Google vs. Microsoft AI Max: Similarities and Key Differences

With the global rollout of Microsoft AI Max for search campaigns, a key question arises: how similar is this technology to Google’s AI Max, and what are the fundamental differences between them? At first glance, both platforms use a remarkably similar approach to artificial intelligence. They help ad campaigns expand beyond traditional keyword targeting, automatically adapt ad creatives, and dynamically select the most relevant landing pages. Similarities Between Google and Microsoft AI Max Search Query Reach ExpansionAI Max goes beyond the manually added keyword list. The system analyzes keywords, ad creatives, landing page content, user intent, and search context. This allows it to identify relevant queries that do not directly match existing keywords—an capability especially critical for complex, long-tail conversational searches. Automated Ad Copy AdaptationBoth systems leverage existing ad assets and website content to generate additional messaging variations. AI selects the most appropriate text combinations based on the specific user and search query, making ads more personalized and relevant. Dynamic Landing Page SelectionAI Max does not always direct users to a single, predetermined landing page. Instead, it evaluates user search intent and routes them to the page on the site that best fulfills their query. This yields a more cohesive user journey: Search Query → Ad Copy → Relevant Landing Page. Where the Differences Lie Despite their similar underlying mechanics, Google AI Max and Microsoft AI Max are not identical solutions. The platforms differ in their approaches to automation, available settings, advertiser control levels, and reporting capabilities. Consequently, applying an AI Max strategy from one ad platform to the other without adaptation is not recommended. Key areas to consider separately include: Core features shared by Google and Microsoft AI Max Platform-specific capabilities and limitations Methods for controlling automation and analyzing performance Best practices for testing AI Max in new and existing campaigns without compromising baseline performance What Google and Microsoft AI Max Have in Common Beyond basic capabilities, Google and Microsoft AI Max share a similar operating principle and set of core mechanics. AI Max is a Feature Suite, Not a Separate Campaign Type Unlike Performance Max, Demand Gen, Audience Ads, and other campaign formats, AI Max does not create an entirely separate campaign type. Instead, it is a set of advanced features that can be toggled on within existing search campaigns. The three core functions of AI Max deliver maximum value when used in tandem, which is why advertisers typically see the best results by activating them simultaneously: Search Query Match Expansion: The system identifies new, relevant queries that fall outside the standard keyword list. Since these queries may differ from the initial seed semantics, ad copy adaptation ensures the ads remain contextually relevant. Final URL Expansion: Automatically determines the page on your website that best matches the user's specific query. This is particularly valuable for sites with extensive product catalogs or numerous landing pages. Text Asset Adaptation and Generation: Adjusting the ad copy to fit a specific search query works seamlessly with dynamic landing page selection. The ad presents a tailored offer that aligns directly with the page the user will land on. Together, these features create a cohesive feedback loop: New Queries → Adapted Ad Copy → Most Relevant Landing Page. Do You Need to Activate All Features at Once? No. If an advertiser is not ready to hand over full control to AI immediately, AI Max can be introduced incrementally. For instance, an e-commerce store with an extensive inventory and consistent margins might start by testing Final URL Expansion alone. This expands coverage across product pages without requiring separate ad groups for every single item. At the same time, the advertiser can hold off on search query match expansion. This staged approach makes it easy to measure the impact of AI Max while maintaining tight control over campaign structure. Risk-Free Testing Through Experiments Whether activating one, two, or all three features, AI Max can be thoroughly vetted using controlled experiments. While both platforms support safe testing, Google and Microsoft implement it differently: In Google Ads, experiments run within the existing campaign by splitting traffic and routing a percentage to a test variant with AI Max enabled. In Microsoft Advertising, Search Experiments compare the original baseline campaign against a duplicated test campaign with AI Max turned on. Either method allows advertisers to measure the performance impact of automation before scaling changes across their entire account. How to Properly Test AI Max To evaluate the true impact of AI Max on ad performance, conducting controlled testing is essential. Here are key best practices to ensure reliable results: Start with Your Top-Performing CampaignSelect a campaign with stable historical performance, sufficient traffic, and a healthy conversion volume. Low-data campaigns create statistical noise, making it difficult to discern whether performance changes stem from AI Max or routine traffic fluctuations. Implement a 50/50 Traffic SplitDivide traffic evenly between the control group and the test group. A balanced split provides a clean baseline comparison to evaluate the isolated impact of AI Max. Avoid Ending Tests PrematurelyAI features and Smart Bidding strategies require a learning period. A common pitfall is drawing conclusions just a few days after launch. Set up a full-scale A/B test and give the system adequate time to collect data and adapt. Focus on Business Results, Not Just ClicksAn increase in click volume alone does not equate to higher campaign efficiency. Keep primary focus on business-impacting metrics: Conversion Rate (CR) Cost Per Acquisition (CPA) Return on Ad Spend (ROAS) Revenue/Conversion Value (overall financial impact) Conversion-Based Bidding: A Must for Search Term Matching Expanded search term matching relies heavily on conversion data. Consequently, conversion-oriented bidding strategies are required to run this AI Max feature effectively. To supply algorithms with sufficient signal data, aim for a minimum of 15–30 conversions within a 30-day window. If a campaign cannot reach this threshold, defer AI Max testing until higher volume is established, or implement micro-conversions with value rules based on funnel progression: Started Application: $10 value Midway Through Application: $20 value Completed Application: $50 value Approved Loan/Contract: Actual business value (synced via offline conversion import) This structured value mapping helps algorithms recognize and optimize toward high-intent user actions. Brand Safety Controls: Google vs. Microsoft Both Google and Microsoft provide controls to ensure automation does not compromise brand safety or account structure, offering tools for brand inclusions, brand exclusions, term exclusions, and message constraints. However, management caps and limits differ significantly across both platforms: Brand Inclusions Google Ads: Up to 10 lists per campaign (up to 5,000 brands per list) Microsoft Advertising: Up to 20 lists per campaign (up to 100 brands per list) Brand Exclusions Google Ads: Up to 10 lists per campaign (up to 5,000 brands per list) Microsoft Advertising: Up to 20 lists per campaign (up to 100 brands per list) Term Exclusions Google Ads: Up to 25 per campaign Microsoft Advertising: Up to 25 per campaign Message Constraints Google Ads: Up to 40 per campaign Microsoft Advertising: Up to 40 per campaign Disclaimer Functionality Differences Microsoft supports integrated disclaimers that do not consume ad copy real estate and remain fully compatible with AI Max. Google tests disclaimers as dedicated extensions, which may occupy the second description line of the ad creative. Differences Between Google and Microsoft AI Max Although the core capabilities of AI Max in Google and Microsoft share many similarities, the platforms take noticeably different approaches toward feature controls, search query matching, and data transparency. Unified Enablement vs. Granular Feature Selection While all three core AI Max functions deliver peak performance when used together, advertisers often prefer to test each capability independently. Microsoft Advertising provides full granularity: all three AI Max features are set up as individual toggles, allowing advertisers to turn them on or off independently. Google Ads uses a slightly different structure. Once AI Max is enabled at the campaign level, Search Term Matching is activated globally by default. Advertisers must manually disable it at the individual ad group level if desired. Furthermore, Microsoft manages AI Max primarily at the campaign level, preserving standard targeting controls (geography, ad schedules, time zones) at the ad group level without dedicated AI Max settings. Conversely, Google allows advertisers to fine-tune certain AI Max parameters directly at the ad group level, including: Location of Interest URL Inclusions Brand Inclusions Ultimately, Google offers greater control at the ad group level, while Microsoft centralizes its AI Max configuration at the campaign level. Search Matching Mechanics and Data Transparency Both platforms encourage shifting away from rigid keyword syntax, as strict keyword dependence can restrict reach on complex, conversational, long-tail queries. However, they handle query data and reporting transparency differently: Microsoft Advertising offers unmasked visibility into search queries driving clicks across AI Max, Performance Max, standard Search, and Shopping campaigns. These insights are accessible via dedicated Search term and Search term landing page reports. Google Ads redacts a portion of search queries due to privacy thresholds. As a result, advertisers cannot always review every exact query that triggered an ad impression or assess its precise relevance. To help offset this reduced visibility, Google provides broader negative keyword management and close variant controls. Signals Driving Query Matching Algorithms The operational differences between Google and Microsoft are largely driven by their distinct underlying data ecosystems. Beyond text keywords, both algorithms rely on behavioral, conversion, audience, and contextual signals: Google AI Max Signal Stack: YouTube user activity data Historical user search behavior First-party conversion data Landing page content Secondary keywords within the ad group Audience signals (In-market, Demographics, and First-party data like Customer Match) Microsoft AI Max Signal Stack: LinkedIn B2B profile and network data Historical user search behavior First-party conversion data Landing page content Secondary keywords within the ad group Audience signals (Impression-based remarketing, In-market, Demographics, and First-party data like Customer Match) While the algorithmic logic remains similar, the distinct ecosystem signals reflect each company's native strengths: Google heavily leverages YouTube engagement data, whereas Microsoft taps into its proprietary LinkedIn B2B network insights. How to Use AI Max in New and Existing Account Structures AI Max brings several of Performance Max’s core artificial intelligence capabilities directly into search campaigns. As a result, it is completely logical that advertisers want to test one or all of its features. However, before implementing AI Max, there are critical considerations to address—especially when working with mature, active ad accounts. Here are five key questions to ask before launching AI Max: Can your conversion data be trusted, and is there sufficient volume for the algorithms to learn? Do your landing pages effectively communicate what your brand offers to AI models? Do your existing ad assets strictly align with your brand guidelines? Is Performance Max already running in the account? Does your budget realistically match your campaign targets? Let’s examine each of these in detail. Is Your Conversion Data Reliable? AI Max is fundamentally reliant on conversion-based bidding strategies. Conversion data serves as the primary signal that algorithms use to identify high-value users and optimize performance. If conversion tracking is broken, misconfigured, or yielding insufficient volume, AI Max will struggle to make high-quality decisions regarding search term matching. Volume Benchmark: Aim for a minimum of 15–30 conversions over the last 30 days. Account Maturity: Accounts with over 90 days of stable, high-quality conversion data are significantly better suited for AI Max deployment. New Accounts: Brand-new accounts should first focus on accumulating a solid baseline of conversion data. This establishes the necessary signal history before enabling AI Max and automated bidding strategies. Do Your Landing Pages Help AI Systems Understand Your Business? Your landing page serves as a primary signal source for ad platforms evaluating your offer. Consequently, the content must be clear not only to human visitors but also to machine algorithms. Clear body text, structured layouts, descriptive headings, and comprehensive product descriptions enable the system to better understand what you offer and who your ideal customer is. Image and Video Context: Using descriptive alt-text for visual elements gives algorithms additional context about page content. The Risk of Vague Content: If a page fails to explicitly explain what product or service is offered, its core differentiators, or why a buyer should choose your brand, AI will struggle to leverage that page for creative generation or relevant query matching. Bot Access Restrictions: Completely blocking search crawlers and AI bots from indexing content cuts off vital signal streams. Testing Site Clarity: A quick method to evaluate how well AI Max understands your website is to test building a Performance Max campaign. If the platform’s automatically generated ad assets and text variations diverge significantly from your intended brand messaging, your landing page content likely needs optimization first. Do Your Ad Assets Match Your Brand Style Guide? Both Google and Microsoft utilize existing ad text assets as foundational building blocks to generate new ad combinations. Therefore, existing headlines and descriptions must remain consistent and fully aligned with your brand guidelines. For example, if your brand guide mandates sentence case for headlines, but several legacy ads use Title Case, the algorithms receive conflicting formatting signals. Before activating AI Max, perform a comprehensive audit of your ad copy to review: Writing and capitalization style Framing of core unique selling propositions (USPs) Tone of voice Product and service naming conventions Outdated messaging that no longer reflects current brand positioning The same auditing standards apply to landing page text. What If Performance Max Is Already Running in the Account? Because AI Max integrates Performance Max-style AI mechanisms into standard Search campaigns, implementing it alongside a mature, well-optimized PMax campaign may yield smaller incremental gains. However, this does not render AI Max redundant. It remains highly valuable in scenarios where Performance Max: Is primarily driving Shopping feed traffic Has budget constraints Is not fully capturing Search demand potential Needs to keep specific strategic offers strictly within Search environments While PMax spans multiple ad channels and excels in e-commerce environments, AI Max is specifically designed to enhance Search campaigns. Running AI Max and PMax simultaneously within the same account is neither inherently right nor wrong. The best approach is to evaluate account-level performance holistically to confirm whether the combination generates incremental conversion volume, revenue, or efficiency gains. Does the Budget Match the Set Goals? AI Max requires conversion-driven bidding—Smart Bidding in Google Ads and automated bidding strategies in Microsoft Advertising. A primary cause of poor campaign performance is attempting to achieve too many fragmented goals with an inadequate budget. For instance, a plumbing company should generally avoid bundling routine service inquiries and emergency burst pipe repairs into the same campaign. These offerings differ drastically in price points, urgency, margins, and operational capacity. Budgeting Best Practice: Group products or services into a single campaign only if their value and target cost-per-acquisition (CPA) fall within roughly 20–30% of each other. If the variance is wider, restructure your campaigns: adjust conversion values and target ROAS (tROAS), apply necessary URL exclusions, and ensure the daily budget is sufficient to support all targeted audience segments. Key Takeaways Conceptually, Google AI Max and Microsoft AI Max are built on very similar frameworks. The core differences stem from platform-specific execution mechanics and control settings. Both platforms deliver peak performance when all three AI Max features are used in tandem. However, their management models differ: Google Ads offers granular AI Max controls at the ad group level. Microsoft Advertising centralizes AI Max configurations primarily at the campaign level. Platform Evolution: Both networks continue to update features and settings based on advertiser feedback. Choosing how to structure AI Max across Google and Microsoft depends not only on the features themselves, but also on which platform's structure best supports your team's workflow and control requirements. Read this article in Ukrainian.