Google presents AI Max framework during live stream

Google hosted a live stream presentation on the AI evolution of search, where Toluse Akinlabi, a retail account manager for Sub-Saharan Africa at Google, outlined the company's AI Max feature suite for search campaigns. The presentation covered technical implementation details, performance metrics, and strategic recommendations for advertisers adopting artificial intelligence-powered automation.

The presentation centered on how artificial intelligence transforms search functionality and user behavior patterns. "AI is giving Google search brand new superpowers," Aken Labi stated during the broadcast. "It's fundamentally changing how search works and how people search for new information and try to match with new brands and services out there."

According to the presentation, AI overviews reaches more than two billion users across more than 200 countries every month. Users demonstrate increased satisfaction when receiving answers through AI overviews compared to traditional search results, driving longer and more natural search patterns that require different approaches to relevance matching.

Technical architecture and implementation

AI Max operates through two primary components: expanded targeting and asset optimization. The expanded targeting system utilizes search term matching to extend keyword reach through broad match expansion, keywordless technology similar to Dynamic Search Ads, and asset-based targeting mechanisms.

Asset optimization functions through text customization, which modifies ad asset relevancy to match specific user queries, and final URL expansion, which directs users to optimal landing pages based on search intent rather than advertiser-specified destinations. "What we'll do is look at your provided headlines and descriptions," Aken Labi explained. "And then we'll draw on information we find on your landing pages and then use generative AI in combination with all of this to have better personalization for the actual ads that we serve to your users."

The system appears in the Google Ads interface under search campaign settings, where advertisers can activate the feature through a simple toggle labeled "optimize your search campaigns with AI Max." Users can independently control text customization and final URL expansion, though Google recommends enabling both features for optimal performance.

Search term matching expands campaign reach beyond specified queries by analyzing overall intent signals. When an advertiser includes a broad match keyword for "burgers," the system can match related queries like "skincare for dry sensitive skin" to a "moisturizer" keyword based on landing page information and other asset signals, even when those specific terms don't appear in campaign settings.

The automated asset creation process generates headlines and descriptions using generative artificial intelligence combined with advertiser-provided content and landing page information. This approach delivers what Aken Labi described as "hyper personalization" that improves ad relevancy and overall campaign performance beyond static advertisement variations.

Final URL optimization analyzes landing pages across advertiser websites to identify the most appropriate destination for each query. While advertisers might direct all traffic to a general product category page, the system might determine that specific queries warrant landing on individual product pages instead, improving user experience and conversion potential.

Performance claims and advertiser results

According to the presentation, advertisers using AI Max in search campaigns typically experience 14 percent more conversions at similar cost per acquisition or return on advertising spend metrics. For advertisers primarily using exact match and phrase match keywords, the typical performance increase reaches approximately 30 percent.

The presentation featured a case study from L'Oreal Paris demonstrating significant performance improvements. According to Aken Labi, AI Max-specific keywords brought 67 percent more click-through than other match types, reduced cost per conversion by 30 percent, and doubled conversion rates compared to campaigns without AI Max activation.

However, these performance claims contrast with independent testing results documented by advertising professionals. Early analysis suggested the majority of AI Max-generated impressions failed to produce conversions, with significant discrepancies between Google's internal studies and real-world campaign performance.

Control mechanisms and transparency features

The presentation addressed advertiser concerns about transparency and control in automated systems. AI Max supports negative keyword lists, URL controls for excluding specific pages from final URL expansion, and brand and location controls at the ad group level.

Advertisers can remove individual assets that don't align with brand guidelines or product positioning. The system will introduce asset exclusions capabilities, and users can provide specific headlines and descriptions for advertisement delivery. Asset pinning remains available for advertisers requiring exact wording, though this functionality requires disabling final URL expansion.

Reporting capabilities include performance comparisons showing incremental benefits from AI Max activation. Asset reports enable advertisers to identify high-performing headlines and descriptions for addition to base campaigns while removing assets that don't align with brand or product requirements.

Landing page reports facilitate website optimization and URL exclusion management. Keyword reports display performance metrics for AI Max-generated keywords, enabling advertisers to add successful terms directly to campaigns rather than relying exclusively on automated matching. Search term reports allow identification and exclusion of irrelevant queries through negative keyword implementation.

The keywords tab displays total AI Max expanded searches and total AI Max landing page matches at the bottom of keyword lists, enabling direct comparison with non-AI Max performance across metrics including click-through rates, clicks, and impressions.

Search terms reports under insights and reports can be filtered by keyword source, distinguishing between AI Max-generated terms and advertiser-provided keywords. The assets tab shows expanded final URL assets created through text customization, while the landing pages tab under insights and reports displays specific pages selected either automatically or by advertisers.

Implementation recommendations and best practices

The presentation outlined specific prerequisites for successful AI Max deployment. Measurement configuration should align search campaigns with conversion strategies based on business objectives, utilizing features like enhanced conversions, data-driven attribution, and minimizing conversion delay timing.

Bid strategy configuration must match business goals, and campaigns should not be limited by budget constraints before AI Max activation. "If your search campaigns already limited by budget, when you add AI Max, which is essentially looking for more ways to drive conversion volumes, it's not really going to be able to find new users because you're very limited by budgets," Aken Labi noted.

Campaign structure recommendations emphasize simplicity, with segmentation based exclusively on business goals. Ad groups should avoid duplicate keywords with different match types, consolidate to achieve at least 30 conversions monthly, and maintain tight thematic focus. Fashion retailers, for example, should structure ad groups around product categories like dresses and shoes rather than broader organizational schemes.

Landing page quality requires ongoing attention. Websites must remain current and comply with Google policies, with regular reviews using landing page reports to identify underperforming destinations for exclusion or optimization. Copy and call-to-action elements should support automatic asset generation through relevant and useful content.

Experimentation framework

For advertisers testing AI Max before full deployment, the presentation recommended a structured experimental approach. Campaigns selected for testing should not be budget-limited, should use appropriate bid strategies, and should avoid periods of unusual seasonality that could confound results.

The testing methodology follows a pre-post structure comparing four-week periods, with campaigns running AI Max for approximately four weeks after a one-to-two-week learning period. Performance evaluation should examine return on advertising spend, cost per acquisition, and conversion volume against periods without AI Max activation.

Experiments should avoid newly created campaigns, portfolio bidding strategies, shared budgets, and seasonal periods like Black Friday for retailers. Brand campaigns may require separate consideration due to strict budget requirements and distinct performance characteristics. Generic campaigns often provide more suitable testing environments for AI Max evaluation.

Testing guidelines specify an eight-week total period, including one to two weeks for learning, four weeks of active experimentation, and post-period analysis excluding the learning phase. This structure enables meaningful performance comparison while accounting for system optimization time.

Strategic implications for campaign management

The presentation positioned AI Max as transforming the relationship between campaign managers and advertising automation. According to Aken Labi, the system reduces time spent on campaign restructuring, keyword planning, and advertisement copy generation, enabling focus on strategic oversight rather than tactical execution.

The workflow shift moves advertisers from continuous campaign restructuring and individual keyword optimization toward simplified account structures. Keywordless targeting reduces the burden of comprehensive keyword list maintenance, while performance evaluation shifts from keyword-level metrics to total performance against targets.

Advertisement copy management becomes automated to individual user queries rather than requiring manual relevancy reviews for responsive search advertisements. Dynamic landing page selection replaces engineered relevance through advertisement pinning, with exclusions maintaining brand safety and alignment with organizational goals.

"As a human, your role is going to be really important to fuel the AI with high-quality data so that the AI can help you find new customers and help you better optimize towards your goals," Aken Labi stated. "You'll still be in control, you'll be able to optimize but a lot of the manual work in terms of bid settings and actually just finding the customers can be automated and done in a much more relevant way using AI Max."

The presentation emphasized that human expertise remains essential for providing high-quality data inputs that enable artificial intelligence to identify customers and optimize toward business objectives. While automation handles bid management and customer identification, strategic oversight and quality control continue requiring human judgment and intervention.

Industry context and adoption challenges

Google announced AI Max for Search campaigns on May 6, 2025, promising performance improvements through automated targeting and creative optimization. The feature gained API support through Google Ads API version 21 on August 6, 2025, enabling programmatic campaign management and reporting integration.

Google positioned AI Max as part of its comprehensive Power Pack strategy announced on September 16, 2025, combining AI Max for Search, Performance Max, and Demand Gen campaigns for optimized automated advertising across multiple channels. The Power Pack offers advertisers two strategic approaches: channel control through AI Max and Demand Gen combinations, or maximum reach through deployment across all three campaign types simultaneously.

Industry professionals have raised concerns about AI Max implementation, particularly regarding aggressive Search Partner Network expansion. Analysis from marketing professionals suggested that AI Max demonstrates concerning patterns on Search Partner placements, generating disproportionate impression volumes compared to traditional match types while delivering questionable conversion performance.

Google introduced additional control mechanisms through text guidelines functionality on September 10, 2025, enabling advertisers to specify term exclusions and messaging restrictions using natural language instructions. These controls address brand safety concerns within automated advertising systems, though implementation requires text customization activation.

New reporting metrics rolled out for Search AI Max campaigns, adding "AI Max expanded matches" and "AI Max expanded landing pages" metrics at the all keywords level. These reporting enhancements provide deeper insight into how artificial intelligence expands campaign reach beyond traditional keyword targeting, addressing transparency concerns from marketing professionals.

The broader advertising industry continues evaluating AI Max adoption amid questions about performance outcomes and control mechanisms. Google Ads Editor version 2.10, released on July 8, 2025, incorporated AI Max functionality alongside enhanced Performance Max controls, demonstrating Google's systematic integration of automation features across campaign management infrastructure.

For marketing professionals, these developments represent significant shifts in search advertising strategy and execution. The increasing emphasis on artificial intelligence-driven automation requires balancing efficiency gains against control requirements, particularly for advertisers with specific brand guidelines or performance targets that may not align with algorithmic optimization approaches.

The presentation from Aken Labi provides Google's perspective on AI Max capabilities and implementation best practices, though actual performance outcomes will depend on individual advertiser circumstances, campaign configurations, and market conditions. Organizations considering AI Max adoption should conduct controlled testing following Google's experimental framework while monitoring performance against business objectives and historical benchmarks.

Timeline

• May 6, 2025: Google announces AI Max for Search campaigns with promises of 14% conversion improvements
• May 30, 2025: Channel performance reporting begins appearing in Performance Max accounts
• July 2, 2025: Google rolls out AI Max match type reporting
• July 8, 2025: Google Ads Editor 2.10 released with AI Max functionality
• July 14, 2025: Brand guidelines rollout completed for all new Performance Max campaigns
• August 6, 2025: Google Ads API v21 introduces AI Max support and campaign transparency tools
• August 17, 2025: Industry testing reveals mixed AI Max results contradicting Google's performance claims
• August 29, 2025: Google Ads launches "Ads Decoded" podcast series featuring AI Max discussions
• September 9, 2025: Google introduces new AI Max campaign reporting metrics for expanded matches and landing pages
• September 10, 2025: Text guidelines functionality announced for Performance Max and AI Max campaigns
• September 14, 2025: Google unveils comprehensive AI advertising suite at Think Week 2025
• September 16, 2025: Google announces Power Pack strategy combining AI Max, Performance Max, and Demand Gen
• October 2025: Google hosts live stream presentation with Toluse Akinlabi explaining AI Max implementation details

Summary

Who: Toluse Akinlabi, a retail account manager for subsaharan Africa at Google, presented the technical details and strategic framework for AI Max. Google developed the feature suite for advertisers seeking automated optimization across search campaigns, while marketing professionals and advertising agencies represent the primary audience for implementation guidance.

What: AI Max constitutes a suite of artificial intelligence-powered features for standard search campaigns designed to increase conversions and reach through expanded targeting and asset optimization. The system includes search term matching for keyword expansion, text customization using generative artificial intelligence for advertisement personalization, and final URL expansion directing users to optimal landing pages based on query intent rather than advertiser specifications.

When: The live stream presentation occurred in October 2025, following the initial AI Max announcement on May 6, 2025. Google released API support on August 6, 2025, and integrated the functionality across multiple platform updates throughout summer and fall 2025, including Google Ads Editor version 2.10 on July 8, 2025, and various reporting enhancements through September 2025.

Where: The presentation reached audiences through Google's "Accelerate with Google" live stream platform. AI Max functions within Google Ads search campaigns globally, with implementation available through the web interface, Google Ads Editor desktop application, and Google Ads API for programmatic management. The system serves advertisements across Google Search properties and Search Partner Network placements.

Why: Google positioned AI Max as addressing fundamental changes in search behavior, where users increasingly employ longer, more complex queries through features like circle to search, Google lens, image search, and voice search. The company claims these behavioral shifts require automated systems capable of matching advertisements with user intent at scales beyond manual keyword management. AI Max aims to solve what Google describes as the challenge of meeting "the scale of human curiosity" through artificial intelligence-powered relevance matching, though industry testing has revealed questions about actual performance outcomes compared to Google's internal studies.