AI-Optimized PPC Campaigns: Tools for Google Ads Management
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Artificial Intelligence has become a core feature of the Google Ads platform, though—surprisingly—it’s not entirely new. Google has been expanding its use of AI within Google Ads for nearly a decade, starting with the introduction of Smart Bidding for PPC campaigns in 2014. One could even argue that Google Ads’ first AI feature was the introduction of Quality Score in 2003. While not AI in the way we think of it today, Google’s earliest algorithm-driven features used data to optimize ad placements, a form of artificially intelligent decision-making. As AI has evolved from relatively simple algorithms to the remarkably complex machine learning models we see today, Google Ads and its AI capabilities have evolved alongside it. With modern Generative AI Tools now at our fingertips, we can envision how these tools will continue to develop and what the future impact of AI in Google Ads management might be.
A History of AI in Google Ads Management
Quality Score (2003)
Depending on how you define “Artificial Intelligence,” Google Ads has been using AI nearly since its inception, back when we still called their PPC advertising platform Google AdWords. After AdWords launched in 2002, Google wanted to improve the overall effectiveness and user experience of the platform. This would eventually lead to the introduction of Quality Score in 2003, arguably Google's first major algorithmic overhaul and step toward automating ad relevance and quality. Quality Score is still used to this day and utilizes a basic form of algorithmic intelligence that evaluates ads based on their relevance, expected clickthrough rate, and landing page experience. Increasing the relevance of the ads increased user engagement with ads.
Since Quality Score rewards the highest-quality ads with better placement and lower costs, this made the ad auction process even more efficient, allowing more advertisers to move into the space. Introducing quality score helped Google balance user satisfaction with advertiser success, making a win-win for businesses and consumers alike.
Conversion Optimizer (2007)
Next on Google’s path toward Artificial Intelligence in Ads Management was a feature called Conversion Optimizer. Conversion Optimizer was an early automated PPC bidding tool in Google AdWords that would use historical conversion data to adjust bids.
To take advantage of Conversion Optimizer, advertisers would set a target cost per acquisition. Conversion Optimizer would look at historical conversion data and use data like keyword performance, user location, and time of day to identify patterns that led to successful conversions and determine the ideal bid for each auction.
This powerful change to the platform would allow advertisers to focus on conversions with real business outcomes rather than just clicks, making Google AdWords an even more useful tool for businesses. This was arguably Google’s first foray into automated bidding strategies, a feature now so powerful, that it consistently outperforms manual bidding, The next step would be to bridge the gap between manual bidding and automated bidding, which Google would do when it introduced Enhanced Cost-Per-Click.
Enhanced Cost-Per-Click (2010)
It’s important to remember that in this era, we are still in an environment where manual bidding dominates the philosophy of Google Advertisers. Before Google’s AI tools were as ubiquitous as they are now, advertisers were quite skeptical about the efficacy of automated tools, especially new ones that would leave them with less control. Enhanced Cost-Per-Click, while still a notable step toward fully automated bidding, was a hybrid bidding strategy, providing a balance between the control of manual bidding and the efficiency of automated bidding. With eCPC, you would still set manual bids, but the algorithm would automatically raise your bids by up to 30% in auctions that it deemed more likely to result in a conversion.
Like Conversion Optimizer, eCPC also utilized historical conversion data to make these adjustments in real time. The result was a bidding strategy that generated conversions more efficiently and kept advertisers feeling in control. Looking back, eCPC can be viewed as the tool that helped advertisers become more comfortable with using AI tools in ad management and relinquishing more control to Google’s Algorithms.
Smart Bidding (2014)
In 2014, Google released a set of automated bidding strategies called Smart Bidding. Powered by machine learning, these bidding strategies were designed to optimize for different goals in every auction in real-time, what Google calls “auction-time bidding.” The machine learning algorithm, a form of artificial intelligence much closer to what we use today, uses a wide variety of bidding signals like drive, location, time of day, and user intent to make nuanced decisions on how to bid within each auction. This is similar to Smart Bidding’s predecessors eCPC and Conversion Optimizer, but machine learning is the key difference here.
Since Smart Bidding is built on machine learning AI, it could learn from past conversion data and continuously improve its own bidding strategies automatically, and the more data it was fed, the better it got at identifying high-quality queries that lead to conversions. In fact, Smart Bidding is arguably the point at which not using Google’s AI tools began to become a major hindrance to performance.
While marketers and advertisers want to avoid handing over full control to Google, there’s no denying that these Smart Bidding strategies are immensely powerful. In addition to driving great results, utilizing Smart Bidding helped to eliminate some of the busywork that came with managing manual bidding strategies, allowing marketers to put more focus on higher-level strategies and tactics.
Responsive Search Ads (2016)
Now that bidding strategies had been automated, it was time for Google to start automating the PPC ads themselves. Up to this point, the main ad type within Google AdWords (Yep, still AdWords at the time!) was Standard Text Ads, a basic ad type that allowed for a single headline, two description lines, and a display URL. These ads were pretty limited, with much lower character counts and flexibility than we are used to today. To combat these limitations, Google would introduce Responsive Search Ads and change Standard Text Ads into Expanded Text Ads, allowing for more headlines and increasing character counts for descriptions.
While Standard Text Ads got a nice upgrade, the real star of the show was Responsive Search Ads. This ad type allowed advertisers to write up to 15 headlines and 4 descriptions, allowing for a myriad of combinations to be tested against each other with machine learning AI in the auction to determine which versions of the RSA would perform best. This dynamic optimization essentially eliminated the need for advertisers to A/B test different ad copy variants against one another, and instead have the ad run those tests on themselves all the time, generating incrementally better results and automatically learning over time which combinations of ad copy performed best.
For a time, advertisers would use a combination of both ETAs and RSAs, the former providing a higher level of control over the exact copy that a user would be seeing, the latter providing a data-driven solution to test a variety of ad copy at once. However, the trend toward fully automated systems was not slowing down. In 2021, Google would announce that the following year, Expanded Text Ads would no longer be an option. Advertisers could still use their existing ETAs, but could no longer make new ones and, once paused, they could never be reactivated again. The introduction of RSAs marked a major shift toward the more AI-driven, automated Google Ads platform that we know today.
Google Ads (2018)
In 2018 Google would officially rebrand AdWords to Google Ads. On the surface, it’s a simple name change, but it would also signal a deeper shift within Google itself, pivoting from the traditional PPC model toward the expansive AI-centric platform that exists now. The rebrand marked the beginning of a new era for Google Ads, one where automation, machine learning, and AI-driven tools define the nature of paid search advertising.
Today’s Tools for Google Ads AI Management
Broad Match
Throughout the years, Google has made adjustments to its keyword match types, removing some types, and changing the behavior of other types. As it stands, the three match types are Exact Match, Phrase Match, and Broad Match. Years ago, these match types behaved exactly as their titles described, each having a different level of strictness for query matching.
The same is mostly still true today, however, the tightness of matching has loosened up quite a bit, now utilizing what Google calls “semantic matching” meaning that a query can trigger a keyword if Google’s AI thinks the terms are related, even if the query doesn’t contain your target keyword. Since these changes to match types, Google has begun to heavily encourage the use of Broad Match keywords which take full advantage of their AI-powered semantic matching. The underlying idea is to allow the algorithm to cast a wider net, allowing in more queries that may not have qualified under a stricter match type, but are still valuable queries that may lead to conversion. Since the AI can leverage the wide array of signals and identify patterns in the increasingly complex user journey, Google argues that the use of Broad Match keywords will help advertisers tap into traffic that they could not have otherwise anticipated or planned for. This strategy may feel counterintuitive for a long-time advertiser, but as match types have changed, so too do best practices for campaign structure.
The use of Broad Match keywords as Google intends and recommends requires much more extensive negative keyword lists than in the past, and careful monitoring of search queries to remove low-relevance, low-quality search queries. Broad Match isn’t necessarily a one-size-fits-all solution and is far from being the standard for modern Google Ads campaigns, so it’s important to approach their widespread implementation with caution and attention.
Performance Max
Performance Max campaigns, commonly referred to as PMax is one of Google’s latest fully automated campaign types, designed to maximize performance across Google’s array of advertising channels all within a single campaign structure. Unlike your usual Google Ads campaigns, PMax uses Google’s advanced AI systems to automate every aspect of your ads including bids, targeting, and even your ad creative based on whichever goal you specify, such as conversions, revenue, or leads.
Similar to the type of AI that runs automated bidding strategies, PMax uses a wide array of real-time signals and historical data to determine the most effective ad placements, audiences, and ad messaging for each and every auction, learning and improving as it’s fed more data. The main benefit of PMax is its all-in-one campaign nature, allowing for ease of management and eliminating the need to manage multiple campaigns across a variety of channels. However, relegating control to AI does come with certain drawbacks, mainly the lack of direct control and limited insights into what aspects of the ads are actually being optimized by the AI, such as channels or audiences. You still have access to high-level performance data like clicks, impressions, and conversions, but there is constrained access to the details of how PMax achieves these results across Google’s various ad networks. This leaves advertisers and businesses alike to place a huge amount of trust in Google’s relatively new AI systems, and given the data-driven nature of modern business, this can be a notable drawback for those looking to glean information from their advertising data.
Gemini
Gemini, Google’s AI-powered chat assistant, is arguably one of the biggest changes Google has ever made to its Search Engine Results Page. Gemini is a conversational AI tool built with a large language model (LLM), designed to integrate an AI assistant into everyday search experiences. It functions sort of like a blend between a search engine and a librarian. Instead of browsing through websites to find your answer, it can do the browsing for you and generate your answer in a conversational form. Originally released on March 21, 2023, as Bard, Gemini is still very new and changing frequently.
Today, Google is beginning to integrate Gemini directly into the Google Ads platform using the conversational experience to assist in building Search campaigns by generating ad creatives and keywords. While we expect Google to continue to add new AI tools within the Google Ads platform, these are not the only impacts that Gemini has over Google Ads and ad performance.
Given that Gemini is such a big change to the search engine results page, the home of Search Ads, the simple act of it being there affects ad visibility, as the AI overview takes up the space where you’d normally see the top-listed ads. This not only requires users to scroll further down the page to engage with Ads, but the AI overview may even answer queries and prevent users from exploring a website like they once would. The AI overview taking this prime SERP real estate affects Organic results too, pushing those even farther down the page than top-ranked ads. This can lead to decreased impressions, clicks, and CTR. In fact, early testing by Search Engine Land showed that the AI overview could lead to an 18–64% decrease in Organic traffic, primarily for informational-type search queries. So in the context of Google Ads, it’s likely that informational keywords will be more heavily affected than commercial keywords since those queries tend to trigger the AI overview less. Still, it will be important to closely monitor any updates to Gemini, as such a fundamental change to the search experience will inevitably shake up the search landscape we’re all familiar with.
How AI Affects Your Google Ads Management
Changing User Behavior
These new AI assistants aren’t going away and will likely only become more integrated into our everyday lives. While these changes won’t happen overnight, they will inevitably alter user behavior and interaction with the SERP, and to a greater extent user interaction with the internet as a whole. As users become more accustomed to the instant answers that AI generates, and more importantly as they begin to trust those answers, we will likely see fewer clicks on both Organic results and Ads, especially for informational search queries where an AI overview can provide a concise, conversational answer to objective questions.
As users become used to interacting with search in conversational interfaces, they will incorporate AI assistance in their research phase for whatever product or service they may be looking for. In some cases, user engagement with ads and Organic listings may become more passive, allowing Gemini to make decisions for them. For both Advertisers and SEOs alike, this could mean fewer opportunities to engage with users through traditional means as the need for click-driven discovery diminishes.
On the opposite end of the spectrum, some users may inherently distrust the AI overview, making their engagement with ads and Organic listings more intentional, and their clicks even more valuable.
AI-Enhanced Opportunities
While Gemini and other AI features and tools may create some challenges, they will also provide new opportunities. Taking advantage of real-time optimization, these machine learning tools can do much more data analysis than a human could do manually, allowing advertisers to focus on higher-level strategies and tactics to improve ad performance, rather than spending so much time on busy work. This is also true for advertisers’ creative workflows, with Google’s AI capable of generating ad copy suggestions, image variations, and other creative assets, reducing the overall effort required to kick off and maintain ad campaigns.
With ad formats like RSAs and PMax, AI dynamically optimizes ad creative, assembling combinations that resonate with searchers. Google’s predictive AI-powered analytics can also forecast future campaign performance and outcomes based on historical data, helping advertisers generate conversions at the most efficient prices, anticipate seasonal trends and patterns, and determine the most effective budget before hitting diminishing returns.
Anticipating the Future of AI in Google Ads
It’s impossible to predict exactly where Google is heading with its AI tools in general and more specifically the Google Ads platform, but looking at the whole timeline, we can speculate about the future of the platform.
Some advertisers envision a future of asset-driven campaigns, where instead of individual ad types, you upload a series of ad assets—logos, headlines, descriptions, images, videos, etc.—and allow Google to dynamically create whichever ads their AI deems the most effective for your business. The AI could then decide which assets to use and how to arrange them for different placements, audiences, and times. Advertisers would still have full control over the inputs and assets they provide, but if we assume a continued trend toward full automation and AI reliance, the future of ad campaign management may look unfamiliar to how we know it today.
Some envision a system similar to the real-time optimization of PMax but with more granular data available to strategize around. Again, this is entirely speculative and not based on any official announcements from Google. Still, looking at the broader picture and the trend toward automation over manual control, we can imagine a future where AI handles much of the hands-on processes while advertisers steer the ship from the top down.
How to AI-Optimize Your Google Ads Campaigns
Start Testing AI Tools and Features
Although tools like Smart Bidding took time to outperform manual methods, it's valuable to test them early. Use A/B testing to find the AI features that best fit your workflow, industry, and audience. Let AI handle the heavy lifting while you focus on the metrics that align with your business goals.
Stay Informed and Prepare to Adapt
AI is continuing to evolve and doesn’t seem to be slowing down any time soon, making it more important than ever to keep updated on Google’s changes, especially those relating to the Google Ads platform and Gemini.
Pay attention to trends within your industry, as some sectors will be more immediately impacted than others. That being said, it’s safe to assume that eventually, these AI changes will affect everyone regardless of industry so staying proactive, flexible, and adaptable is key.
Keep updated on Google’s Ads and Commerce blog to stay ahead of feature announcements and get the latest news straight from Google. Proactively learning about and testing these features will give your business a competitive edge as we continue down this incredibly dynamic AI path.
The Google Ads platform has changed dramatically since its inception, and looking through the history of algorithmic and AI-driven ad management tools, we can identify a clear trend toward AI automation that will likely continue as we head deeper into the future. As unsure as the future may seem, we know that many of these AI tools that have been around longer produce excellent results, giving us hope for an even brighter future for AI-driven advertising.
The novelty of AI will eventually fade and become so integrated into our everyday lives that it will be ubiquitous. As we approach this future, staying diligent in testing new features and staying ahead of the curve of AI developments will be the key to competing in a market that isn’t slowing down for anybody.
Are you interested in leveraging AI in your Google Ads management strategy but aren’t sure where to start? Are you too busy to keep up with AI news and want a team of experts who can help you parse through what’s important and what’s just fluff? We’re here to help you navigate the changing landscape of Google Ads and digital marketing more broadly. Our team stays up to date with the latest trends, important news, and strategies to give our clients a competitive advantage. Reach out today to schedule a free consultation and find out how AI can transform your digital strategy.
