Microsoft’s AI Max rollout created an unusual naming problem for advertisers. Google and Microsoft now use the same product name for Search automation built around expanded matching, customized ad text, and landing-page selection.
As of September 2026, the products are at different stages of rollout. Microsoft has only recently moved AI Max into general availability, while Google is already folding older Search automation into the same framework.
The comparison is therefore less about feature names and more about how those features behave inside each advertising system. Controls, reporting, imports, audience options, and the future of Dynamic Search Ads create the differences that matter once campaigns are live.
Microsoft and Google Built AI Max Around the Same Three Ideas
Neither product is a separate campaign type. Google positions AI Max as an optimization layer within Search campaigns, while Microsoft offers its version as a suite of AI features inside standard Search campaigns.
Both products revolve around three core functions:
- expanding search-term matching beyond the existing keyword list;
- customizing ad text using existing assets and website content;
- choosing a more relevant landing page through final URL expansion.
Much of the setup will look familiar to advertisers working across both platforms. Microsoft even uses the same text customization and final URL expansion terminology found in Google’s workflow.
The labels may match, but the surrounding controls do not. Reporting, targeting options, migration rules, and legacy campaign behavior can all differ once the automation starts influencing traffic.
Microsoft AI Max vs Google AI Max Compared
Feature lists make the products look almost interchangeable. Day-to-day account management shows a wider gap, particularly around control depth, reporting, migration, and imported campaigns.
| Area | Google AI Max | Microsoft AI Max |
|---|---|---|
| Product type | Optimization layer for Search | AI feature suite for Search |
| Search expansion | Broad match plus asset-based and keywordless technology | AI-powered search term matching |
| Text customization | AI-generated or adapted ad copy | AI-generated or adapted ad copy |
| Search-term reporting | AI Max match type, Source column, and query/headline/URL views | AI optimized labeling and search-term landing-page reporting |
| AI asset reporting | AI Max asset performance available in asset reporting | AI-generated and advertiser-provided assets shown together |
| Control depth | More documented ad-group-level controls | More campaign-centered AI Max setup |
| New Search campaigns | AI Max selected by default | AI Max enabled by default |
| Migration pressure | Older Search automation is being consolidated into AI Max | Legacy structures can continue alongside AI Max |
| DSA direction | Auto-upgrade begins February 2027 | DSA remains supported |
| Google Import | Not applicable | Compatible AI Max settings can be imported from Google Ads |
No single row predicts which platform will perform better. What the table does show is how two campaigns with similar starting points can develop different traffic patterns and management needs after launch.
Search Term Matching Expands Beyond the Keyword List
Traditional Search structure begins with keywords. AI Max gives both platforms more ways to connect ads with searches that were never written into the original keyword set, then applies each platform’s own inventory, bidding logic, controls, and reporting to that wider pool.
How Google Ads AI Max Expands Search Matching
Google Ads AI Max combines broad match with asset-based, keywordless, and landing-page-based technology. Google’s AI Max search term matching documentation explains how those signals can identify relevant searches the existing keyword list might otherwise miss.
This is broader than simply switching existing keywords to broad match. Campaign content and destination pages can also influence whether a query enters the auction.
Google exposes several controls below campaign level. Search term matching can be turned off for individual ad groups, while brand inclusions, URL inclusions, and locations of interest can also operate more granularly in supported configurations.
How Microsoft Advertising AI Max Handles Search Expansion
Microsoft Advertising AI Max pursues the same general objective inside Microsoft’s Search ecosystem. Microsoft’s August 2026 AI Max release describes search term matching as one of the product’s three central components, with particular emphasis on longer and more intent-rich searches.
That gives campaigns more room to discover useful queries without requiring advertisers to anticipate every variation in advance. Results from Google should not become the benchmark for Microsoft traffic, though. Inventory, auction conditions, targeting options, and audience behavior can all change the outcome.
Microsoft keeps familiar Search controls around the additional automation. Negative keywords, budgets, bidding strategies, conversion goals, targeting settings, brand controls, and term exclusions continue to define the campaign’s boundaries.
Google Gives Advertisers More Ad-Group-Level Steering
Google currently documents more AI Max-specific decisions below campaign level. This matters most in accounts where services, products, or types of intent need different tolerances for search expansion.
A discovery-focused ad group may benefit from wider matching. An expensive service category, regulated offer, or tightly controlled brand segment may need much narrower rules.
Microsoft still provides meaningful controls, but more of its AI Max setup remains centered on the Search campaign and the settings already surrounding it.
Text Customization Comes With Similar Guardrails
Text customization allows both platforms to adapt creative instead of relying entirely on advertiser-written combinations. Google can generate or modify copy using campaign assets and landing-page content, while Microsoft incorporates automated creative into its AI Max setup.
Generated wording still has to respect brand rules, regulated claims, local restrictions, pricing, and the offer on the destination page. Microsoft provides term exclusions for AI-generated creative. Google exposes AI Max asset performance in reporting and provides controls advertisers can use to restrict generated messaging.
The concern is not how much copy either system can produce. Accuracy, commercial fit, and approval requirements matter more.
Controls are still evolving as well. An account tested earlier in the rollout may not expose exactly the same options available in September 2026, so current settings are more useful than an old AI Max screenshot or workflow.
Final URL Expansion Changes Which Page Gets the Click
Final URL expansion allows both systems to send a searcher to another relevant page on the advertiser’s domain. Better query-to-page alignment can help, but the landing page is no longer determined entirely by the original ad setup.
Large websites need more scrutiny here. A page can look contextually relevant while being outdated, poorly localized, weak at qualification, or less persuasive than the destination a campaign manager would have chosen manually.
Landing-page reporting should therefore sit beside search-term and conversion data. A higher conversion count does not prove that automated routing improved if the selected pages attract weaker prospects or steer visitors toward the wrong offer.
Lead generation makes the issue especially visible. Two landing pages can post similar conversion rates while producing inquiries with very different sales value.
Search-Term Reporting and AI Signals Differ
Reporting is one of the clearest areas where the products stop feeling interchangeable. Both platforms let advertisers investigate traffic created through expanded matching, but they organize that visibility differently.
Google Shows Match Source and Query-Journey Details
Google marks expanded traffic with an AI Max match type and provides a Source column that helps explain whether matching came through broad-match expansion or keywordless matching. Reporting can also connect a search with the headline, landing page, campaign, and ad group involved.
Some low-volume searches may be omitted from search-term reporting for privacy reasons. The visible report is therefore not necessarily a complete record of every query involved in delivery.
Even with that limitation, the added context makes performance changes easier to investigate. New queries, different creative, and automated landing-page selection can be examined separately instead of being folded into one AI Max result.
Microsoft Uses AI Optimized and Landing-Page Reporting
Microsoft marks expanded traffic with an AI optimized match-type label in search-term and keyword reporting. Advertisers can also inspect generated assets and use search-term landing-page reporting to see how final URL expansion routed traffic.
The review process remains straightforward: identify the new search, examine the message and destination, then look at what happened after the click. Campaign-level conversion totals alone do not explain whether the additional reach was useful.
The Audience Context Around AI Max Is Different
AI Max also sits inside two different advertising ecosystems. Microsoft Search campaigns can use LinkedIn profile targeting based on company, industry, job function, and Job Seniority where those options are available.
For B2B advertisers, that can add another layer of context around otherwise similar search demand. Google has its own audience, bidding, brand, location, and URL controls, so copying an AI Max setup from one platform does not recreate the same environment on the other.
The Same AI Max Name Leads to Different Migration Paths
Migration creates a sharper separation than the three headline features. Google is folding several older forms of Search automation into AI Max, while Microsoft has not committed to the same treatment of its legacy campaign structures.
Google Is Consolidating Legacy Search Features Into AI Max
Starting in September 2026, Google began automatically upgrading campaigns using the campaign-level broad match setting or text customization, formerly Automatically Created Assets, into AI Max. Dynamic Search Ads operate on a later timetable, with DSA auto-upgrades scheduled to begin in February 2027.
Those are separate transitions, not one broad September migration. The timing is easier to follow in Dynamic Search Ads to AI Max, where the changes are mapped to the settings they actually affect.
Google’s revised Dynamic Search Ads migration timeline confirms the broader direction. Search automation that previously lived under several settings and campaign structures is gradually moving under AI Max.
Microsoft Is Keeping DSA Alive
Microsoft has chosen a different path so far. AI Max is generally available and enabled by default for new Search campaigns, yet Dynamic Search Ads remain supported until further notice.
A structure Google is preparing to migrate can therefore continue as a standard DSA campaign in Microsoft Advertising. Accounts that once mirrored each other may gradually develop different architectures even while scheduled imports remain active.
Teams managing both platforms will increasingly need separate campaign maps. Shared branding does not mean older Search structures are evolving on the same timetable.
Google Import Creates Parity on Paper, Not in Practice
Microsoft can import AI Max settings from Google Ads, including through scheduled Google Import workflows. This reduces duplicate setup work, but the imported settings still enter Microsoft’s inventory, reporting, targeting options, campaign rules, and conversion setup.
A post-import audit is worthwhile. Advertisers should verify:
- whether AI Max remained enabled as expected;
- which brand, search-term, and exclusion controls carried over;
- how final URL expansion routes Microsoft traffic;
- whether a former Google DSA structure appears differently on Microsoft;
- whether conversion goals and reporting labels still match the intended setup.
DSA exposes the limitation particularly well. When Google upgrades DSA campaigns, Microsoft says Google Import will convert those structures back to standard Dynamic Search Ads campaigns in Microsoft Advertising. Two accounts can therefore stop being structurally equivalent even though the import itself completed successfully.
Google Import is best treated as a setup shortcut rather than a promise of campaign parity. The Microsoft account still needs its own review and performance test.
Why 14% vs 13.6% Is the Wrong Comparison
The vendor performance claims look close enough to invite a simple comparison. Google reports that non-retail advertisers activating AI Max in Search typically see about 14% more conversions or conversion value at a similar CPA or ROAS.
Microsoft’s published test uses a different methodology. Across 44 advertiser-run A/B experiments conducted from June through August 2026, Microsoft reported approximately 13.6% spend-weighted conversion uplift with AI Max enabled. Only 10 of those experiments produced statistically significant positive results for conversions or conversion value.
The datasets, advertiser populations, periods, statistical treatment, and outcome definitions do not match. Putting 14% beside 13.6% therefore does not establish which platform will perform better in a particular account.
Both results support a narrower conclusion: AI-assisted Search expansion can produce incremental gains under some conditions. The meaningful benchmark remains the advertiser’s own controlled test against a credible baseline.
AI Max Does Not Turn Search Into Performance Max
Google AI Max does not replace Performance Max. One extends a Search campaign, while the other remains a separate cross-channel campaign type with access to broader inventory.
That difference becomes particularly important in accounts running both formats. AI Max vs Performance Max separates the Search optimization layer from the cross-channel campaign model, which helps explain why enabling AI Max is not equivalent to moving budget into PMax.
Microsoft also operates Performance Max. Enabling Microsoft AI Max therefore does not imply abandoning broader automated campaign formats there either. The two campaign types solve different problems despite sharing substantial automation underneath.
Which AI Max Setup Makes More Sense for Lead Generation?
Lead-generation accounts should pay less attention to vendor uplift claims and more attention to the conversion signal each system receives. Search expansion can uncover valuable long-tail demand, but it can also produce more low-quality forms when every submission is treated as equally useful.
A useful AI Max test should track more than conversion count. At minimum, compare:
- qualified lead rate;
- cost per qualified lead;
- sales opportunity rate;
- landing pages selected by AI Max;
- conversion lag;
- downstream revenue, booked jobs, or another business outcome.
This becomes particularly important with Microsoft Ads for B2B lead generation. Professional audience data can add context to search demand, but it cannot make an unqualified inquiry valuable. CRM qualification and pipeline data show whether expanded reach actually improved the account.
Google’s more granular documented controls may suit accounts that separate services tightly and want different expansion rules by ad group. Microsoft’s Google Import workflow can reduce setup work for teams using both platforms, but convenience at launch does not remove the need for an independent lead-quality test.
Running Both Platforms Requires Two Testing Plans
Enabling Microsoft AI Max after a Google Import does not turn Microsoft Advertising into a second copy of the Google account. Controls, migration rules, inventory, reporting, professional audience options, and legacy campaign handling can all push the two accounts in different directions.
Business measurement should stay consistent, but the campaign tests should remain separate. Conversion definitions can align where possible while expanded search terms, selected landing pages, generated assets, and post-CRM lead quality are reviewed independently on each platform.
The shared AI Max name is useful shorthand for a similar direction in Search automation. It is not a reason to transfer settings or performance assumptions from one network to the other.