I Helped Scale Google Ads to Billions, Here’s How I’d Build an AI Search Strategy Today

Early in my career, I worked at Google helping scale the Ads platform. Billions of dollars in advertiser spend moved through systems my teams worked on, and I got a long look at what separated accounts that compounded from accounts that stalled.

It was rarely cleverness. The advertisers who won understood how the machine actually made decisions, measured everything, and moved budget the moment the evidence said to.

What The Auction Years Taught Me

Three lessons from that era carried over intact. Platforms reward the signals they can measure, not the effort you put in. Advertisers tried to outspend a weak quality score all the time. It never worked. The accounts that won handed the. The search implication is whether the section improves the evidence around the page, not simply whether it adds more wording. Clear entities, crawlable structure, internal links, and useful context are what make the topic easier to evaluate.

The risk is usually hidden in the execution layer. A page can look fine to a human and still fail for an automated visitor if the form, call to action, rendering path, or confirmation step is not accessible enough for the agent to complete the task.

The Game Moved From Ranking To Being The Answer

Your buyers have started asking ChatGPT, Perplexity, Gemini, and Google AI Mode the questions they used to type into a search box. The assistant reads dozens of sources, composes an answer, and names a few brands. The answer is the result. For search teams, the important part is not the headline movement by itself. It is whether the shift changes which communities, forums, video surfaces, or publisher pages now satisfy the query better than the old ranking pattern.

The useful check is whether this improves the system behind search performance, not only the words on the page. Internal links, crawlable content, clear entities, current evidence, and a sensible page structure all help the recommendation become easier to trust.

The 6 Signals I'd Build Around

When we audit a company's AI visibility, six signals explain most of what we find: Brand Authority. Assistants favor brands other people mention by name on trusted sources. Your own blog barely counts. Third party mentions do. Content. For search teams, the important part is not the headline movement by itself. It is whether the shift changes which communities, forums, video surfaces, or publisher pages now satisfy the query better than the old ranking pattern.

The 90 Day Sprint I'd Run

We run this with clients as a 90 day sprint in three phases, the same structure I use for 90 day growth audits, pointed at a new target. The practical question is what this changes in the system: the page structure, the evidence presented, the measurement habit, or the way the topic is connected to related work.

The practical value is in connecting the idea to an observable signal. That means deciding what should be checked, what would prove the issue is real, and where the team should make the smallest useful improvement first.

Days 1 to 30: Audit And Foundation

Start with a drill anyone can run this afternoon. Write five prompts a real buyer would ask, questions with money behind them, like "best [category] for [use case]" or "[your product] vs. [competitor]." Run all five in ChatGPT, Perplexity,. The strategic issue is whether automated visitors can understand, trust, and complete the same journey a human visitor can. Agent readiness is partly technical, but it is also about clear tasks, accessible flows, and reliable evidence.

Days 31 to 60: Experiment

Run one tightly scoped test per channel, small enough to read in 30 days. Rewrite 10 revenue pages into extraction friendly formats with direct answers and comparison tables. Build a genuine presence in the two subreddits where your buyers. For search teams, the important part is not the headline movement by itself. It is whether the shift changes which communities, forums, video surfaces, or publisher pages now satisfy the query better than the old ranking pattern.

Days 61 to 90: Scale And Systematize

Kill what did not move citations. Put real budget behind what did. Build the weekly ritual: the same 20 run drill, the same prompt set, logged against the baseline. Give it a named owner. On most teams that owner is the strategic lead, for. For search teams, the important part is not the headline movement by itself. It is whether the shift changes which communities, forums, video surfaces, or publisher pages now satisfy the query better than the old ranking pattern.

Where To Start This Week

You do not need 90 days to start. You need this week. Run the 20 prompt drill on your own brand. It takes 30 minutes and will change the tone of your next marketing meeting. Pull channel by channel spend and find the line item surviving on. The practical read is that brand signals need to be consistent enough for both people and AI systems to form a stable view of the company, its expertise, and its trust signals.

What The Auction Years Taught Me in practice

Introduction Early in my career, I worked at Google helping scale the Ads platform. Billions of dollars in advertiser spend moved through systems my teams worked on, and I got a long look at what separated accounts that compounded from. For search teams, the important part is not the headline movement by itself. It is whether the shift changes which communities, forums, video surfaces, or publisher pages now satisfy the query better than the old ranking pattern.

What the visibility signal actually changes

What the visibility signal actually changes: i Helped Scale Google Ads to Billions, Here’s How I’d Build an AI Search Strategy Today: the Strategic Visibility Angle should be treated as a visibility signal, not a standalone headline. Introduction Early in my career, I worked at Google helping scale the Ads platform. Billions of dollars in advertiser spend moved through systems my teams worked on, and I got a long look at what separated accounts that compounded from accounts that stalled. This connects with So Build What It Can Read when the same signal needs a clearer operating decision. A useful companion note is AI Is Merging Paid and Organic Visibility, because it looks at a nearby part of the same system.

What the visibility signal actually changes: the practical question is whether the page, brand evidence, and surrounding content make the answer easier to trust. If that support is weak, search systems can still understand the topic but fail to connect it confidently to the brand. The same pattern also shows up in Beyond Brand Sovereignty, where the practical question is how the signal becomes visible.

What the visibility signal actually changes: that is why the response should begin with an audit of the evidence already on the site before creating a new asset. The fastest improvement is often a clearer page, a better internal link, or a stronger explanation of why the brand belongs in the answer.

Where the evidence needs to be tested

Where the evidence needs to be tested: a single study or ranking observation should not become a strategy by itself. It should become a diagnostic prompt: which source is being trusted, which query pattern is affected, and which part of the site would make that trust easier to earn?

Where the evidence needs to be tested: that keeps the response grounded. The goal is to improve the evidence chain around the topic rather than publish another summary that repeats what every other page already says.

Where the evidence needs to be tested: the important distinction is between a useful signal and a fashionable talking point. A useful signal changes the brief, the page structure, the linking plan, or the measurement view.