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15:15, 11 January 2026
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In Russia, Ad Labeling Shifts Into a “Smart” Mode With AI Prompts

As previously reported, ad labeling in Russia gradually stopped being passive, box-ticking reporting in 2025. Labeling and reporting services began assisting users at the stage of preparing and publishing ad content.

Photo: iStock

The market is moving — or has already moved — toward a model where an online service does more than simply accept data. It prompts, checks, and validates it. The next step in this direction is embedding AI directly into the workflow. This approach is seen as a logical way to reduce errors and speed up work for teams managing large volumes of advertising.

“Labeling should no longer be the final barrier before a campaign goes live. It should work at the moment ad content is created and published,” explains Kristina Markina, head of the SmartErid project.

New Year, New Tasks

At SmartErid, the introduction of an AI layer was scheduled for 2026, with a strictly practical focus. The first function is automatic identification of the ad format. Banners, articles, videos, and landing pages all require different attributes, and mistakes at this stage often trigger a chain of revisions and repeated data submissions. The second function provides prompts for filling in required attributes. The system analyzes text and visuals and suggests an appropriate category. This reduces manual work and lowers the number of corrections.

“Most problems arise not from bad intent, but from incorrect classification of ad content. If you remove this risk point, everything else tends to work smoothly,” Markina notes.

Statistics From the Moment of Publication

At the same time, the market is moving toward labeling directly within CMS platforms. In this scenario, a token is assigned and statistics begin to be collected at the moment content is published. Users no longer need to open separate interfaces or prepare data exports. They publish ad content as usual, while the technical side runs in the background through integrations and APIs.

“The ideal process is when a person doesn’t think about labeling as a separate task at all. They just work with content,” Markina says.

AI-Based Auditing

Another new direction set to launch this year is AI-based auditing of advertising campaigns. The system will proactively flag issues, missing attributes, incorrect regulatory references, and format mismatches. This will reduce the risk of fines and ease the burden on teams responsible for compliance.

Data Accumulation and Analysis

An additional effect will come from the accumulation of data within the Unified Internet Advertising Registry. Based on this data, the market gains a more accurate picture of placement volumes and format dynamics. Once again, a formal administrative obligation is turning into a tool that helps businesses operate faster and with greater confidence.

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In Russia, Ad Labeling Shifts Into a “Smart” Mode With AI Prompts | IT Russia