Artificial intelligence has been part of Google Ads for some time, although its presence is much more evident now. We see it in bidding strategies, in the interpretation of searches, in ad creation, in audiences, and, of course, in campaigns like Performance Max or in more recent solutions like AI Max.
And it's not something unique to Google Ads. AI is changing a large part of the digital advertising, from how resources are created to how we analyze the results of a campaign.
Does this mean we can leave more and more decisions up to Google? Not exactly. For automation to work, it needs good data and, above all, clear objectives. Before asking the algorithm to optimize a campaign, we need to know which products we want to promote, how much we can invest to generate a sale, and what level of profitability the business needs.
That's why, when we talk about AI tools for Google Ads, we're just as interested in what they can do as we are in how we use them.
AI is already part of Google Ads
Many of the AI tools we use most frequently in campaign management are built right into the platform itself. Google has been automating processes and streamlining tasks for years which used to require a lot more manual adjustments.
Smart Bidding and Automated Bidding
Smart Bidding uses conversions and other available signals to adjust bids in each auction. In practice, this means we no longer have to spend as much time manually adjusting bids.
But someone has to decide what we're asking the algorithm to do.
Let’s consider, for example, an e-commerce site with hundreds of SKUs. Not all products have the same profit margin, nor do they sell at the same rate throughout the year, nor is it in the business’s best interest to invest the same amount in all of them. We can generate more sales, yet those sales may not be the ones that matter most to the business.
That's where automation requires a strategy.
Performance Max and AI Max
Performance Max was one of the major shifts toward a model based on signals and automation. With AI Max, this evolution is now extending to search campaigns as well, where Google is better able to interpret the intent behind a search query and broaden its scope beyond the keywords we've set up.
This has another interesting consequence: The website is playing an increasingly important role within the campaigns themselves.
The landing page, its content, and the way we describe our products or services provide context for Google's systems. The evolution from DSA campaigns to AI Max This reflects this change quite well. The campaign and the website are becoming increasingly interconnected.
AI Tools for Analyzing and Planning Campaigns
With more automation, we might think there are fewer things to analyze. In our day-to-day work, however, the opposite is true. We have more data and signals available, so a significant part of the job is figuring out which ones are relevant and what they’re telling us.
From Keywords to Intent
Google Keyword Planner It's still useful for researching search queries, estimating search volume, or getting a sense of CPCs. But keywords no longer play as prominent a role as they did a few years ago.
On our ISEM Strategic Report for E-commerce 2026 We're talking specifically about this change. Google is evolving toward a model in which, in addition to keywords, other factors are increasingly coming into play signals, search intent, and context.
We see this clearly with Performance Max and AI Max. It’s no longer just about choosing a list of keywords, but about providing Google with enough information so it can understand what we're looking for, who we want to reach, and what is valuable to the business.
AI for Working with Data
Here, too tools such as the following have been introduced: ChatGPT or Gemini. They can help us work with large amounts of data, compare time periods, identify patterns, or spot something that might warrant further analysis.
But there's a difference between noticing that something has changed and knowing why.
If conversions drop, for example, the problem could be with the campaigns, but it could also be due to inventory, a price change, seasonality, a competitor’s promotion, or even the website itself. AI can help us pinpoint the problem sooner, but We need context to understand what's happening.
AI Tools for Creating Ads
This is undoubtedly one of the most obvious applications of AI. Today, we can generate suggestions for headlines, descriptions, or ad variations in a very short amount of time, using both Google's solutions and external tools.
Ad copy and promotional messages
Google Ads can now Generate titles and descriptions using the information available in the campaign and on landing pages. We can also use Gemini or ChatGPT to come up with other approaches, test messages, or work on different versions of the same ad.
That said, coming up with 15 headlines in a few seconds doesn't mean that all 15 are good.
You have to make sure that the message truly represents the brand, that it addresses what the user is looking for, and—something that is sometimes overlooked—that What we promise in the ad matches what you'll find later on the landing page.
Here, AI saves us time and allows us to explore more options. Selection, testing, and follow-up analysis remain important.
AI for Creating and Optimizing Ad Creative
The visual aspect has also changed quite a bit. We can now generate images, modify backgrounds, or adapt resources to different formats without always having to start from scratch.
Google Ads and Product Studio
Dfrom within Google Ads itself We can generate and edit images or create new versions to use in our campaigns.
For e-commerce, Merchant Center Product Studio It is particularly interesting because it allows you to work directly with the images in the catalog: improve their resolution, modify backgrounds, or create new contexts for the products.
We can also turn to tools such as Canva and other generative AI solutions. But here we apply the same logic as with copies: just because we can produce many images quickly doesn't mean we need to use them all.
A creative concept must continue to accurately represent the product, maintain the brand's identity, and, above all, make sense within the campaign.
Google Ads' AI needs good data
We may have excellent tools and a highly automated campaign, but If the data we use isn't reliable, we have a problem.
Smart Bidding and other automated systems learn from conversions and available signals. That's why, as Google Ads becomes more automated, Accurate measurement becomes even more important.
This is also where the web analytics. We need to know what happens next beyond the click—which actions are truly valuable and whether we're correctly tracking the conversions that we later use to optimize our campaigns.
In e-commerce, this is very clear. Simply reporting that a purchase has taken place is not the same as analyzing its value, cross-referencing the results with other business data, or incorporating first-party data. The better the information the algorithm receives, the better the conditions under which it can learn and optimize.
This is one of the points we also address in our SEM Report: automation is shifting part of the workload toward data quality, measurement, and defining the objectives we set for the platforms.
Can AI manage Google Ads on its own?
It can do more and more. It can adjust bids, interpret signals, discover new search queries, generate resources, analyze data, and perform specific optimizations.
But There are decisions that can't be understood by looking at Google Ads alone.
Automating isn't the same as deciding
Let’s imagine that a campaign is achieving the ROAS we’ve set for it. On paper, it’s working. But perhaps we’re mainly selling low-margin products, we have a category with excess inventory that we need to move, or we want to prioritize acquiring new customers even if profitability is somewhat lower for a while.
Google can optimize for the goal we set. The question is whether that is really the goal the business needs.
In our 2026 Strategic SEM Report for E-commerce, we specifically identify several areas that are becoming increasingly important in management: business strategy, investment, measurement and data, analysis, and understanding of AI itself.
That's why automation doesn't make management disappear. What it's doing is shifting where we focus our attention and which tasks truly add value.
How We Use AI in Google Ads at Geotelecom
At Geotelecom, we use artificial intelligence to streamline analyses, allocate resources, identify opportunities, and make better use of the available information. But we don't apply exactly the same approach to every account, because Each project is based on different data, needs, and objectives.
That balance between automation, data, and judgment is part of our work as Google Ads agency: Make the most of everything the platform has to offer without losing sight of what the business really needs.
In addition, we're integrating AI into our own tools. Pulse, developed by Geotelecom and currently being rolled out gradually to our clients, brings together the key metrics for each business in a single platform and uses AI to help us extract insights that we then incorporate into our strategy.
Ultimately, that's where we see the most potential. Not in using AI just for the sake of it or in automating as many tasks as possible, but in have more information, detect what is happening sooner, and make better decisions.
And as Google Ads continues to automate processes, knowing what to ask of that technology—and, above all, how to interpret what we get back, will become increasingly important.
