How I Built My Own AI Ecosystem for Google Ads
A Head of PPC on what AI tools for Google Ads really do, why there is no universal AI agent, and the ChatGPT-based stack he built around his own methodology.
Updated 2026-09-28 · 10 min read · ppcfy.ai team
I often see people in PPC communities asking what AI tools for Google Ads actually help with everyday work.
For me, the answer came from my own experience rather than from advertising. I’ve been managing Google Ads and Meta Ads accounts as a Head of PPC for quite some time, and I’ve tried enough different tools to see where the actual functionality ends and the marketing promises begin.
What AI Tools for Google Ads Can Actually Do
I’ve tried different PPC tools, and they take different approaches. Some are mainly focused on bringing advertising data together and analyzing it. Others go further with automation and recommendations.
But there is still one thing they generally have in common: the specialist remains responsible for the actual decision.
There is also an important difference between an AI assistant and an AI agent. An assistant can analyze data, explain what is happening, and recommend what to do. An agent can go further by using connected tools to retrieve information and perform actions as part of a workflow.
When you visit the websites of tools in this space, you often see promises of an autonomous AI agent that can launch campaigns, optimize budgets, and think like a human.
In reality, the level of automation can be very different from one product to another. Some tools mainly identify anomalies and provide recommendations. Others can execute predefined actions or automate specific parts of account management. The important question is not simply whether a product calls itself an AI agent, but what it can actually access and what actions it is allowed to perform.
That distinction became important to me when I started building my own setup.
Why There Probably Won’t Be One Universal AI Agent
Every Google Ads specialist works differently. We all build our own methods from experience, and we work with clients who have different goals, budgets, and limitations.
For one client, the main priority might be keeping CPA under control. For another, lead quality or revenue can matter more. One specialist may be comfortable making relatively large budget changes automatically, while another may want every significant change reviewed first.
Because of that, it’s very difficult to build one tool that works equally well for everyone. There are simply too many variables, and every specialist makes decisions based on their own approach.
So instead of looking for a ready-made AI agent that fits exactly the way I work, I decided to build my own system around my own methodology.
My AI Stack for Google Ads
Before getting into the individual components, it is worth explaining what I mean by an AI stack.
For me, an AI stack is not just the AI model itself. It is the combination of the model, connected tools, data sources, client context, instructions, and automation workflows that I use to complete actual work.
In my case, ChatGPT is the main environment, while the other components give it access to the information and tools it needs to work with real client projects.
What Is an MCP Server?
For anyone who hasn’t come across MCP yet:
MCP (Model Context Protocol) is an open standard that allows compatible AI applications to connect to external systems where data and tools live.
An MCP server can expose tools that an AI application can use to retrieve information or perform actions. Depending on the server and the permissions it has, those actions can range from reading data to making changes in an external service. MCP itself is not an AI agent; it is the protocol layer that allows the AI application to interact with external tools and data.
In my case, the MCP server connects ChatGPT with Google Ads and Google Analytics 4. This gives the LLM access to current advertising account data and lets it work with those accounts directly from the chat.
The important part is that the AI is no longer working only from the knowledge it was trained on. It can request current information from the connected systems and, where the available tools allow it, perform actions there.
What Can a Google Ads MCP Server Do?
The exact capabilities depend on the MCP server and the permissions and tools it provides.
In my setup, I use MCP to retrieve current Google Ads and GA4 data, analyze campaigns, and perform specific Google Ads actions. This includes things like adding negative keywords, creating ad groups and ads, and changing budgets.
The Google Ads API provides both reporting capabilities and methods for creating, updating, and removing supported resources. This means that an MCP server can sit between an AI application and the Google Ads API and expose only the operations that the server is designed and authorized to perform.
Google Analytics 4 can provide another layer of information. The Google Analytics Data API allows applications to request reports using dimensions and metrics such as users, sessions, conversions, revenue, and other available data.
This is an important difference from simply asking an LLM about PPC. The model can work with the actual account data instead of relying only on its general knowledge.
My AI Stack
ChatGPT — the main ecosystem I work in.
MCP server — from ppcfy.ai, connecting ChatGPT to Google Ads and Google Analytics 4.
Google Drive — my file storage and the information hub for each project. This is where I keep the information the LLM needs to work with. It can find existing files, create new ones, and, where the connected app supports it, work with documents directly from ChatGPT.
Slack and Asana — I use them for communication with clients and the team, assigning tasks, and managing workflows.
Gmail — for communication and sending finished materials and reports.
A separate ChatGPT Project for each client — this isn’t a plugin or a separate platform, but it makes the whole setup much more useful. Projects keep related chats, files, instructions, and other sources together, giving ChatGPT a shared context for ongoing work. Connected apps can also be used in project chats where supported.
The important thing is that these components are not isolated tools. They form a workflow in which each part has a different role.
How I Use MCP with Google Ads and GA4
In my setup, the MCP server does most of the core work. It can retrieve current data from Google Ads and Google Analytics 4, analyze campaigns, and make certain changes in Google Ads.
For example, I can use it to add negative keywords, create ad groups and ads, or change budgets.
The whole setup lets me handle most of my day-to-day PPC work inside one ChatGPT ecosystem. A client task can come in through Asana or Slack, and I can then work with the advertising account, prepare the result, and send the finished report through Gmail.
The important part is that the LLM is working with the actual client and account context throughout the process.
This is also where the difference between an AI assistant and an AI agent becomes practical. The model is not just answering a question about how Google Ads works. It can retrieve the relevant account data, use connected tools, process the result, and continue with the next step in the workflow.
Example: Automating Google Ads Reporting
One of the most useful workflows is monthly reporting.
The process can look like this:
Google Drive → report template
MCP → current Google Ads data
ChatGPT → analysis
Google Drive → finished report
Gmail → email
For example, when I need to prepare a monthly Google Ads report, the LLM can take the report template from Google Drive, get current Google Ads data through the MCP server, analyze the results, create the report, and prepare it for sending by email through Gmail.
The same workflow can also include Google Analytics 4 data when it is relevant to the report. This makes it possible to combine advertising performance with the analytics data needed to understand what happened after the click.
With the right workflow in place, most of this can happen automatically. The person still reviews the result and gives the final approval.
AI Skills in ChatGPT
Another part of my setup is AI Skills.
A Skill is a reusable workflow that gives ChatGPT instructions, supporting information, and, where applicable, structured steps for completing a specific task more consistently. A Skill can include instructions, examples, code, and supporting resources.
I see Skills as a more structured approach than keeping one huge set of instructions for every task. Instead, you can create separate Skills for specific processes.
A Skill can define what the model needs to do, in what order, what information it should use, what tools it should work with, and what the final result should look like.
For example, I can create a separate Skill for preparing a monthly Google Ads report.
The LLM first follows the instructions in the Skill and works through the reporting process. It looks at the project context, reviews the relevant client information, checks the report template, and follows the rules for preparing the report.
Then it can use the connected MCP server to get current Google Ads data. The LLM analyzes the data and creates a new document in Google Drive based on the report template.
The report follows the logic and rules defined in the same Skill and uses the data retrieved through MCP.
The advantage is not simply that the model has more instructions. The advantage is that the process becomes reusable. Instead of explaining the same reporting methodology every time, I can define it once and use the same workflow again.
Automating Google Ads Tasks with Scheduled Tasks
Another part of the setup is scheduled automation.
ChatGPT can run one-time or recurring scheduled tasks, and current versions also support event-triggered tasks through connected apps for eligible users.
For example, I can schedule a task to check search queries every Monday. By 9 a.m., I can already have a list of potential negative keywords ready for review.
I can then ask the LLM, with an additional confirmation step, to add those negative keywords to the relevant Google Ads account.
There is one important limitation to keep in mind: a scheduled task created inside a ChatGPT Project cannot access files uploaded to or stored in that project. So the workflow has to be designed around the data and connected tools the task can actually access.
Claude has a similar concept. Claude's scheduled tasks can run recurring work remotely and can use connected tools and files available to the Claude environment.
A routine task that used to take a lot of time can become a simple process of reviewing and approving a result that is already prepared.
Read Access, Actions, and Human Approval
There is an important distinction between giving an LLM access to advertising data and allowing it to make changes.
An MCP server can expose different tools, and those tools can have different capabilities. A read-only workflow might retrieve account data and produce an analysis. A workflow with write access can go further and create, update, or remove supported resources.
This distinction matters when the connected system is a real advertising account.
In my workflow, I don’t treat automation as a reason to remove human control.
The idea is to let the LLM do the repetitive work — collect data, analyze it, prepare changes, and create reports — while the specialist remains responsible for reviewing and approving important actions.
That balance is especially important when an AI system has permission to change live advertising campaigns, budgets, keywords, or ads.
Conclusion
For me, it makes more sense to build my own AI ecosystem than to keep looking for one universal AI agent for Google Ads — or even for PPC in general — that promises to do everything.
I can connect an MCP server and the tools I need, create my own Skills and scheduled tasks, and build workflows around the way I actually work.
The MCP server is a key part of this setup. Without a connector such as MCP, the LLM cannot directly retrieve current data from a Google Ads account or perform actions there through connected tools. It can still answer questions using its general PPC knowledge, but that’s very different from working with the actual account.
It’s also important to create a separate project for each client and give that project as much useful information as possible about the client and the work. Projects are designed to keep related chats, files, instructions, and other sources together, which makes them useful as a persistent context layer for ongoing client work.
The more relevant context the LLM has, the better it can understand what you do, how you do it, and why.
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About the Author
Yevhen Ostroverkh
Founder of ppcfy.ai
PPC & AI Automation