How to Create an AI Assistant for Adtech: Complete Guide for AdOps & Advertisers

With the growing complexity and scale of digital advertising, AI agents are gradually becoming essential tools in the AdTech ecosystem. They not only automate and streamline processes but also fundamentally transform how advertisers manage operations and make strategic decisions.

This article explores the opportunities AI assistants bring to AdTech and how they can boost the efficiency of your advertising efforts with the power of AI.

What is an AI Agent and How is It Used in Advertising?

Let’s begin with an AI agent definition.

These intelligent systems can make decisions and take actions autonomously, based on real-time data analysis and pre-defined objectives.

The range of modern AI tools includes solutions of different complexity, from simple customer service chatbots to advanced multi-agent programmatic advertising systems.

How Do AI Agents Work in the Advertising Industry?

Advertising AI agents act as smart decision-makers aimed at optimizing campaigns in real time. This is made possible through the analysis of vast data sets and predictive modeling. Based on the received insights, they can automatically adjust the parameters of ad campaigns to improve their performance.

Here’s a typical workflow of how AI agents function in advertising:

Step 1. Data gathering

AI agents collect and process data from various sources. They typically process data such as user behavior (time on page, clicks, views), campaign metrics, context, and audience segments.

Step 2. Analysis and decision-making

AI agents leverage real-time data to predict user intent, select the most relevant ad creative, optimize timing and format, and fine-tune bids and targeting strategies.

To forecast outcomes, they often utilize machine learning models and predictive algorithms.

Step 3. Execution

When AI agents extract all the insights they need for their work, they can perform the required actions. For instance, they can launch or pause ads, personalize ad creatives, and allocate budgets across platforms based on the ongoing market conditions.

This entire process operates continuously, enabling AI agents to respond faster and more accurately than any human team.

Perks of Using AI Agents in Digital Advertising

To better demonstrate the value of AI agents, let us share the key benefits that you can leverage after its launch.

• Increased campaign efficiency. AI agents continuously analyze ad performance data. Based on the results, they can make real-time decisions regarding required adjustments of bids and targeting. They can also reduce wasted ad spend by pausing underperforming ads or scaling top-performing ones in real time.
• Time savings. AI tools help businesses eliminate a huge volume of manual work. They can successfully automate such repetitive tasks as ad A/B testing, performance analysis, keyword research, etc. Thanks to this, human experts have more time for strategic and creative tasks.
• Well-grounded decisions. AI agents can efficiently analyze massive volumes of data that are far beyond human capacity. This allows AI systems to identify trends, make predictions, suggest highly promising strategic actions, and make decisions based on evidence.
• Personalized ad experiences. AI agents use real-time data to tailor messaging and adjust ad creatives based on individual user behavior. It means that such AI-powered AdTech systems can deliver the right ad to the right person to increase the likelihood of conversion.
• Smarter budget allocation. AI-driven AdTech agents can automatically reallocate ad spend on what works best at the moment. Thanks to this, they maximize ad performance across channels and help companies prevent overspending on underperforming segments.
• Cross-channel optimization. AI-powered systems support cross-channel optimization by managing campaigns across platforms like Google Ads, DSPs, and programmatic exchanges with consistency and efficiency.
• Scalability. The number of ad campaigns that your team can manage manually is quite limited. With an AI agent, you can scale operations without additional manual effort.
• Fraud detection and brand safety. Artificial intelligence can identify undesired behavior, like click fraud, and detect harmful content. If your ad campaign includes placing your ads on websites with a suspicious reputation, an AI agent can block this action to protect your brand integrity.
• Continuous learning. AI agents can learn from historical data to improve future performance. In other words, they get smarter over time and help you achieve better results.

Different Types of AI Agents in AdTech

There are several types of AI agents that are used across domains and advertising is not an exception.

• Rule-based agents. Such solutions follow predefined if-then rules. They can automate simple tasks, but they have limited adaptability and can’t learn from data.
• Reactive agents. These AI assistants are designed to respond to real-time data. Quite often, they are used to adjust bids during programmatic auctions. They do not retain historical data or possess learning capabilities.
• Goal-based agents. They can evaluate multiple possible decisions to make the best one based on defined objectives. For instance, they can be used to adapt an ad campaign to the continuously changing conditions to achieve maximum ROI.
• Utility-based agents. For making decisions, such agents rely on a utility function that helps them choose options that can bring the biggest benefits. These AI assistants can offer deeper and more detailed decisions than goal-based tools. For instance, they can help to find not only the best channel for placing your ad but also the right balance between reach, cost, and quality. That’s why such AI agents are often used for tasks where multiple goals are involved.
• Conversational agents (also known as virtual assistants). Users can interact with these systems using prompts in natural language. Such agents can answer questions, help find the right dataset, generate reports, suggest improvements for ad creatives, etc.
• Multi-agent systems. These agents can operate autonomously and collaborate with other agents. Systems of this type stand out for a high level of automation and scalability for complex campaigns. They can coordinate bidding, targeting, and budget allocation across multiple platforms.

How to Choose the Right AI Agents for Advertising

The right choice of an AI agent will greatly influence the efficiency of your campaigns. That’s why it’s critical to align the agent’s capabilities with your specific business goals. Based on our practical experience in the implementation of AI across different domains, we’ve prepared the following checklist. It will help you not miss important points for consideration.

Define Your Goals

First of all, you need to understand what you want your AI agent to do. For instance, you may need such a solution to automate media buying, to conduct performance analysis, to detect fraud, or to optimize ad creatives.

Each use case and its complexity might require a different type of AI assistant.

Ensure Compatibility with Your Existing Stack

Your AI agent won’t work in isolation. Depending on its exact type and objectives, it may need to interact with analytics tools, marketing automation platforms, ad exchanges, DSPs, CRMs, etc.

It’s vital to check in advance whether there won’t be any compatibility issues.

Assess Data Processing Capacity and Regulatory Compliance

When you already have a solution under consideration, analyze its capability to handle big volumes of data. Advertising is one of the domains where an AI agent needs to process a lot of data from various sources. Apart from it, it’s necessary to verify data privacy regulation compliance.

Assess Transparency and Explainability

Lack of transparency is one of the key challenges related to the use of AI tools, especially in high-budget ad campaigns. It’s absolutely natural that you want to know how your money is spent and why this or that decision is made.

That’s why we recommend you opt for AI systems that can provide you with visualizations of how conclusions are reached, as well as audit trails for bidding and ad placements.

Think About Scalability

It’s vital to make sure that an AI agent will meet your ongoing needs. But it is also essential to think about your future growth and the expansion of your operations. Your AI agent should be able to cope with the increasing volumes of data and ad campaigns without deterioration of its performance.

How to Implement an AI Agent for Advertising

When you have chosen or built an AI agent that fully satisfies your needs, it’s time to deploy it. Here are the most important steps for successful implementation.

Prepare your data

“Garbage in, garbage out” is one of the key principles of AI systems. To avoid any disappointing consequences of using your AI assistant, you need to take care of the quality of data that you will use.

That’s why you should attentively collect and organize relevant marketing data, like ad campaign performance and user behavior, to train your AI agent on it.

Integrate your AI assistant into your ad stack

You need to integrate your AI solution with your ad servers, DSPs, SSPs, and other solutions required for agent functioning. It is also crucial to introduce new workflows that will guide how your AI agent will be included in your ad-related processes.

Monitor and fine-tune your AI agent

For efficient functioning of your agent, it is vital to continuously monitor its performance in order to detect bottlenecks and anomalies as soon as possible. If required, introduce enhancements and retrain your agent on new, more relevant data.

Apart from this, it is also recommended to implement feedback loops so the agent can learn from both successes and failures.

Start with a pilot campaign

We recommend you test your AI agent on a smaller-scale campaign first to compare its performance with your benchmarks and validate decision accuracy. The launch of a pilot campaign before going all-in will help you identify gaps in workflows, data quality, and other aspects at the earliest stages.

Scale gradually

Once the pilot campaign is successful, you can expand the use of your AI agent to full-scale campaigns, as well as introduce new channels and markets. Step-by-step, you can combine your agent with other AI tools like content generation and sentiment analysis.

Final Word

Successfully implementing an AI assistant requires careful planning, high-quality data, and seamless integration – but the rewards are substantial.

With powerful AI agents at hand, you can seamlessly scale your ad campaigns, increase personalization, and leverage actionable insights based on real-time data.