The 4-step roadmap to AI agents for Google Ads

Every week, there’s another announcement about AI agents. Google is building them, software vendors are selling them, and LinkedIn would have you believe every marketing team will soon have an autonomous employee managing campaigns around the clock.

It’s easy to conclude that the next competitive advantage is building an AI agent as quickly as possible.

While that idea may hold some truth, I think it’s the wrong perspective.

After spending the last year building agentic systems for Google Ads, it’s become clear that not every organization is ready for an AI agent. The businesses that see genuine commercial value all follow roughly the same journey, while the ones that struggle usually skip straight to the expensive part.

1. Build a foundation before involving AI

Before experimenting with AI, invest in two foundations: your knowledge base and your data. This is the least exciting stage, which is probably why it’s the one I often see businesses skip.

One of the biggest misconceptions surrounding AI is that it compensates for poor processes. In reality, it simply automates those processes faster.

The quality of any AI system is less about the model you use than the context you give it. Even the most capable large language model (LLM) can’t make sensible decisions if your business knowledge is inaccessible and your data is fragmented across multiple platforms.

Your knowledge base should document the following elements in a format AI can understand:

• Products.
• Services.
• Business rules.
• Tone of voice.
• Campaign structure.
• Internal processes.

At the same time, make sure your marketing data is accurate, connected, and accessible. Whether you use BigQuery or another centralized warehouse matters less than eliminating the silos that stop AI from seeing the full picture.

Dig deeper: Agentic AI and vibe coding: The next evolution of PPC management

2. Exhaust off-the-shelf AI before you build

You don’t need developers to start benefiting from AI. I’d argue most Google Ads teams haven’t yet exhausted what today’s off-the-shelf tools can already do. Many advertisers underestimate what’s already possible.

Start by exporting campaign data into ChatGPT or Claude. Ask it to audit account structure, identify wasted spend, surface search term opportunities, or review your shopping feed. The latest models are remarkably capable at analyzing large datasets, often uncovering patterns that would previously have taken hours in spreadsheets.

Next, link these tools with Google Ads, Google Analytics, or Google Merchant Center through pre-built Model Context Protocol (MCP) connectors. Instead of exporting spreadsheets every week, this approach lets you query live account data while retaining persistent business context through projects or custom GPTs.

For many organizations, this combination will deliver the majority of the value they’ll ever need. Build only when you’ve genuinely reached the limits of this setup.

3. Build custom systems when you need the complexity

Eventually, you might outgrow off-the-shelf tools. This happens when your requirements become more specific.

For instance, you might need to combine advertising performance with stock availability, pricing, margin, and customer relationship management (CRM) data. Or, you might want AI to continuously monitor accounts instead of waiting for prompts or automate approval workflows while retaining appropriate controls.

This is when custom development becomes worthwhile.

Developers make AI systems more reliable. Together, custom MCPs, guardrails, orchestration, scheduling, and cost optimization transform an interesting demo into a system that’s dependable enough to use every day.

Dig deeper: Why PPC AI agents fail without business data

4. Identify and encourage early AI adopters

Ironically, the biggest obstacle to successful AI adoption rarely has anything to do with technology. Instead, it’s people.

The organizations that progress the fastest don’t necessarily expect every employee to become an AI expert overnight. They identify enthusiastic early adopters and give them space to experiment. Then, they encourage early adopters to share what works and gradually embed those workflows across the wider team.

Rather than replacing marketers, AI changes how and where marketers create value.

We’ve already spent the last decade handing more execution to algorithms via Smart Bidding, broad match, and Performance Max. Agentic AI is simply the next stage of that evolution. The marketer’s role continues to shift away from manual execution toward strategy and judgment.

The goal isn’t autonomous marketing

The biggest mistake I see is businesses trying to automate every aspect of Google Ads. That isn’t where the value lies.

Agentic AI is exceptionally good at repetitive, data-heavy work like auditing accounts, monitoring performance, analyzing trends, and surfacing optimization opportunities. When you take those tasks off experienced marketers’ to-do lists, they can spend more time on strategy, creative problem-solving, and business objectives.

The teams that outperform over the next few years won’t necessarily have the most sophisticated agentic AI. They’ll be the ones who understand where AI creates leverage, where human judgment still matters, and how to build the foundations that allow the two to work together.

That’s why the first step is building an organization that’s ready to benefit from AI agents.