A recent survey of the digital advertising industry shows that budget allocation for automation and machine learning has reached its peak , with organizations best prepared to scale AI allocating an average of 21,3% of their marketing budget to these initiatives . What was once treated as a trend of structured testing is now the operational core of any online sales strategy . At the heart of this movement is Google Ads , which has undergone profound transformations in how advertisers buy media, create ads, and measure results.
Online retail demands a rapid response to changing consumer behavior, which now involves more complex, multi-channel searches, often mediated by artificial intelligence assistants. To keep pace with this scenario, Google's platform has shifted the burden of work: the focus is moving away from micromanaging bids and keywords and towards the quality of business data provided to the tool.
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GOOGLE ADS IN 2026: FOCUS ON AI
- What has changed in ad creation? The artificial intelligence integrated into the platform now generates variations of text, images, and videos in real time, testing combinations to deliver the highest-converting format for each specific user.
- How does segmentation work without third-party cookies? The platform has come to rely primarily on first-party data (first-party data) of online stores and predictive modeling to identify behavioral patterns and find qualified buyers.
- Have traditional research campaigns come to an end? No, the search network remains active, but it operates in conjunction with highly automated campaigns that expand reach to YouTube, Discover, and Gmail simultaneously.
- What is the role of performance agencies now? The technical bidding operation was replaced by business intelligenceThe manager's focus is on structuring the data architecture, aligning campaign goals with actual profit margins, and refining creative assets.
The Evolution of Artificial Intelligence in Campaigns
The changes to Google Ads in 2026 aren't just about new buttons on the interface. The platform's architecture has been restructured so that language models and computer vision can make split-second decisions during auctions.
Generating dynamic assets and creatives
The integration of advanced AI models directly into the advertiser dashboard has altered the campaign production workflow . Instead of uploading dozens of static banners, marketers provide brand guidelines, product catalogs, and base images. From this material, Google Ads builds and adapts ads for different sizes and platforms, adjusting the tone of voice and visual appeal according to the browsing history of the person who will see the ad.
This requires brands to invest more in the quality of the original material. Artificial intelligence scales production, but the effectiveness of the campaign depends on high-resolution images, rich product descriptions, and videos that capture attention in the first few seconds.
Refined Performance Max (PMax) campaigns
Performance Max campaigns have become the cornerstone of e-commerce strategy. While previous years faced resistance due to a lack of visibility into where ads appeared, updates have provided greater brand exclusion controls and insight reports based on search terms.
PMax in 2026 functions as an engine that optimizes Google Merchant Center product inventory across the entire Google ecosystem. The difference is that advertisers now utilize much more precise audience signals , feeding the AI with high-value customer lists and offline conversion data to target the algorithm only to users with real purchase potential.
Demand Gen and the focus on visual demand
While PMax has a strong inclination towards bottom-of-funnel conversion, Demand Gen campaigns have taken on the role of building purchase desire in an immersive way. Utilizing spaces like YouTube Shorts and Google Discover, this format essentially relies on short videos and impactful images. AI plays a role here by mapping lookalike audiences based on the store's best customers, delivering ads to people who weren't actively searching for the product but whose consumer behavior aligns with the offer.
The Impact of Privacy and the Value of FIRST-PARTY DATA
Google Ads ' technological transition is directly linked to global privacy regulations and the obsolescence of third-party cookies . The ability to track an individual user across the internet has been replaced by technologies focused on preserving identity.
In this scenario, first-party data has become the main fuel for successful campaigns. Online stores that effectively structure the collection of emails, phone numbers, and purchase history from their customers have a clear competitive advantage . By uploading this encrypted information to Google Ads , the store teaches artificial intelligence about the exact profile of its ideal buyer.
To address tracking gaps, Google has improved conversion modeling. When a user does not consent to the use of cookies, AI analyzes aggregated signals (such as device type, time, and browser) and cross-references them with historical data to estimate, with high accuracy, whether a conversion occurred and which ad it should be attributed to.
SUCCESS METRICS: WHAT TO ANALYZE NOW
The way campaigns are evaluated has evolved along with the tools available. Vanity metrics or analyses purely based on click volume have given way to financial indicators.
- POAS (Profit on Ad Spend): o ROAS (Return on Advertising Spend) Focusing solely on revenue often masks losses on products with low margins. POAS directly cross-references the cost of Google Ads with the gross profit of the item sold., guiding the AI to seek sales that actually generate revenue for the company.
- Customer Acquisition Cost (CAC): with the feature of acquiring new customers consolidated in smart campaigns, It is possible to instruct the algorithm to pay more in the auction only if the user has never purchased from the store before., separating the retention cost from the base expansion cost.
- LTV (Lifetime ValuePredictive: o Google Ads It evaluates browsing behavior to predict which users are most likely to make repeat purchases over the months, automatically adjusting bids to capture these profiles.
A CHANGE IN STRATEGIC OPERATIONS
| Traditional Google Ads Management | Management with AI (Current Scenario) |
| Manually adjust bids by keyword and device. | Defining ROAS/CPA targets based on profit margin. |
| Segmentation based on generic interests and third-party data. | Segmentation guided by customer lists (First-Party Data) |
| Manually creating dozens of text and banner variations. | Supply of high-quality assets for dynamic assembly. |
| Focus restricted to conversion volume and cost per click. | Focus on the actual profit of the operation (POAS) and repurchase forecast. |
For those working in e-commerce, the daily task requires monitoring how the product catalog is structured (XML feed), ensuring that tracking tags on the server function perfectly, and evaluating which product categories should receive greater budget pressure based on inventory and seasonality.
If your online store is still running campaigns based on outdated micromanagement structures, your budget may be being consumed by inefficient algorithms. With over 13 years of experience in e-commerce and technology, the e-Plus team understands exactly how to translate your business data so that artificial intelligence works in favor of your profit margin. Learn about e-Plus' paid traffic services and talk to our experts about the next step in your digital strategy.
FAQ – Frequently Asked Questions about Google Ads in 2026
How does Performance Max use artificial intelligence?
The campaign analyzes real-time data to decide where, when, and to whom to display ads from your catalog. It cross-references search intent signals, browsing history, and purchase behavior to deliver the exact combination of text and image that generates conversions.
Is it still worth focusing on keywords in Google Ads?
The search network still accounts for a large part of purchase intent. However, the focus has shifted from exact matches of isolated words to understanding the context and intent behind the phrase the user typed, thanks to natural language processing.
What is needed for Google Ads AI to function properly?
The algorithm needs a large volume of qualified data and learning time. For the automation to make good bidding decisions, it is essential to have conversion tags correctly configured with the actual purchase values and to provide up-to-date lists of the store's customers.
How to integrate first-party data into campaigns?
E-commerce platforms and CRMs should be connected to Google Ads to send encrypted information about who buys from the store. This allows artificial intelligence to find patterns and search for audiences with similar behavior on the internet.
