Everyone should know how AI transforms Google Ads

The advertising landscape is undergoing a seismic shift as AI transforms how businesses manage and optimize their Google Ads campaigns. Recent case studies, including those of Ex Libris and Sephora UK, vividly illustrate the substantial advantages of shifting from manual and segmented account structures to simplified, AI-driven frameworks. With 15% of daily Google Ads searches being entirely new queries, the traditional approach of granular campaign control is no longer sustainable. Instead, marketers must embrace AI's ability to interpret data at scale and automate optimizations across channels. In this article, explores how companies can restructure their Google Ads accounts to improve AI efficiency through real-world success stories and practical strategies.

1. The Shift from Manual to AI-Driven Campaign Management

The transition from manual campaign management to AI-driven optimization represents a fundamental evolution in digital advertising. Traditional approaches, which relied on meticulous segmentation and manual keyword matching, are increasingly being rendered obsolete by the scalability and precision of AI-powered tools. Platforms like Google Ads now leverage advanced machine learning algorithms through solutions such as Performance Max (PMax) and Smart Bidding, enabling advertisers to automate critical functions—from bid adjustments to cross-channel audience targeting—across Search, Display, YouTube, Gmail, and Discover.

For enterprises, this transformation is not just a technological enhancement; rather, it represents a crucial strategic necessity. Consider the case of Ex Libris, which replaced its Dynamic Search Ads (DSA) with PMax, achieving an 8% reduction in cost per acquisition (CPA) while improving return on ad spend (ROAS). Compared to Dynamic Search Ads (DSA), which operate only on Google Ads search, Performance Max (PMax) uses artificial intelligence to optimize ad delivery across channels, allowing campaigns to be aligned with broader marketing goals. This highlights a key understanding: AI performs best with integrated data and cohesive structures, rather than with disjointed campaigns. Excessive segmentation, once a hallmark of manual management, now limits AI's ability to identify patterns and drive performance at scale.

Topkee's expertise in Google Ads aligns seamlessly with this paradigm shift. By integrating AI-driven tools like PMax into client campaigns, Topkee enables businesses to unlock efficiencies that manual methods cannot match. For instance, Smart Bidding—a core component of Topkee's service offerings—automates bid strategies based on real-time data, adjusting for factors such as device type, location, and time of day to maximize conversions or ROAS. This eliminates the guesswork and labor-intensive adjustments inherent in manual bidding, freeing marketers to focus on strategic initiatives like creative development and audience insights.

Moreover, Topkee's approach emphasizes the importance of data quality and structure in AI optimization. The company's proprietary TTO framework ensures clean conversion tracking, a prerequisite for effective Smart Bidding. By automating event tracking and synchronizing data with Google Ads, TTO provides the robust datasets AI needs to make accurate optimizations. Similarly, Topkee's keyword research and thematic grouping services help clients consolidate campaigns into coherent structures, enhancing AI's ability to detect trends and allocate budgets efficiently.

The operational benefits of this transition are profound. AI-driven campaigns reduce reliance on manual interventions, minimizing human error and enabling real-time adjustments. For example, PMax dynamically allocates budgets to high-performing channels and creatives, a task that would require constant oversight in a manual setup. This automation not only improves performance but also reduces operational overhead, allowing teams to reallocate resources toward higher-value activities like creative testing and market analysis.

However, adopting AI-driven management requires a mindset shift. Advertisers must move away from rigid control and embrace AI's iterative learning process. This involves trusting algorithms to test broad-match keywords, explore new audiences, and experiment with ad placements—all while maintaining alignment with business goals. Topkee's advisory services guide clients through this transition, ensuring they strike the right balance between automation and strategic oversight.

In short, the move to AI-driven campaign management is rewriting the course of Google Ads. By leveraging tools like PMax and Smart Bidding—businesses can achieve superior performance, operational efficiency, and scalability. The era of manual micromanagement is giving way to a new paradigm where AI handles the heavy lifting, empowering marketers to focus on innovation and growth.

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2. Case Studies: Success with Simplified Structures

The transition to AI-optimized Google Ads structures has delivered measurable success for businesses across industries, demonstrating the tangible benefits of simplification and automation. Two prominent examples—Ex Libris and Sephora UK—highlight how consolidating fragmented campaigns and leveraging AI-driven tools like Performance Max (PMax) can unlock superior performance with reduced operational overhead. These cases underscore a critical insight: excessive segmentation, once considered a best practice in manual campaign management, now hinders AI's ability to identify patterns and optimize at scale.

Ex Libris, a leading Swiss online bookseller, faced the dual challenge of managing an extensive product catalog (spanning books, music, films, and games) while competing in a shrinking-margin market. Initially, the company relied on Dynamic Search Ads (DSA) to automate keyword matching for its diverse inventory. While DSA proved effective in driving search traffic, its limitations—operating exclusively within Google Ads Search and requiring manual oversight for cross-channel coordination—prompted the team to explore PMax. By consolidating five DSA campaigns into a single PMax framework, Ex Libris achieved an 8% reduction in cost per acquisition (CPA) and improved return on ad spend (ROAS). PMax's cross-channel automation enabled the AI to dynamically allocate budgets to high-performing placements across Search, Display, YouTube, and Discover, a task impractical to replicate manually. Notably, the shift freed the marketing team to focus on strategic initiatives like creative optimization and audience expansion, rather than granular bid adjustments. Post-implementation, Ex Libris expanded PMax to its entire search portfolio, further streamlining operations and paving the way for scalable growth.

Similarly, Sephora UK has demonstrated the powerful benefits of simplification through its restructuring. The beauty retailer reduced its campaign count by 85% by merging low-volume segments into broader thematic groups (e.g., combining "luxury skincare" and "affordable skincare" under a unified "skincare" campaign). This consolidation provided clearer signals for AI to optimize bids and creatives, resulting in a 42% higher conversion rate and 13% improvement in ROAS. The success stemmed from aligning the account structure with AI's need for coherent data patterns—overly narrow segments had previously fragmented performance signals, limiting the algorithm's ability to detect trends. Sephora's approach also highlights the operational efficiency gains: fewer campaigns meant less time spent on manual optimizations and reporting, allowing the team to redirect resources toward creative testing and market analysis.

These cases reveal three universal lessons for marketers adopting AI-driven structures:

Consolidation Enhances AI Efficiency: Both Ex Libris and Sephora achieved breakthroughs by replacing siloed campaigns with unified, goal-aligned structures. For AI to thrive, campaigns must be grouped thematically (e.g., by product category or funnel stage) rather than by hyper-specific keywords or demographics.
Cross-Channel Automation Unlocks Scale: PMax's ability to optimize across multiple channels simultaneously—a capability absent in manual or single-channel tools like DSA—was pivotal in driving efficiency. Advertisers should prioritize solutions that break down channel silos to maximize reach and budget flexibility.
Strategic Oversight Complements Automation: While AI handles tactical optimizations, human expertise remains essential for setting business goals, refining creatives, and interpreting insights. Ex Libris' iterative testing of PMax creatives and Sephora's thematic regrouping exemplify this balance.

The successes of Ex Libris and Sephora UK also challenge lingering misconceptions about AI-driven advertising. Some advertisers fear losing control over granular targeting, but these cases prove that strategic simplification—coupled with rigorous performance monitoring—can yield better results than micromanagement. For businesses hesitant to transition, a phased approach (e.g., testing PMax with a subset of budgets) can mitigate risk while demonstrating ROI.

Looking ahead, the trajectory is clear: AI-optimized account structures are no longer a nice-to-have for competitive performance marketing. By learning from these pioneers, Topkee is enabling businesses to fully leverage AI’s potential to deliver greater efficiency, scalability, and insights in an increasingly automated environment. The key is to view simplification as a strategic advantage, not a compromise.

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3. Core Principles for AI-Optimized Account Structures

To effectively leverage AI, marketers must prioritize data consolidation and thematic grouping. For example, a florist groups "red roses" and "pink roses" into a single "roses" campaign, which provides clearer signals for AI to optimize bids and creatives. Ad strength metrics serve as a feedback mechanism; scores below "good" indicate that the structure is too vague or fragmented. With professional experience, Topkee is able to quickly identify such issues and assist companies in making adjustments. In addition, smart bidding requires clear conversion data that is aligned with value-based goals. Topkee demonstrates exceptional skill in combining and centralizing data, thus maintaining the precision and consistency of conversion data. By reducing unnecessary segmentation, with Topkee's professional guidance, companies can enhance AI's learning ability to create more accurate and efficient Google Ads campaigns.

4. Practical Implementation Steps

Restructuring for AI begins with organizing campaigns by priority and themes. For instance, a retailer might separate "brand" and "non-brand" campaigns while grouping products into broad categories (e.g., "electronics" or "apparel"). Smart Bidding and Broad Match keywords further amplify AI's reach, allowing algorithms to test diverse queries and audiences. Marketers should also monitor ad strength and conversion data closely, using insights to refine themes over time. Sky's adoption of Enhanced Conversions—a privacy-compliant tracking solution—demonstrates the importance of data quality, recovering 12% more Search and 21% more YouTube conversions previously lost to measurement gaps.

5. Benefits of Simplified Structures

Streamlined Google Ads accounts yield three key advantages: better performance, operational efficiency, and deeper insights. AI-optimized campaigns, like those of Sephora, achieve higher conversion rates with less manual tuning. Teams save time on granular adjustments, redirecting efforts toward strategic planning. Thematic structures also simplify performance analysis; for example, a consolidated "roses" campaign makes it easier to assess category-wide trends versus isolated SKUs. As AI continues to evolve, businesses that simplify now will gain a competitive edge in scalability and agility.

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6. Future Trends in AI-Driven Advertising

AI's role will expand into full-funnel strategies, as seen with YouTube's Demand Gen campaigns, which combine brand awareness and performance goals. Generative AI tools like Gemini are already assisting creators in content ideation and production, while predictive analytics will enable long-term ROI measurement. For example, Samsung's AI-optimized Galaxy S24 launch drove a 5.5% lift in purchase intent, showcasing the potential of automated, multi-channel Google Ads campaigns.

7. Strategic Recommendations for Marketers

To capitalize on AI, marketers should:

Test-and-learn: Pilot PMax or Demand Gen campaigns with a portion of budgets.
Balance budgets: Allocate 50–60% to brand-building (upper funnel) and 40–50% to performance (lower funnel), as Domino's did to achieve 45% higher YouTube ROI.
Collaborate with creators: Partner with influencers for authentic engagement, as G-Shock did to generate 10M+ impressions.

Conclusion

The future of Google Ads lies in AI-optimized account structures that prioritize simplicity, data quality, and cross-channel automation. By learning from pioneers like Ex Libris and Sephora, businesses can unlock higher efficiency, performance, and insights. Start simplifying your campaigns today—and if needed, consult with experts to navigate the transition seamlessly.