Four stages of adopting AI agents in Google Ads

Using AI agents to manage Google Ads has recently become a widespread trend, though not every company is equally prepared for it. In an article published on Search Engine Land, Robert Simpkins analyzes experience in this field and notes that organizations achieving real commercial results tend to follow nearly the same path, while those running into trouble often jump straight to an expensive, complex stage.

The first stage is laying a solid foundation before bringing AI in at all. The author argues that the quality of any AI system depends less on which model is used than on what context it's given. Companies therefore need to first build a knowledge base covering products and services, business rules, brand tone, campaign structure, and internal processes, and establish centralized infrastructure that makes data easy to access. Otherwise, existing flawed processes just get automated faster by AI — nothing more.

The second stage calls for fully exploring what off-the-shelf AI tools can already do before building anything custom — many Google Ads teams don't make full use of the tools already available to them. The author suggests starting by feeding campaign data into tools like ChatGPT or Claude to analyze account structure, spot wasted budget, surface new keyword opportunities, and review product feeds. The next step is connecting services like Google Ads, Analytics, and Merchant Center through ready-made MCP (Model Context Protocol — a standard protocol that links AI systems to external data sources) connectors. For most organizations, this combination alone delivers the bulk of the value they're after.

The third stage is building custom systems once requirements outgrow what off-the-shelf solutions can handle. That might be driven by the need to tie ad performance to inventory, pricing, margin, and CRM data, a need for continuous account monitoring, or a requirement to automate approval workflows. Custom MCPs, safeguards, governance and planning systems, and cost optimization are exactly what turn an impressive demo into a reliable system you can trust to run every single day.

The fourth stage is identifying and encouraging the employees who become early champions of AI. The author observes that the biggest obstacle to rolling out AI agents isn't the technology — it's organizational culture. AI is changing where and how marketers create value, which is simply a continuation of a decade-long trend of handing algorithms more and more execution-level tasks: first Smart Bidding, then broad match, then Performance Max, each advancing in the same direction.

The author warns against trying to automate "every single aspect" of Google Ads. AI is most effective at repetitive tasks that require processing large volumes of data — auditing, monitoring, and trend analysis — freeing marketers to spend more time on strategy and creative work. Going forward, success will belong to organizations that understand exactly where AI creates an edge, when human judgment still matters, and how to build the foundation needed for the two to work together.