Google Ads Adds AI Dashboards and In-Store Sales Tools

Google Ads has started showing AI-generated dashboards inside some advertiser accounts, according to Search Engine Land, which reported on September 8, 2026, that the feature lets advertisers type plain-text prompts to generate charts and narrative summaries of campaign performance. Google announced the capability in August, and Search Engine Land reported it is now appearing in accounts following that announcement.

The same Search Engine Land report described the dashboards as including an AI-generated summary meant to explain what changed in performance, in addition to the visualization itself. Google has also added AI-powered insights elsewhere in Google Ads, including on the account homepage and through an in-product assistant called Ask Advisor, per Search Engine Land.

What the in-store sales update adds

In a separate report published the same day, Search Engine Land said Google Ads introduced two features for multi-location businesses: Local Customer Optimization, a toggle for Performance Max store-goals campaigns that prioritizes budget toward nearby in-market users across Maps, Waze, and local Search, and a new option to connect CRM or Google Sheets data to Google Ads through Data Manager for measuring in-store sales. Google said Local Customer Optimization is rolling out now and the Data Manager option is expected in the coming weeks, according to Search Engine Land.

Separate reporting on attribution, llms.txt, and ChatGPT Ads

These items are separate developments reported around the same period, not parts of one story. Search Engine Journal published an analysis by Bengu Sarica Dincer arguing that modeled and estimated data increasingly fills gaps left by signal loss in analytics platforms, and that treating modeled figures as equivalent to directly observed measurements can lead to misallocated budgets. The piece recommends labeling modeled or estimated metrics as directional rather than definitive and comparing results across more than one attribution model.

Separately, Search Engine Journal reported that Common Crawl reviewed over 500,000 llms.txt files from a recent crawl and found that 68% originated from a plugin or template, and 22.56% contained no links. Common Crawl also found that some files included crawler-access rules the llms.txt format itself cannot enforce, and that 136,578 robots.txt files were located at the llms.txt path. Among 32 files that appeared to deny Common Crawl's own bot, none of the 31 it could check actually blocked that crawler in robots.txt, according to Search Engine Journal.

A third Search Engine Journal report, by Brooke Osmundson, noted that OpenAI has not published performance benchmarks for ChatGPT Ads across advertisers, industries, or campaign types roughly six months after ads began testing. The report cited advertiser-shared cost-per-click figures ranging from about $3 to over $22 depending on market and campaign, and noted the platform lacks an auction-insights-style reporting view.

What to check before relying on these reports

For the AI dashboard feature and the in-store sales tools, Search Engine Land's reporting does not specify how the dashboard summaries or in-store sales data distinguish modeled estimates from directly observed conversions. Advertisers using either feature can confirm that distinction directly with Google. Separately, Search Engine Journal's Common Crawl data indicates that having an llms.txt file in place does not by itself control crawler access, since enforcement depends on the rules set in robots.txt.