At this point, it is clear: AI is here to stay, across all industries, in all of our lives. You’ve probably noticed the tonal shift in advertisements, generative search results on Google, the services offered by major companies, and even the content you’ve been served online. It feels like every industry has found a way to wedge an AI companion into their offerings.
As digital marketers, it is becoming even more important to understand the impact this has on the platforms we use. We need to know where AI genuinely enhances marketing efforts and where we still need careful human oversight. Nowhere is this clearer than in search. AI-powered paid search is reshaping how users interact with search engines, and marketers who adapt thoughtfully can find real opportunities.
Google’s Drive for Innovation and the Role of AI in Paid Search
Google prides itself on being at the forefront of technological innovation. From its early dominance in search algorithms to advancements in machine learning, Google has always positioned itself not just as a service provider but as a pioneer shaping the future of information access. The company’s rollout of AI-powered search experiences is just the next chapter of that mission.
But while innovation is valuable, it’s important for marketers to recognize that these advances also serve Google’s broader goals: increasing platform stickiness and focusing on its own profit motive.
Default Bias Engineering: Navigating Automated Recommendations
Let’s talk about default bias engineering. Default bias engineering is the practice of designing platform features and settings in a way that nudges users to accept default options that primarily benefit the platform itself, often without the user’s full awareness. Platforms like Google often frame features such as “AI Essentials” as must-use recommendations, encouraging marketers to auto-apply settings that allegedly guarantee success.
The truth is more complicated. These recommendations are often designed to drive platform adoption metrics and revenue growth, not necessarily to improve your campaign outcomes. When automation is designed by the same entity that profits from your spend, it’s worth asking who it really serves.
Default settings are engineered to look helpful, but they can lock advertisers into broader targeting, looser control, and higher costs—without delivering better results. Blindly accepting these defaults risks undermining your strategy, your targeting precision, and your profitability.
Marketers should treat all easily auto-applied or recommended AI settings with skepticism. Validate whether the setting actually aligns with your goals, your brand voice, and your desired outcomes. Smart automation is strategic.
What Other Agencies Will Recommend (and Why They Might Be Wrong)
A lot of agencies and digital marketers will argue that we need to narrow our focus and reduce the number of keywords we use, claiming that “too many keywords will lower your optimization score” or cause “budget dilution.”
Maybe that’s true in a vacuum, where the team isn’t working to continuously optimize campaigns and iterate on past success. But in our case, we saw something different.
Lets start with the big scary concept of budget dilution, a term some agencies will claim is a symptom of having too many active ads, keywords, or other elements in your strategy. It’s often used to justify oversimplified campaign structures with fewer touchpoints, all in the name of streamlining and reducing the need for frequent incremental adjustments.
But this thinking is the antithesis of economies of scale. It assumes that adding complexity through more intent coverage, more ad variations, and entry points inherently leads to worse performance. In reality, strategy is more than the sum of its parts. Campaigns with thoughtful layers and intentional design don’t suffer from noise. A well-executed structure will create a symphony of sound, not a chaotic cacophony.
No one wants to see their campaigns do worse as they scale. At Eight Oh Two, we’re constantly trying new methods to scale campaigns for our clients, and by looking at the results of one of these instances, we can begin to put this theory to the test.
Over the course of our test on a better way to implement broad match keywords, we more than tripled the number of keywords in campaigns by adding match types, new variants, and additional relevant terms we believed could improve performance.
In one campaign, we moved from a simplified broad match strategy (aligned with Google’s default bias engineering) to our own best practices. The results were clear:
- Spend increased 109% year over year.
- Revenue increased 78% year over year.
Most importantly, we were in the driver seat, and we were able to control our growth. And no, there was no budget dilution.
Optimization Scores and AI
Now let’s look at optimization scores, and what they really do.
Optimization Score is a Google Ads metric designed to estimate how well your campaigns are set up according to Google’s best practices. It’s expressed as a percentage, with 100% supposedly meaning your campaign is “optimized.”
Here’s the thing: Optimization Score does not measure actual business outcomes like conversions, revenue, or profitability.
Instead, it measures how closely your settings align with Google’s preferred configurations, many of which push greater adoption of automated bidding, broad match keywords, and other AI-driven features.
We should treat Optimization Score as a diagnostic tool, not a key performance indicator.
In most cases, we’ve found that removing redundant keywords only boosts Optimization Score by around 0.7%. Not exactly game-changing. When you dig deeper into Google’s own tips, it becomes even clearer:
“Adding very similar keywords isn’t recommended, as only one keyword would match both searches. However, doing so won’t affect your costs or performance in any way.”
To put it simply, the only detriment to having extra keywords is time and effort on our part as marketers. It’s not going to “dilute our budgets,” as some would have you believe.
By leveraging AI tools to generate keywords, and then managing them and reviewing their performance down the line, we can open up possibilities and increase accuracy while making our campaigns more robust.
That is not to say that all keywords are relevant, or that there is no such thing as redundancies. But as Google continues to push for mass adoption of broad match without guard rails, marketers need to be even more cautious about what changes they accept.
AI Is Already Embedded in Search—and Has Been for Years
If you think AI in search is brand new, think again. Autocomplete suggestions, smart bidding strategies, and personalized results have been quietly powered by machine learning for years. Google’s Search Generative Experience (SGE) and Microsoft’s AI-driven search tools are just the latest evolution. We’re not starting from scratch here. We’re just adapting to a landscape where AI is a bigger, louder part of the search ecosystem.
Low-Lift Tasks Where AI Enhances Search Marketing
Not every use of AI needs to be revolutionary. Some of the most valuable applications today are in the basic, repetitive tasks that drain time and focus. AI tools can:
- Surface keyword suggestions based on real user intent, not just raw search volume
- Analyze search query reports faster to identify irrelevant traffic and suggest negative keywords
- Draft first-pass ad copy variations for faster A/B testing
- Summarize large volumes of search trend data into actionable insights
These tasks aren’t new.
Marketers have always done them manually. AI just moves the heavy lifting into the background, freeing us up to spend more time where it really counts: analysis, strategy, creativity.
As digital marketers, our goal is not to streamline our teams, or replace real people with an AI-powered facsimile of the role they play. We need to use AI to deliver efficient results without losing the empathy that only humans can provide.
Elevating Junior Marketers Without Automating Judgment
One of the most promising aspects of AI-powered search tools is how they can elevate less experienced marketers through knowledge sharing and as a source of aid. Instead of spending the first year of their career manually pulling search term reports and scrubbing keyword lists, junior team members can:
- More quickly spot trends and shifts in user behavior
- Focus on developing messaging, testing ideas, and understanding customer psychology
- Participate earlier in strategic conversations because they have better data at their fingertips
The next generation of marketers is working in a world where we have more at our disposal than ever before. But here is the caution: AI does not replace the need for human judgment. A keyword tool might suggest a high-volume search term that looks promising on paper but makes no sense for your brand. An auto-generated ad might technically match a query but miss the emotional tone your audience needs.
The best use of AI is as a force multiplier for good judgment, not a substitute for it. Junior marketers still need strong mentorship, training, and critical thinking skills. AI just gives them better tools earlier.
So, What’s Next?
It’s easy to say “start small, play it safe.” But eventually, that won’t be enough. Beyond broad match, we’ve also leveraged AI-powered elements of Google Ads, including how campaigns are targeted at the geographic and even language levels.
What’s guaranteed is that as Google rolls out new updates to their platform, more and more will be “powered by AI.” We know this is fine. But that brings us to what we don’t know. Lets look at how broad match search terms are generated:
“Broad match uses the power of Google AI to extend its reach beyond exact and phrase match by identifying related queries to reach more customers and drive better performance.”
Okay great, that doesn’t tell us too much. Phrase match keywords show ads on searches that “include the meaning of” your keyword, and exact match show on searches that have the “same meaning” as your keyword. Honestly, even those definitions could be better. By putting forth broad match as powering ads that show on searches that “relate” to your keyword, as defined by AI, and as a default to boot, Google’s left out some key information. (Don’t worry, though; we figured it out here.)
We can leave that at “broad match, AI figures it out.”
But what about geographic targeting? In our test, we found that Google’s AI actually does a great job at figuring out who is interested in a set area. You can read more on it here. The TL;DR is that our campaign that left the distribution of national level delivery up to Google’s AI had promising results.
Oh, and let’s take a quick look at Google’s official summary of how interest is determined.
“If the Google Ads system detects geographic areas that someone has shown interest in, we may show appropriate ads targeted to that area or surrounding areas (known as ‘location of interest’).”
Some of the ways that Google might detect a location of interest include:
- Terms used in searches that indicate a location
- Past searches that indicated a location of interest
- A person’s past physical locations
- The content and context of a website where an ad is displayed. (Keep in mind that the mention of a location on a page doesn’t always indicate an interest in that location.)
- Searches on Google Maps or Google Maps for mobile
- If someone sets a custom location for Google search results
Finally, let’s look at how Google uses AI to determine what language we speak. You can find more detail on this here, but by successfully leveraging Google’s language-targeting abilities, we were able to greatly widen the reach of our campaigns, with minimal manual work on our end when deciding who to target. In fact, the only thing we had to change at the targeting level was the language settings. Now let’s see how Google defines the way it detects languages on the search network:
“Google Ads uses a variety of signals to understand which language the user knows, and attempts to serve the best ad available in a language the user understands. These signals could include query language, user settings, and other language signals as derived by Google AI.”
Oh, there’s our “Google AI” buddy again. Like broad match, this is a case where we are getting less insight into the actual signals feeding Google’s AI. At the end of the day, this is something we will need to adapt to as marketers. Google’s not going to wake up one day and decide to make all of their tools open-source, so we have to do the testing ourselves. By continuing to leverage new tools, and aiming to fully understand them, we can make the best of everything we have at our disposal, and do more than just replace marketers with machines.
How to Integrate AI-Powered Paid Search Today
- Start small. Use AI to speed up tasks you already understand deeply, like negative keyword mining or ad copy brainstorming.
- Validate everything. Never deploy AI-generated outputs without a second look. Mistakes can cost real money.
- Stay focused on fundamentals. Good search marketing is still about relevance, clarity, and delivering value to the user.
- Resist the shiny object syndrome. Not every AI tool is worth your time. Choose tools that solve real problems, not just new problems.
- Explore opportunities to dig deep into the AI that powers the tools we use, beyond the instructions that come with them.
AI is reshaping the digital marketing landscape, but the fundamentals have not changed. Strategy, empathy, and critical thinking still win. Smart marketers will use AI to enhance their skills, speed up their processes, and uncover new insights—without handing over the keys entirely.
In search marketing, staying grounded is your competitive advantage. AI can help you move faster and think bigger, but only if you keep your hands on the wheel.Want to see how AI-Powered search can deliver for you and your clients? We’d be happy to dig in and discuss strategies. Reach out to us using our contact form or connect with us on LinkedIn.
