The Future is Here: How Generative AI Can Enhance an AdOps Team

Roman Vrublivskyi explores the use of generative AI in the advertising and marketing world.

Image by Tara Winstead

The adoption of generative AI keeps gaining momentum, and the advertising and marketing industry is among the leaders in terms of using it (37%). Moreover, this trend isn’t going to decrease. While in 2022 large-scale generative AI adoption accounted for 23%, it’s expected to double in 2025.

This isn’t surprising since AI allows for freeing up some time and focusing on core tasks that require comprehensive human intervention, along with cutting costs on advertising operations and minimizing errors. In this article, I’d like to clarify how exactly generative AI can help an AdOps team.

Analyzing campaign data

Any type of digital advertising involves a thorough analysis of large amounts of data: user information, past customer interactions, campaign metrics (traffic type, ad format, device OS, geo, etc.), and so on. The collected insights are then used to optimize the performance of current and future campaigns. However, data collection and analysis can be rather time-consuming tasks, and these are exactly where generative AI can provide significant assistance.


Thus, with its help, AdOps professionals can easily generate Excel formulas and identify patterns and trends that can become a basis for data-driven advertising decisions. Moreover, after analyzing the data, generative AI tools can create reports, charts, and graphs, simplifying the data entry process. AI can also be leveraged to organize the data to streamline the analysis which is time saving and convenient for digital professionals.

Troubleshooting and optimization

AdOps experts are responsible for the impeccable performance of advertising campaigns, which involves optimizing their performance and solving the issues that arise. With generative AI, these tasks may also get simpler.

“It’s still crucial to remember that AI tools can also make mistakes, just like humans. Therefore, your prompts should be detailed and clear, while all the outcomes should be checked.”

For instance, in terms of troubleshooting, AI tools can analyze the code pieces to identify bugs. As for optimization, AI can be used for performing competitive research, generating keywords on the basis of existing content, getting suggestions on campaign parameters, refining the content, optimizing market and customer segmentation, predictive forecasting, identifying the most effective bid range for programmatic advertising, etc.


Generating code and content

Content generation has always been among the main tasks of generative tools like ChatGPT and others, but in terms of AdOps, they can have a broader application. For instance, it can be used for generating HTML ad codes and templates. However, it’s essential to remember that code pieces should still be reviewed by someone with sufficient coding skills, while all the requirements should be clearly outlined in the prompt. The more specific your request is, the more accurate and effective outcome you get.

On the basis of the analyzed data, AI can also provide you with more personalized and creative ad copy, video scripts, and other types of content required in advertising. For instance, you can use it to generate several iterations of the same advertisement while personalizing certain elements for every specific audience segment. This way, gaining experience of working with AI tools, digital marketers can drive conversions with less time and effort.

Basically, AI’s abilities in content generation have virtually no limits. AI can be used to create educational content for new employees (e.g., guides on ad quality assurance, ad fraud prevention, etc.), images, short-form videos, music, product descriptions, surveys, and so on. Content translation and voicing are demanded AI services as well.

Personalizing communication

Personalization is the key to effective communication, and it’s not only about ads themselves. AdOps teams often have to communicate with other departments, stakeholders, clients, etc. Leveraging generative AI tools, they can get a deeper understanding of other conversation participants. For instance, AI can help collect some insights about the client’s industry, as well as gather feedbacks of the clients to understand the satisfaction level of the customers and find out the way of the product or service improvements.

Besides, generative tools can create unique content like emails or messages tailored to every specific recipient. Starting from addressing a customer or anyone else by their name and ending with adapting the content to an individual’s preferences and behavior, AI can automate repetitive and time-consuming tasks and deliver more effective outcomes. When needed, it can also be used for A/B testing in email marketing – just request several different versions of the same message.

At the same time, AdOps teams should avoid complete reliance on generative AI. Yes, it’s powerful, but not almighty. Any generated messages should be reviewed and critically adapted to every individual case and business, while any new prompt should be sent within a new chat. This way, a tool won’t add any irrelevant content to its responses.

To sum up

In advertising operations, generative AI has a wide range of applications: analysis, troubleshooting and optimization, personalization, and so on. Such tools can enhance an AdOps team by minimizing the effort required for various time-consuming processes and ensuring more effective campaign results. Moreover, adopting AI is virtually a must in the modern competitive landscape.

However, it’s still crucial to remember that AI tools can also make mistakes, just like humans. Therefore, your prompts should be detailed and clear, while all the outcomes should be checked. By following these simple recommendations and analyzing the performance of the results delivered by generative AI, you will be able to make the most of such tools.

Picture of Roman Vrublivskyi

Roman Vrublivskyi

Roman is the CEO of SmartHub

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