help direct AI Max towards fresh demands and new prospects.

Between the lines. Up until this point, preventing AI Max from engaging in branded queries required exclusion lists. A native setting would simplify this and potentially offer more insight into brand intent handling.

The big picture. Google seems committed to adding more oversight to automated campaigns, reacting to our calls for greater transparency and control over AI.

If these controls are deployed widely, it could indicate Google’s acknowledgment of our traffic management concerns, as they forge ahead with AI automation.

What to watch. Whether this is a full release, a selective test, or just an experiment is still unclear. Keep an eye on your AI Max settings and stay alert for updates from Google regarding branded search controls.

Bottom line. This new control in AI Max might soon empower advertisers to distinctly separate branded and non-branded traffic—something many of us have long requested. But for now, it’s an observation rather than a confirmed rollout.

First spotted. This development was originally highlighted by Paid Search specialist Thomas Eccel, who shared his discovery on LinkedIn.

Inspired by this post on Search Engine Land.
May 29, 2026

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