How Can SEO and SEM Collaborate to Adapt to AI Max for Search?

With generative AI fully penetrating the advertising space, Google’s AI Max for Search is transforming how search marketing operates. Similar automated ad products have also emerged on platforms like Meta and TikTok.

Compared to the traditional keyword-centric approach, AI Max for Search relies more on comprehensive learning from user behavior signals, historical conversion data, and creative assets, with AI autonomously deciding when, to whom, and in what combination to display ads. Simply put, manual control is giving way to “signal feeding” and “model learning.”

In this context, teams accustomed to operating SEO and SEM separately are increasingly realizing the need for closer collaboration between the two. Whether optimizing ad conversions through AI or improving organic search visibility, both rely on consistent user intent understanding, high-quality content assets, and clear behavioral data feedback.

What is AI Max for Search? What Changes Does It Bring?

AI Max for Search is Google’s next-generation intelligent search advertising product, with the core philosophy of “letting AI make ad placement decisions.” Unlike traditional search ads that rely on manually setting keywords, match types, and bidding strategies, AI Max operates more like a “fully managed” system. Advertisers no longer dictate every detail of keywords, creatives, or landing pages but instead provide assets, signals, and objectives, leaving the rest to AI.

AI Max introduces three key changes compared to traditional search ads:

AI Max doesn’t just change how ads are placed—it poses a systemic challenge to marketing strategies, team collaboration, and content production models. This is precisely why SEO and SEM must move toward collaboration: only by integrating resources and unifying signals can AI make smarter decisions.

The Collaborative Opportunity for SEO and SEM: A Content-Centric Unified Strategy

In the logic of AI Max for Search, keywords, creative combinations, and URL selections are all managed by the system, leaving advertisers with fewer direct control variables. But one exception remains critical and controllable: content.

Whether for SEO or SEM, the core question is the same: “When users search for this term, what valuable content can you provide?” AI decides what to serve, to whom, and where to land based on its holistic understanding of your website content, ad creatives, and user behavior. Thus, content quality, structure, and consistency become the core levers for true collaboration between SEO and SEM.

Why is unifying content structure so important?

1. Helps AI correctly understand website content intent

   • Unified expressions in titles, paragraphs, and module layouts help AI assess the relevance between page topics and user search intent.
   • Avoids semantic disconnects where SEO pages emphasize informational completeness while SEM landing pages overly compress content.

2. Optimizes the system’s evaluation and recommendation of page value

   • AI uses structural cues (e.g., H-tags, lists, FAQs) to assess page richness and relevance.
   • Unified structures create clearer signal loops between organic and paid paths.

3. Improves content asset reuse and maintenance efficiency

   • Identical information architectures enable quick adaptation across multiple channels.
   • Large-scale content production and iteration become more manageable and efficient.

Suppose you’re a company offering enterprise CRM systems. Your SEO team conducts thorough keyword research and builds well-structured, comprehensive pages around search intents like “customer relationship management software” and “sales automation tools,” with modules including product features, use cases, customer testimonials, FAQs, and CTAs.

Meanwhile, the SEM team runs AI Max for Search ads with independently crafted landing pages. These pages are more “sales-driven”—shorter in structure, more exaggerated in language, stripping out explanatory content and leaving only slogans and sign-up buttons. In the era of manual ad placement, A/B testing could work, but under AI Max, problems arise:

1. AI cannot accurately grasp your core value proposition

   • When matching searches for “CRM system automation,” the system sees SEO pages highlighting “customer data management and sales process integration,” while ad landing pages only say “double your sales.” This semantic inconsistency makes it hard for AI to judge intent alignment, reducing placement precision.

2. The system prefers pages with complete structures and stable signals

   • SEO pages, with FAQs, internal links, and higher dwell times, provide clearer signals for AI to evaluate. Ad pages, with simpler structures and sparse behavioral data, lose priority in URL expansion or placements.

3. Dual maintenance burdens content teams, lowering optimization efficiency

   • Maintaining two inconsistent sets of pages wastes resources and prevents AI models from learning unified user behavior logic from historical data, ultimately hurting conversion performance.

This example shows that in AI Max for Search’s logic, you can no longer guide ad placements through artificial divisions or channel segregation. The system speaks only one language: structured, intent-aligned, and signal-stable content.

Unifying content structure is the prerequisite for AI to “understand who you are.”

How to Unify SEO and SEM Structures?

In AI Max’s placement model, advertisers no longer manually assign landing pages for each ad. Instead, the system dynamically selects the most suitable URL based on search intent, creative performance, user behavior, and other signals. This demands that your website structure itself follows a “unified and interpretable” content logic.

However, different types of advertisers vary in landing page structures, so unification approaches will differ:

Website Pages vs. Dedicated Ad Landing Pages: Structural Unification and Adaptation

URL Exclusion: A Key Tool for Structural Unification

Whether using website pages or dedicated landing pages, URL Exclusion is critical:

• Not all pages should serve as ad landings (e.g., terms of service, newsrooms, help docs may have high bounce rates but low conversion potential).
• Without exclusions, AI might misselect pages based on behavioral data, hurting overall conversions.
• Ensure user path interpretability and attribution clarity to avoid mixing organic traffic pages into ad paths.

If your site’s structure, intent layering, or attribution logic isn’t clear, start small with AI Max by limiting eligible pages, then gradually optimize and expand based on system behavior.

Should You Abandon Dedicated Landing Pages and Use Only Website Pages?

Many teams ponder this before adopting AI Max for Search: if the system favors clear, intent-aligned content pages, should they **ditch traditional landing pages and optimize their website for ad承接?

The recommendation: Don’t abandon landing pages, but “website-ize” them—align their structure, semantics, and behavior with your main site.

Why?

• Landing pages still excel in design freedom and conversion efficiency, especially for promotions, launches, and high-tempo campaigns.
• But AI no longer just “executes settings”—it picks intent-matching, data-rich pages from your entire site. Disjointed content is noise.
• Future ad landing pages must structurally resemble your website, conversion-focused, yet technically recognizable as part of your trusted content network.

Implementation tips:

• Adopt unified page templates (e.g., product highlights → user value → case studies → FAQs → CTA) across website and ads.
• Integrate top-performing landing pages into main navigation or sitemaps for structural cohesion.
• Establish cross-team (SEO/SEM) content workflows: produce once, deploy everywhere, boosting efficiency and AI recognition.

Conclusion: SEO and SEM—No Longer “Two Paths to One Goal,” but One Path to Go Further

AI Max for Search marks a new era where search ads revolve around “signals and semantics.” Here, the lines between SEO and SEM blur. Content structure, user paths, and behavioral signals no longer serve “organic rankings” or “paid placements” separately but collectively shape an AI-driven decision system.

Unifying structures isn’t just about collaboration—it’s about helping AI “understand” your business, users, and value proposition.

The future of high-performance search marketing isn’t two teams, two content sets, or two messaging styles. It’s one unified, well-structured content asset pool, flexibly deployed across traffic strategies.