Like you, I’m getting tired of artificial intelligence (AI) dominating the narrative of every news article, shareholder update, and new product announcement. You can wade through 100-page manifestos where OpenAI researchers claim AI will be “able to automate basically all cognitive jobs,” or watch Elon Musk haltingly hand-wave an assertion that AI will “provide universal high-income [and make] work optional” for all of humanity, but there’s a decided lack of intelligent discourse on how it will actually change an advertiser’s daily life.
So to help ground the understanding of how AI is most likely to shape the landscape of advertising, I’ve outlined the four main themes that are most likely to impact advertisers. Some are happening today and some are yet to take shape. My goal is to impart some salient tips on what you, as a marketing leader, can do to better position yourself for the changing ecosystem.
1. Machine learning takes on targeting and optimization
The most commonly deployed, active subset of AI in-market today is applied machine learning (ML).
Google’s relatively new Performance Max (PMax) and Meta’s Advantage+ Shopping Campaigns (ASC) are perhaps the best examples of how advanced, self-tuning products are changing the shape of performance advertising. Both employ broad-based machine learning for both targeting purposes and providing feedback loops to increase the efficacy of performance advertising. In the old world, this was largely handled by a campaign manager. Meta lookalike campaigns are a great example: A campaign manager would upload their ideal audience, and Meta would go find similar audiences. Meanwhile, the campaign manager could choose the destination, bid strategy, exclusions, etc. In the new world, an algorithm controls the targeting and bid optimization.
To nonprogrammers, the best analogy I can use for how this mechanism operates is the robotic vacuum cleaner the Roomba. In most cases, a human being can vacuum a floor much more efficiently and faster than a Roomba. Likewise, until recently, a human performance media manager was the best bet for managing your campaign.
While increasing scale, using multiple formats, and other factors are continually making human-controlled systems more difficult, there is one trend in particular that is driving the rise of ML-enabled performance buying: signal degradation. With the deprecation of deterministic identifiers, we’ve lost the most important part of performance advertising’s optimization loop: the tracking data. In our analogy, that’s much like turning the lights off in the room you need to vacuum. All of a sudden, the robotic Roomba has a huge leg up on the human being. They aren’t affected by the dark (as they use infrared sensors to operate), they have a perfect memory, and if not initially, then over time they will use that memory and sensors to vacuum far more efficiently than a human ever could.
This is the new reality with performance media. Without deterministic identifiers, we’re now feeling our way in the dark. No longer can we map users across the web. Instead, we rely on machines that are constantly experimenting with bid prices, placements, ad types, and targeting strategies — at a scale that’s unprecedented and impossible for a human to do — that don’t require following a user across the web. Because Meta and Google make the majority of their money through advertising, over the last couple of years, these scaled advertising companies have invested huge amounts into addressing this new paradigm and have built highly sophisticated ML tools that can run advertising “in the dark.”
What does this mean for the advertiser? The most effective thing you can provide these new ML tools is data. Specifically, data on the value of the users from the campaign. This data provides a feedback loop that will increase the efficacy of the advertising. There are two main themes to consider when architecting this feedback loop: 1. Send broad signals of success and 2. send signals early.
Broad-based signals of success (or failure) will help the algorithm uncover successful attributes with less data. Consider, if your success metric is 1 out of 100 users who convert — a 1% click-through rate (CTR) — the algorithm has a single user to learn from. If you can point to 10 out of 100 users (10% CTR) who are more likely to convert, you’ll have 10x the users to help the algorithm build the attributes of success. Your campaign will learn how to optimize 90% more efficiently.
Sending signals early helps the algorithms iterate and optimize more quickly. If your success signal is the conversion of a user, 30 days after install, the algorithm will need to wait 30 days to determine if its optimization changes are effective. If you can send a signal back on day 1, the campaign will be able to optimize and iterate at 30x the speed.
In both cases, early predictive success signals can help these new ML-based advertising tools find and iterate toward success faster.
2. GenAI runs ad creation and optimization
When it comes to asset creation, this is where things get interesting. Creative is often the biggest return on investment (ROI) on your paid media strategy. Think about it: You’re buying ad space, and that’s a fixed price. The creative you stick into the slot doesn’t materially impact the price of media, but it does impact user conversion. You can serve a creative that works, or one that doesn’t. This means that with a fixed delivery cost, you control performance. Therefore, getting better ROI for paid media is often about producing more effective creative. Creative is traditionally time-consuming and rather expensive to create, and making iterations and experimentation is slow and costly. Generative AI (GenAI) is a clear opportunity.
Unfortunately, advertising outcomes — like statistics — aren’t always intuitive. If you’ve wondered why advertisements are so ugly, it’s because ugly ads are noticed, and noticed ads are more effective. The biggest challenge currently facing the GenAI application is allowing advertisers to maintain brand integrity. Brands are unwilling to risk integrity for performance, unchecked. Even Google’s explanation of one of their app campaign settings, “automatically created assets,” has an example of issues from auto-generated text for a smartphone campaign.
One of the created headlines is “Trade-in offers available,” which is clearly not part of the advertiser-provided text. It’s a perfect example of why guardrails are needed before AI can be released to create media assets.
These limitations are temporary and surmountable — companies are already working around this today. One of the most novel ideas I’ve come across is taking existing video assets and using AI to iterate, customize, and localize video assets. Clearly asset creation stands to gain massive benefits from GenAI, once the kinks are worked out.
There is a clear use case for GenAI is augmenting the ad creative process. Leaders in this space are already testing how GenAI can help them, whether it be creating ideas, iterations, variations, or localized options. Future leaders in this space will find methods to add guardrails and protections to ensure these systems help them iterate to winning advertising strategies, without causing disruption.
3. SEO will become “AIO”
You’ve probably already encountered Google’s Gemini-created search summaries. Unsurprisingly, Google has announced the insertion of sponsored results into AI Overviews. Clearly the future of AI-generated search summaries will contain advertising by way of branded AI suggestions. As the nature of search changes with the adoption of large language models (LLMs), so will the nature of search advertising. I’m thinking of this as a shift from “search engine optimization (SEO)” to “artificial intelligence optimization (AIO).”
How do AI-generated search summaries change consumer behavior? Think about your own experience with ChatGPT, Gemini, or Copilot. You ask a question — one that may include quite a bit more detail and context than a general Google search — and expect a singular outcome. Instead of providing a generalized query, expecting to parse multiple existing pieces of content for an answer, you’re looking for a singular, customized, interpretative result to your specific query. Think of a Google search for “Remove wine stain” versus “How do I get this cabernet stain out of my white linen shirt at the restaurant?”
Initially, a search paradigm shift will be advantageous to existing search-engine advertising methodologies — namely Google’s — because it can heavily rely on its existing keyword framework, the backbone of its incredibly successful search ads business. Keywords chosen by advertisers will provide some context for insertion in AI search summaries with no need to change workflows or buying methodologies. Google can immediately apply keywords toward LLM responses.
However, as this use case matures, I expect that innovators and fast movers will quickly start to learn methods for measuring and optimizing on the new paradigm. The most expensive Google keyword today is “insurance.” This (complete) search term is extremely valuable because performance advertisers have found that that search term leads to conversion on a new policy. But with LLM-generated answers, user search behaviors will change and may no longer include previously expected keywords. A more effective search query for selling insurance may turn out to be “How do I reduce my living expenses?” or “What are the financing rates for a new car?” or “What is the best auto loan available in my area?”
The point is, as a user’s method of engagement with information changes, so will the most effective route of advertising. Successful advertisers in a world with AI will need to understand how to better optimize their content for these changing methods of engagement.
4. Personalized AI will create new routes for user discovery
Apple has announced an on-device small language model (SLM) — dubbed Apple Intelligence — which will use protected personalized information to help users better interact with their device. At first, this will be applied for device-centric things like emotive text messages, on-device search, and text editing — all things that Apple largely controls. But it will also control engagement with third-party apps as well.
The App Intents framework allows apps to surface content and actions to Spotlight search — and eventually, Apple AI— for user engagement opportunities. In this year’s Worldwide Developers Conference (WWDC), the example used was searching for a certain hiking trail without opening an app. In the very real possibility that Apple AI becomes an important primary method for users to interact with their device, this will change how apps surface, measure, and optimize user engagement.
It’s difficult to overstate how much impact this is likely to have on discovery and user engagement. Once a competent AI can start stringing together these App Intents, it will change how users find and select vendors and content. An example would be: “Have Thai food delivered when my mom’s Uber gets to the house.” This request will require interaction and content across multiple vendors, where the AI will need to interpret intent and gather context from third-party apps. How the context is surfaced will be up to and controlled by the apps themselves. Those who innovate to move with this trend will be rewarded with increased AI capabilities and by extension, engagement.
This marks a coming gold rush of opportunity as apps learn how to choose, surface, measure, and optimize how App Intents drives user interaction and behavior. App Intents will initially take shape similar to app store optimization, where apps will vie for popular search terms, and it’s not a stretch to envision an evolution into App Search Ads, where apps actually bid on the more popular terms for a chance to get in front of the user. Even if it doesn’t turn into an ad network, it will most certainly bring an opportunity for analytics and optimization of how apps interact with App Intents.
Conclusion
While parsing the actual impact of AI may seem ethereal and vague, there can be no doubt it will fundamentally change the way humans interact with technology — and with brands. And of course, these changing user behaviors will dictate shifts in advertiser strategy. While the future of AI might be tough to grok and consumer patterns are still evolving, it’s clear the coming changes AI is bringing to our lives can’t be understated. Much like how mobile changed how consumers fundamentally connect with the internet, AI will do the same. And like with mobile, some companies will innovate and emerge as winners, quickly adapting to these fundamental changes. And also like with mobile, some companies will struggle with these fundamental shifts and fall behind their peers.