Google vs Microsoft AI Max: the same idea, with very different controls

Microsoft has made AI Max generally available across Microsoft Advertising, giving advertisers a second major version of an AI-powered optimization layer built directly into traditional Search campaigns.

At first glance, Microsoft AI Max looks remarkably similar to Google’s version. Both expand beyond static keyword matching, generate additional ad copy from existing assets and website content, and can send users to a landing page that better matches their intent.

That similarity is real. But it is not the whole story.

The more important differences between Google vs Microsoft AI Max sit in the mechanics: how much control advertisers have at campaign and ad-group level, how search terms are reported, which platform signals can influence matching, how experiments are structured, and how easily teams can isolate individual parts of the automation.

For paid media teams, especially those managing B2B SaaS accounts with long sales cycles and strict lead-quality requirements, those differences matter more than the AI Max label itself.

What Microsoft actually launched

Microsoft announced the general availability of AI Max for Search on August 27, 2026 after running the product through earlier pilot stages.

Like Google’s product, Microsoft AI Max is not a new campaign type. It is a set of AI features that sits inside a standard Search campaign.

Microsoft groups three main capabilities under AI Max:

1. Search term matching expands beyond the advertiser’s keyword list by using keywords, ads, landing pages and contextual signals to identify additional relevant searches.
2. Text customization uses existing advertising assets and website content to generate and test additional messaging.
3. Final URL expansion can send a searcher to another relevant page on the advertiser’s website instead of always using the originally specified landing page.

Microsoft says the three capabilities work best together because the platform can connect the search query, generated creative and landing page as one intent-driven journey.

Google’s AI Max follows essentially the same architecture. Its Search campaigns use search term matching, text customization and Final URL expansion to expand reach and dynamically adapt ads and landing pages.

That makes AI Max less of a Microsoft-versus-Google feature race and more of an emerging model for how both platforms think Search advertising should work.

Google AI Max vs Microsoft AI Max at a glance

Area	Google AI Max	Microsoft AI Max
Campaign type	Enhancement to Search campaigns	Enhancement to Search campaigns
Search term expansion	Yes	Yes
AI-generated text	Yes	Yes
Final URL expansion	Yes	Yes
Negative keywords	Supported	Supported
Brand controls	Supported	Supported
Experimentation	AI Max experiment within an existing campaign	Search Experiment using a test campaign
AI Max-specific ad-group controls	More extensive	Primarily campaign-level
Search term visibility	Some queries may be withheld	Microsoft says clicked AI Max queries are reportable
Platform-specific signals	Google ecosystem signals including Search and YouTube	Microsoft ecosystem signals including LinkedIn-related data
New Search campaigns	AI Max selected by default	AI Max enabled by default, but can be disabled
Google Import	Not applicable	Can import AI Max settings from Google Ads

The basic product promise is therefore almost identical. The operating model is not.

The biggest difference is where advertisers get control

Google gives advertisers more AI Max-specific decisions at the ad-group level.

When AI Max is enabled in Google Ads, search term matching is switched on at campaign level but can then be disabled for individual ad groups. Google also supports controls such as URL inclusions, brand inclusions and locations of interest at the ad-group level.

That gives sophisticated accounts more room to decide where expansion should and should not happen.

Microsoft takes a more consolidated approach. Its three core AI Max capabilities can be controlled individually, but AI Max-specific configuration is primarily handled at the campaign level rather than through a new layer of ad-group controls.

This creates an interesting trade-off.

Google provides more granular steering. Microsoft provides a simpler testing surface.

A company may be comfortable allowing AI-driven URL expansion, for example, without immediately opening search term matching as widely. Microsoft makes that separation relatively straightforward.

Google can provide deeper controls once AI Max is active, but advertisers need to understand how campaign-level and ad-group-level settings interact.

Neither model is automatically better. The right one depends on how tightly your campaign structure maps to different products, markets, buyer intents and conversion economics.

Microsoft currently has an important reporting advantage

AI-powered query expansion creates a basic problem for advertisers.

If the platform can decide which searches qualify for your campaign, you need to know what those searches were.

Microsoft says AI Max queries that result in clicks are available through its search term reporting, including a Search Term Landing Page report that connects queries with the pages users ultimately reached.

Microsoft also labels expanded AI Max traffic using an AI optimized match type.

Google has improved AI Max reporting considerably. Its search term reports identify AI Max traffic and can show whether expansion came through broad-match or keywordless matching. Google has also introduced reporting that connects search terms, headlines and URLs.

But Google can still withhold some search queries for privacy reasons.

That distinction becomes significant when AI is responsible for broadening campaign eligibility. The more freedom the platform gets to find incremental traffic, the more valuable query-level transparency becomes.

For B2B SaaS advertisers, this can be particularly important. A campaign may appear to be producing conversions at an acceptable CPA while expanding into informational, student, job-seeker, low-ACV or otherwise commercially weak searches.

Platform conversions alone will not expose that problem. Search-term reporting combined with CRM lead-quality data will.

That is why we would still treat AI Max as an automation layer that requires active search-term and pipeline monitoring rather than a replacement for paid-search management.

Our broader view on this is covered in Google Ads automation: when to trust the algorithm and when to override it.

Google and Microsoft are not feeding AI Max the same signals

The products may share a name and similar features, but the systems underneath them have access to different ecosystems.

Google can draw on signals connected to its own search, audience and media ecosystem, including previous search behaviour, conversion data, landing pages, keywords, first-party audiences and Google properties such as YouTube.

Microsoft has a different data advantage. Its ecosystem includes Microsoft search behaviour and audience data, while Microsoft Advertising can also use LinkedIn-related professional signals in parts of its advertising stack.

For B2B advertisers, that difference should not be dismissed.

A SaaS company trying to reach CFOs, IT leaders or HR decision-makers may discover that the same broad campaign structure produces materially different incremental queries and audiences across Google and Microsoft.

That means importing a successful Google campaign into Microsoft should not be treated as proof that both AI Max implementations will behave identically.

Microsoft has made that import process easier. AI Max settings can now carry over through Google Import, and scheduled imports can continue syncing relevant AI Max settings.

The configuration can transfer. The underlying audience and query environment does not.

The experiment architecture is also different

Both platforms give advertisers a way to test AI Max before committing fully, but they structure those tests differently.

Google has introduced dedicated AI Max experiments that divide traffic and budget inside the existing Search campaign.

Instead of cloning the entire campaign, Google keeps the control and AI Max treatment within the same campaign. Google says this reduces setup differences and learning issues that can occur when two separate campaigns are compared.

Microsoft uses its Search Experiments framework, where an existing campaign is compared against a test version with AI Max enabled.

The practical objective is the same: establish whether AI Max adds incremental business value. The mechanics are different.

This matters because poor experiment design is one of the easiest ways to convince yourself that automation is working.

A useful AI Max test should compare more than clicks or platform conversions.

• Spend
• Search terms
• Conversion rate
• Qualified conversion volume
• Cost per qualified lead
• SQLs or opportunities where enough data exists
• Pipeline value
• Landing pages selected by AI Max
• CPA or ROAS
• Any deterioration in lead quality

Google has recently expanded its own AI Max testing tools with additional budget and ROI experiments, which makes controlled testing increasingly important to how Google wants advertisers to adopt automation.

Be careful comparing the performance claims

Both companies are publishing positive early results.

Google says advertisers activating AI Max in Search typically see 14% more conversions or conversion value at a similar CPA or ROAS.

Microsoft says its early tests produced at least an 8% increase in conversions versus control Search campaigns without AI Max.

Microsoft’s footnote provides additional context. Its analysis covered 44 advertiser-run A/B experiments between June and August 2026, with a spend-weighted conversion uplift of approximately 13.6%. Ten experiments recorded statistically significant positive results on conversions or conversion value under Microsoft’s stated methodology.

Those numbers are interesting. They should not be placed side by side and interpreted as Google 14% versus Microsoft 8%.

The companies used different datasets, advertisers and methodologies. These are platform-reported studies, not an independent head-to-head test.

The useful conclusion is simpler: both platforms have evidence that widening query matching while adapting creative and landing pages can create incremental conversions.

Whether those conversions are incremental business value is something each advertiser still needs to establish.

For B2B SaaS, that distinction is crucial. A 15% increase in demo forms accompanied by a 30% decline in sales acceptance is not an improvement.

Conversion tracking becomes even more important under AI Max

AI Max gives the advertising platform more freedom to decide which searches matter. That makes the conversion signal used to teach the system substantially more important.

Google explicitly recommends conversion-based Smart Bidding for AI Max features such as search term matching, Final URL expansion and text customization.

Microsoft also positions conversion data and automated bidding as core inputs into how AI Max optimizes campaigns.

This creates a familiar problem for B2B advertisers.

If your primary conversion is simply form submitted, the algorithm will become increasingly good at finding people who submit forms.

That does not mean it will become increasingly good at finding companies that become customers.

AI Max should therefore strengthen the case for feeding deeper funnel signals back into advertising platforms through offline conversion imports, CRM integrations and value-based bidding.

A B2B SaaS account with clear differentiation between a lead, qualified lead, opportunity and customer is in a much stronger position to use this kind of automation than an account where every form submission has the same value.

This is also why our approach to Google Ads management for B2B SaaS focuses on qualified pipeline rather than treating every platform conversion as equal.

Your landing pages are becoming targeting inputs

Final URL expansion is easy to think about as a landing-page feature. It is more important than that.

Both Google and Microsoft use website content as part of the context that helps AI Max understand what the advertiser sells and which queries may be relevant.

Your website is therefore becoming part of the campaign’s targeting infrastructure.

Poor page architecture, vague copy, outdated product descriptions and overlapping landing pages can create problems long before a visitor actually reaches the site.

The advertising system itself may struggle to understand which page should serve which intent.

That makes a few things increasingly important:

• One clear commercial purpose for each important landing page
• Specific product and service language
• Accurate titles and page copy
• Consistent terminology across ads and landing pages
• URL exclusions for pages that should never receive paid traffic
• Clean conversion paths
• Clear differentiation between products with very different economics

Google itself warns advertisers to check tracking templates before enabling Final URL expansion because dynamic landing-page selection can create broken destination URLs when tracking templates are not configured correctly.

And if your campaign relies heavily on pinned responsive search ad assets, check the settings carefully. Google notes that pinned RSA assets may not be respected when Final URL expansion or URL inclusions are being used.

This is not an automation setting to switch on without auditing the site it will be reading.

Google is moving faster toward AI Max as the default

There is another important difference in context.

Google is actively consolidating existing Search automation into AI Max.

Beginning in September 2026, campaigns already using text customization, previously known as automatically created assets, are being moved into the AI Max framework.

New Google Search campaigns also have AI Max selected by default, although advertisers can modify or disable the relevant settings.

We covered the implications of that change when Google began making AI Max the default for eligible Search campaigns on September 1.

Microsoft is moving in the same general direction for new campaigns. AI Max is enabled by default when advertisers create new Microsoft Search campaigns, but it can be switched off during or after creation.

Microsoft is taking a different approach to Dynamic Search Ads.

Google is moving existing Dynamic Search Ads toward AI Max, with another automatic upgrade phase scheduled for February 2027.

Microsoft says it will continue supporting Dynamic Search Ads until further notice.

That gives Microsoft advertisers another choice for now, while Google’s Search product stack is being consolidated more aggressively around AI Max.

What should B2B SaaS advertisers test first?

AI Max is much easier to test when the account underneath it is already healthy.

Before enabling it across an entire paid-search programme, we would use a staged approach.

1. Fix measurement first

Make sure conversions represent business value.

Where possible, distinguish qualified leads from raw form submissions and send offline stages back into the advertising platform.

If your conversion data is unreliable, giving an algorithm more targeting freedom will usually amplify the problem rather than solve it.

2. Start with a mature campaign

Choose a campaign with enough traffic and conversion history to produce a meaningful control.

Do not use a brand-new campaign with unstable performance to decide whether AI Max works.

3. Audit search terms before the test

Record the existing query mix.

You need a baseline before evaluating whether AI Max genuinely discovers incremental commercial intent or simply expands reach.

4. Audit every URL the system could reach

Exclude pages that are not suitable paid destinations.

That can include careers pages, support documentation, login pages, irrelevant resources, old landing pages or product areas with different economics.

5. Review existing creative

AI-generated text does not start from nowhere.

Both platforms use your existing ads and website as source material. Bad inputs make bad automation easier to scale.

6. Test against downstream metrics

Do not stop at the platform’s conversion column.

For B2B campaigns, evaluate qualified leads, sales acceptance, opportunities and pipeline wherever your volume permits it.

7. Expand gradually

If AI Max finds useful incremental demand without damaging lead quality, widen the test.

If it does not, inspect search terms, landing-page selection, conversion signals and campaign structure before assuming the answer is simply more budget.

So, is Google AI Max or Microsoft AI Max better?

There is no useful universal answer yet.

Google currently offers a more granular AI Max control structure, particularly at the ad-group level, and can draw on Google’s enormous search and media ecosystem.

Microsoft gives advertisers a comparatively clean campaign-level control model and strong search-term visibility, while its broader ecosystem can bring different B2B signals into the picture.

Microsoft may therefore be particularly interesting as a controlled second environment for advertisers already testing Google AI Max.

Not because Microsoft is automatically better.

Because two platforms using similar AI Max mechanics against different user populations, data signals and auction environments create an opportunity to learn which forms of expanded intent actually produce business value.

For paid media teams, that is more useful than asking which company has the better AI.

FAQs about Google and Microsoft AI Max

The OneMetrik take

AI Max is another sign that the keyword is becoming less of a hard boundary and more of an input.

That does not make keyword strategy irrelevant. It changes the job.

Advertisers increasingly need to manage the inputs and guardrails around automated matching: conversion quality, negative keywords, website content, landing-page structure, brand controls, budgets and the business value assigned to different outcomes.

The platforms are taking on more of the work between those inputs.

That makes weak measurement more dangerous, not less.

Google and Microsoft can both become very good at finding more of whatever your account tells them is valuable.

The strategic question is whether you have told them the right thing.

For B2B SaaS teams, AI Max should therefore be evaluated against qualified pipeline and revenue rather than how many additional searches the algorithm can reach.

That is the test that matters.