In the world of digital advertising, AI tools have long been used to analyze campaign performance, identify what's not working, and suggest improvements. Traditionally, the task of making those changes—like adjusting bids or pausing underperforming ads—was left to the advertiser. However, new advancements in AI are changing this. Some platforms now allow AI agents to make these adjustments automatically, potentially saving advertisers time and letting them focus on broader strategic goals. Leading ad technology (AdTech) companies are improving their AI tools with features like "campaign co-pilots." These AI assistants help advertisers with various tasks—such as creating ads, setting budgets, managing targeting, and generating reports—all through a single conversation. This eliminates the need to switch between multiple dashboards and forms, streamlining the process. For AI agents to work effectively, they need accurate data, clear context, and strategic input. Without proper oversight, the use of AI could lead to unexpected issues. The key difference between AI that suggests changes and AI that makes them lies in where responsibility is placed. When an AI agent makes a change on its own, the reasoning behind it can be less transparent than when a human makes the same decision. Certain operational tasks, like pausing a campaign when it reaches its budget limit or generating performance summaries, are ideal for autonomous execution. These are usually based on decisions already made by the advertiser, and the AI agent simply follows through. Testing has shown that giving AI agents detailed information about a campaign’s goals, funnel structure, and target cost per acquisition (CPA) can lead to significant improvements—sometimes over 100% better conversion performance. Access to advertising platform APIs, which allow AI tools to interact with ad platforms, is usually controlled through API tokens rather than direct account passwords. These tokens can be restricted to specific tasks and revoked if needed, but they also mean that whoever holds the token has access to the functions it covers. The industry is moving toward using MCP (Marketing Cloud Platform) based integrations, which let external AI agents connect directly to ad platform APIs, allowing advertisers to use their preferred AI tools without switching environments. The balance between AI autonomy and human oversight depends on how clearly the strategy behind the automation is defined. Most campaigns today still require a human to step in and address any gaps or issues. If a human is removed from the loop, these gaps can lead to errors. Before allowing AI agents to make decisions, it’s important to consider which conversion events are most important to the business, what routine adjustments are needed, and when human approval is required. The next stage of AI in advertising involves clearly defining the boundaries of what AI agents can do. While execution-level AI can speed up campaign management and reduce manual effort, its success relies on setting clear rules from the beginning. This includes setting limits, providing agents with enough context, and keeping human judgment close to decisions that still require it. The ultimate goal is to use automation where it truly adds value and to ensure that humans make the final call in situations where their judgment is still needed.