# Brand Controls in AI Max Experiments: Who Should Test Now > Source: > Published: 2026-08-20 00:00:00+00:00 Brand controls inside AI Max experiments are the feature a specific group of Google Ads advertisers has been waiting for: the ones who declined to test AI Max for Search because a test could not carry their brand inclusions and exclusions into the treatment arm. On August 20, 2026, Google’s Ads and Commerce blog said that friction is gone. It said two other things in the same post, and one of them is not live yet. The three items are easy to blur together because the post presents them as one expansion of AI Max testing and planning. But Google’s own sentence structure separates them. Multi-campaign A/B testing carries an explicit future qualifier. Brand and location controls inside experiments, and Performance Planner’s projected-impact view with one-click apply, carry none; both are written as things you can do now. Search Engine Land’s same-day write-up, published three minutes after Google’s, did not preserve that distinction, and a reader working from the recap alone would not learn that only one of the three carries a September date. This is a decision post, not a recap. It lays out what each feature is, what Google’s text and help-center documentation actually support, where the one genuine documentation gap sits (location controls inside the experiment flow), and then sorts advertisers into the groups that should run the AI Max experiment they avoided, the groups that should wait for September, and the group that needs to query its migration-date fields before designing anything. - 01One announcement, two clocks.Google’s August 20 post gives multi-campaign A/B testing an explicit “Rolling out in September” qualifier and gives brand/location controls and Performance Planner none. The grammar is the availability signal, and it should drive your plan. - 02Brand controls in experiments are live and documented.Google’s AI Max experiments help page lists brand inclusions and exclusions as Step 5 of the current setup flow, applied to both the control and treatment arms for the duration of the test, with an automatic revert if the experiment is not applied. - 03Location controls are announced, not documented in the flow.The blog pairs “brand or location controls” in one present-tense sentence, but no help page we found documents locations of interest as a step inside the experiment setup. Treat the location half as conditional on what your account shows. - 04Performance Planner’s one-click apply is live and independent.Projected impact of bidding or budget changes on existing campaigns, applied directly in one click, with an undo path through Bulk actions. It is a planning tool, not an experiment, and it does not depend on the other two features. - 05Check the September migration clock before you design a test.ACA and campaign-level broad-match campaigns begin auto-upgrading to AI Max on September 1, 2026. If Google is about to migrate you anyway, a voluntary experiment is how you choose the guardrail settings instead of inheriting defaults. ## 01 — The AnnouncementWhat Google actually said, in two *availability treatments*. The primary source is a short post on Google’s Ads and Commerce blog, [published August 20, 2026](https://blog.google/products/ads-commerce/ai-max-testing-planning-tools/). Three consecutive paragraphs describe three features in two grammatical treatments: one carries an explicit future qualifier, and the other two are written in the present tense with none. That contrast, occurring inside one post, is the strongest evidence available about what is live and what is not. The first paragraph introduces multi-campaign A/B testing, framed as building on the one-click experiments AI Max already offers: a way to test different budgets and ROI targets across multiple Search campaigns in a single test. It opens its second sentence with “Rolling out in September,” and gives no day within the month. The second paragraph covers brand and location controls. The tense is present: new capabilities in AI Max experiments “now let you run tests” with those settings enabled. There is no rollout language, no beta label, and no date. The third paragraph covers Performance Planner, and it uses the same construction as the second: the planner “now allows you to see” how bidding or budget changes may affect existing campaigns, and you can apply those suggestions in one click. Again, present tense, no qualifier. ##### Multi-campaign *A/B testing* Google’s stated aim is to show how scaling campaigns affects the bottom line. The paragraph opens with “Rolling out in September,” with no specific date inside the month. Announced, not live. ##### Brand and location controls *inside experiments* “New capabilities in AI Max experiments now let you run tests with these settings enabled.” Brand settings are documented as a step in the setup flow; location controls are not separately documented there. ##### Performance Planner *projected impact* The planner shows how a bidding or budget-target change may affect current campaign performance, and applies the suggestion directly in one click. Present tense, no rollout language, documented in the Performance Planner help page. "If you rely on specific brand or location controls, A/B testing just got simpler. New capabilities in AI Max experiments now let you run tests with these settings enabled, so you can confidently test the impact of AI Max without compromising those guardrails."— Google Ads and Commerce blog, August 20, 2026 Why lean so hard on grammar? Because Google applied the identical present-tense, no-qualifier treatment to Performance Planner, a feature whose help-center page describes one-click implementation as a working mechanic today, and applied a different, explicitly future treatment to the multi-campaign feature. When a vendor differentiates availability inside a single three-paragraph post, the differentiation is the information. Reading all three as one September wave, or all three as live, discards it. ## 02 — The ThesisThree features, *two clocks*, one decision table. The table below is the reconciled view. Each row pairs Google’s own wording from the August 20 post with what the help center documents, and ends with the reading we would use to make a decision. The location row is the one to read carefully: it sits in the “live” group because Google’s sentence puts it there, but its documentation column is honest about what was and was not found. | Feature | Google’s wording, Aug 20 | Availability reading | Help-center documentation | Decision reading | |---|---|---|---|---| | Present tense, no rollout qualifier — reads as live on August 20 | |||| | Brand controls inside AI Max experiments | “New capabilities in AI Max experiments now let you run tests with these settings enabled” | Live | Brand inclusions and exclusions are Step 5 of the experiment setup flow on the “About AI Max experiments” help page; no beta label or rollout date on the page | If brand control was your objection to testing AI Max, the objection is answered. Run the experiment. | | Location controls inside AI Max experiments | Same sentence: “specific brand or location controls,” present tense | Announced as live in the blog; not confirmed in the experiment documentation | Locations of interest are documented only as a general, ad-group-level AI Max feature; no help page we found lists them as a step inside the experiment setup flow | Conditional. Proceed only if your account’s experiment setup shows a location-controls step; otherwise treat it as unverified. | | Performance Planner projected impact + one-click apply | “Performance Planner now allows you to see how changes … may impact your existing campaign performance. In one click, you can apply those suggested changes” | Live | Performance Planner help page describes one-click implementation of suggested budget or bid changes into live campaigns, with monitoring and undo through Bulk actions | Use now on any existing campaign. Independent of the two experiment features. | | Explicit future qualifier — announced for September | |||| | Multi-campaign A/B testing of budgets and ROI targets | “Rolling out in September, this will help you see exactly how scaling up your campaigns impacts your bottom line” | Announced, not live; no day within September given | Not applicable until rollout. The announcement paragraph gives the scope (budgets and ROI targets across multiple Search campaigns) and the September timing; we found no setup documentation to cite yet | Do not design a Q4 plan around it yet. Check back in September. | *no distinguishing timing at all*. That is not a hostile reading of a fast news desk; it is the ordinary cost of compressing three paragraphs into one. It just happens to erase the exact detail an advertiser needs. ## 03 — MechanicsHow an AI Max experiment *actually runs*. The brand-controls change only matters if you understand what an AI Max experiment is, because the mechanic is what makes a guardrail testable without contaminating the comparison. Per Google’s [“About AI Max experiments” help page](https://support.google.com/google-ads/answer/16450159), an AI Max experiment diverts a percentage of an existing campaign’s traffic and budget to a treatment arm with the AI Max toggle on, against a control arm with it off. No campaign copy is created. That is the structural difference from legacy custom experiments, which duplicated the campaign. Google lists three benefits of that design over custom experiments: faster results, because everything runs inside a single campaign; fewer setup errors, because both arms are updated together; and a shorter learning period. Those are vendor framings rather than measured outcomes, and Google does not document what produces them. The structural fact behind the claims is the one to hold on to: the test runs inside the existing campaign rather than in a copy of it. ##### A traffic split, not a clone The experiment diverts a share of the existing campaign’s traffic to a treatment arm with AI Max on. No duplicate campaign is created, unlike legacy custom experiments. ##### Brand settings are Step 5 Brand inclusions and brand exclusions are configured as a numbered step of the current AI Max experiment setup, and they apply to both arms for the duration of the test. ##### Conditions that stop the flow Text customization already enabled, Display network targeting, a Portfolio bidding strategy, Shared budgets, Bidding exploration, or an existing active experiment. The six blockers deserve a closer look before anyone schedules a test, because they decide who can even start. The help page states you cannot create an AI Max experiment through this flow if the campaign already has text customization (formerly Automatically Created Assets) enabled, targets the Display network, is on a Portfolio bidding strategy, uses Shared budgets, uses Bidding exploration, or already has an active experiment. Shared budgets and portfolio strategies are common in larger accounts, so the practical first step is an inventory: which campaigns are eligible for the experiment flow at all, and which need a structural change before a test is possible. If you are newer to the product, our [AI Max general-availability playbook](/blog/google-ai-max-ga-dsa-sunset-agency-playbook-2026) covers the campaign-level settings this experiment toggles on and off; this post assumes that background and focuses on the testing decision. ## 04 — ConfirmedBrand controls: documented, *step by step*. This is the half of the claim that is confirmed beyond the blog post. Google’s announcement links its “capabilities” wording straight to the AI Max experiments help page, and that page documents brand settings as a working part of the current setup flow. Step 5 reads: “Configure Brand settings such as ‘Brand inclusions’ and ‘Brand exclusions’.” The page carries no rollout date, no beta label, and no coming-soon language anywhere. It describes a flow you can follow today. Three details in that documentation are what make brand safety testable rather than merely present. First, brand inclusions and exclusions can be added during experiment setup “even if your original base campaign uses legacy brand controls or has no brand controls enabled.” You do not need to have migrated your base campaign to the newer brand controls before testing. Second, the settings you add apply symmetrically, which is the point of the next callout. Third, the end-of-experiment behavior is defined: if the experiment is not applied, all AI Max settings revert; if the base campaign had legacy brand controls before the test, it returns to those; if it had none, any brand controls added during the experiment are removed. *automatically apply to both the control and treatment arms*for the duration of the experiment. That symmetry is what lets you isolate AI Max’s effect: both arms run under the same brand guardrails, so any difference between them is attributable to the AI Max toggle, not to a brand-list mismatch between arms. For advertisers whose only objection to AI Max was “I cannot risk my brand exclusions in a test,” this closes the objection. The guardrails are set once, applied to both arms, and either persist (if you apply the experiment) or unwind (if you do not). If your concern runs deeper than the exclusion list, into what AI Max is permitted to write in the ad itself, pair this with our guide to [AI Max text guidelines for brand-safe Search ads](/blog/google-ai-max-search-text-guidelines-brand-safety-ads), which covers the text-customization controls that sit alongside brand inclusions and exclusions. ## 05 — The GapLocation controls: announced, *not documented* in the flow. Here is where honesty about sources matters more than a clean story. Google’s blog sentence pairs the two controls: “If you rely on specific brand or location controls, A/B testing just got simpler.” The grammar puts location controls in the live group. But the help page the blog links to documents only brand settings in the experiment setup flow. It does not mention location controls at all. Location controls do exist as a real, documented AI Max feature. Google’s [“How AI Max for Search campaigns works” help page](https://support.google.com/google-ads/answer/15910187) describes locations of interest as an optional, ad-group-level setting that lets you “Reach specific customers based on their geographical intent even in keywordless matches,” with more than one location of interest selectable per ad group, in addition to campaign-level location targeting. That page also carries no rollout language. What it does not do is describe testing those locations inside an experiment, the way the experiments page describes brand settings at Step 5. Two explanations are consistent with the evidence, and we cannot choose between them from primary sources. Google may not have published experiment-flow documentation for location controls yet. Or the location setting may surface identically to brand settings inside the experiment setup, and Google did not write a separate walkthrough. Either way, the correct posture for an advertiser is conditional, not confident. *What is documented: brand settings at Step 5, and locations of interest as a general ad-group feature. What is not documented: a location step inside the experiment flow.*Google’s help pages show no visible last-updated date, so this may change without notice; check the page, not this post, before you commit. ## 06 — Live NowPerformance Planner: live, independent, and *undoable*. The third feature is the easiest to act on because it is not an experiment at all. Performance Planner “now allows you to see how changes, like bidding or budget targets, may impact your existing campaign performance,” and “In one click, you can apply those suggested changes directly to your campaigns.” Both sentences are present tense. Google’s [Performance Planner help page](https://support.google.com/google-ads/answer/9230124) describes the same mechanic in its implementation section: apply suggested budget or bid adjustments for each campaign directly into live campaigns with a single click. The safety net is worth knowing before anyone clicks. Changes made this way can be monitored and undone through the Bulk actions interface afterward. That turns a one-click change to a live campaign from a leap into a reversible action, which is the right frame for a planning tool: project, apply, watch, and roll back if the projection does not hold. Two boundaries on the claim. Neither the blog post nor the help page says which recommendation types the one-click apply covers beyond bidding and budget targets, so do not extrapolate it to creative, targeting, or asset changes. And a March 9, 2026 note on the same help page concerns the discontinuation of Display and Video campaign planning support; it is unrelated to this feature and is not a dating signal for it. "Rolling out in September, this will help you see exactly how scaling up your campaigns impacts your bottom line."— Google Ads and Commerce blog on multi-campaign A/B testing, August 20, 2026 Set that sentence next to the Performance Planner paragraph and the contrast is the whole story. One feature is described as a thing that will happen; the other as a thing you can do. Advertisers who want to see the projected effect of a budget change on an existing campaign do not need to wait for September. Advertisers who want to A/B test budgets and ROI targets across several campaigns in one experiment do. ## 07 — The DecisionWho should test now, who should *wait*, and who should check the migration fields first. Sort yourself by the objection or goal that kept you from testing AI Max, not by the headline. Four groups cover nearly every advertiser reading this. ##### Brand control was your *only objection* The brand inclusion and exclusion mechanic is documented, live, and symmetric across arms. Nothing touches the base campaign unless you explicitly apply the experiment; otherwise it reverts. Run the test you avoided. ##### Location control was your objection Google’s sentence says live; the experiment documentation is silent. Open your own setup flow. If a location step appears next to the brand step, proceed. If not, hold the test or run it with brand controls alone and treat location as an open question. ##### You want multi-campaign budget or ROAS tests Testing budgets and ROI targets across several Search campaigns in one A/B test is explicitly “Rolling out in September,” with no day given. Do not build a Q4 test calendar on it yet; design single-campaign experiments now and revisit when it lands. ##### You run ACA or campaign-level broad match Those campaigns begin auto-upgrading to AI Max on September 1, 2026. Query the per-campaign migration fields first — Google has not documented whether they carry a date before the migration runs — then decide whether a voluntary experiment now is how you keep control of the guardrail settings rather than inheriting Google’s defaults. Group 4 is the one with a clock on it, and the clock was set before this announcement. On April 15, 2026, Google announced that Automatically Created Assets and campaign-level broad-match Search campaigns would auto-upgrade to AI Max starting September 1, 2026, with a gradual rollout expected to conclude by the end of the month. Dynamic Search Ads were separately delayed to February 2027, and we covered [that DSA delay and what to do with the reprieve](/blog/google-delays-dynamic-search-ads-ai-max-migration-feb-2027-playbook) on its own; it does not apply to ACA or broad match. The [two Campaign fields added in Google Ads API v25.1](/blog/google-ads-api-v251-ai-max-migration-date-fields) on August 19 are the API surface for that migration, and querying them is the one pull to make before designing any experiment on those campaigns. Google has not documented whether those fields carry a date ahead of the migration or only fill in once it has run, so read an empty value as unresolved rather than as “nothing scheduled.” In the same window, Google has said manual language targeting will be removed from Search and AI Max for Search in late September, so the September change list is longer than this announcement alone. Everyone in every group can use Performance Planner today. It is independent of the experiment features, works on the campaign as it stands, and has an undo path. Treat it as the low-risk first move while the experiment decision settles. ## 08 — SequenceThe *order* to do it in. The decision matrix tells you which group you are in. The sequence below tells you what to do first, because the steps depend on each other and the September clock runs regardless of whether you act. **Query the migration fields.** For any campaign using ACA or campaign-level broad match, pull the per-campaign migration fields before anything else. If a date comes back and it falls in early September, that changes the calculus for a voluntary experiment that needs weeks to read; if the fields come back empty, treat that as unresolved rather than as reassurance, and re-query on a schedule.**Inventory experiment eligibility.** Check each candidate campaign against the six setup blockers: text customization already on, Display network, Portfolio bidding, Shared budgets, Bidding exploration, or an active experiment. Shared budgets and portfolio strategies are the usual culprits in mature accounts.**Open the experiment setup and look at Step 5.** Confirm the brand-settings step is present in your account, and note whether a location-controls step appears beside it. That one observation resolves the location question for your account better than any article can.**Set brand guardrails once, for both arms.** Add inclusions and exclusions during setup so they apply symmetrically. Decide in advance whether you will apply the experiment if it wins, because that determines whether the controls persist or revert.**Run Performance Planner in parallel.** Project the impact of any pending budget or bidding change on existing campaigns, apply what holds up, and keep Bulk actions open as the undo path.**Park multi-campaign tests until September.** Note the feature in your roadmap with Google’s own wording, and do not promise a stakeholder a cross-campaign budget test before it ships. The pattern underneath all six steps is the same one we apply across [paid media engagements](/services/paid-media): separate what the platform says is live from what it says is coming, verify the live part against the help center and your own account UI, and let the deadline calendar, not the announcement calendar, set your priority. Where the test itself is the hard part, our [analytics and measurement work](/services/analytics) starts with the experiment design, so the read-out is trustworthy before a single impression is diverted. *The window in which a voluntary, guardrailed experiment is the better option than an inherited migration is measured in weeks, not quarters.*That is why the availability distinctions in this post matter now and may matter less by October. ## 09 — ConclusionRead the *qualifiers*, then decide. ### Two of three read as live. One is September. Location is the unconfirmed half. Google’s August 20 post did three things at once and told you, through its own grammar, which ones you can act on. Brand controls inside AI Max experiments are live and documented down to the step number. Performance Planner’s projected impact and one-click apply are live, independent, and reversible. Multi-campaign A/B testing of budgets and ROI targets is *rolling out in September* and nothing more specific than that. The one place to stay careful is location controls. Google’s sentence puts them alongside brand controls in the present tense, but the experiment documentation does not, and the general locations-of-interest feature is documented at the ad-group level rather than inside the experiment flow. Treat it as *conditional on what your account shows*, and say so plainly to anyone you report to. If brand control was the reason you never tested AI Max, the reason is gone, and the September migration of ACA and broad-match campaigns means a voluntary experiment now is how you choose your guardrails instead of inheriting defaults. Query the migration fields, check the six blockers, set the brand settings once for both arms, and run the test you have been avoiding.