Decision-makers at nonprofits and other mission-driven organizations are being pushed to integrate AI into every facet of their paid media program.
The sheer breadth of applications for AI in digital advertising represents a monumental challenge for those looking to get started.
We break down the main use cases for AI in digital advertising into four distinct pillars, making it easier to focus your efforts and avoid being overwhelmed by the rapidly changing landscape.
4 focus areas for AI integration in digital advertising
Decision-makers at organizations of every size are being pushed to discover the ways artificial intelligence can make their paid media strategy better, more agile, and more effective. In this new paradigm, ideas about how AI can be injected into every facet of advertising have created new opportunities for innovation. But it has also raised questions about exactly where it will make the most impact.
Beyond the potential ethical and transparency concerns presented by AI in our lives, the sheer breadth of potential use cases for AI in digital advertising can make it difficult to determine how to apply it and where. It’s a conversation we’ve had with numerous clients already, and what we’ve found to be most effective in that conversation is framing it around four distinct pillars: targeting, creative and personalization, attribution and measurement, and campaign optimization.
It may sound overly simplistic, but these pillars represent the most significant areas where AI will make—or has already made—an impact on digital advertising campaigns. And if you’re finding yourself struggling to figure out how AI will fit into in your paid media strategy in the months and years ahead, these are great places to start.
Pillar 1: Audience targeting
There was a time when selecting the right audience for your campaigns was as simple as selecting some relevant interest or lookalike segments in Meta and feeling reasonably good that your ads would get in front of the right people. Today, it’s not so straightforward.
Whether you’re a nonprofit looking to reach potential donors or a professional association looking to reach potential members, you probably recognize that your ability to narrowly target relevant users has waned in recent years. Fortunately, AI’s applications in this regard are numerous and seemingly improve every year.
Thanks to AI, audience segments go beyond the directly observed behaviors and affinities of the users they contain. Instead, predictive models, stronger lookalike modeling, contextual targeting, similar-intent keywords, and audience expansion all support advertisers’ goals of reaching the right users at the right time with the help of AI.
Because of this, you need to think about your selected audience segments and keywords more as suggestions, rather than explicit directions, for ad platforms. And while there may come a time when Google and Meta eliminate our ability to handpick audience segments altogether in favor of fully AI-driven targeting—opting us all into Performance Max and Advantage+ style campaigns—that’s a conversation for a much longer article some other time.
Pillar 2: Creative & personalization
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Some of the most popular AI tools, such as ChatGPT, Google Gemini, Midjourney, etc., are already starting to create new opportunities for brands looking to generate ad copy, images, videos, audio, and more for their campaigns. Eventually, these tools could lower the cost of entry for smaller brands that don’t have the budget to justify hiring full video production teams or setting up a custom photo shoot.
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Pillar 3: Attribution & measurement
In a perfect world, every single website conversion you see could be traced back to specific impressions and clicks your digital campaigns generated—giving you focused, granular insight into what is truly driving bottom-line performance for your organization.
Unfortunately, digital advertising measurement is never quite that easy or precise, and the challenge in achieving that level of clarity has only grown in recent years (e.g., GDPR, ad blockers, iOS 14.5, etc.). Fortunately, AI and machine learning have become increasingly important in filling the measurement gap left by an evolving digital privacy and identity landscape.
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More advanced attribution methodologies, such as marketing mix modeling (MMM) and incrementality measurement, are also reliant on machine learning capabilities, especially in instances where in-house data scientists are unavailable.
Ultimately, in this more privacy-focused digital world we find ourselves in, AI will continue to play an ever-important role in helping advertisers understand how their campaigns are performing.
Pillar 4: Campaign optimization
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Campaign optimization may not be the most flashy or visible component of AI in digital advertising, but it’s one of the most essential. And with more data being fed into ad platforms’ algorithms every single day, it’s safe to assume that it’ll only become a more prominent and effective way to manage your campaigns going forward.
Don’t rush. Move strategically.
As AI continues to reshape the digital advertising landscape, it’s clear that everyone, not just the biggest organizations or advertisers, will need to adapt their strategies for this new paradigm. For nonprofits and other mission-driven organizations, AI continues to offer new opportunities to reach the right people, craft personalized, more impactful messages, and make the most of every advertising dollar.
It’s all moving quickly, and it can be overwhelming if you set forth trying to integrate AI into your campaigns all at once. You don’t need to be an AI expert to take advantage of innovations, and breaking things down into the four aforementioned pillars can give you an easy way to focus your efforts where they’re likeliest to be the most effective.
Insights
Insight
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Perhaps the most discussed pillar of AI comes in the form of how this technology can—and already is—unlocking faster, better, and more customized creative output for ads. Though the merits and appeal of AI-generated ad creative will continue to be debated, there’s no question that AI will play a crucial part in the creative development process in the years to come (as evidenced by the fact that Coca-Cola has already put an AI-generated ad out into the world).
Beyond creative generation, AI also presents the possibility of a future where every user receives a unique, personalized version of an ad based on their preferences and interests. Dynamic creative optimization isn’t a new concept by any means, but AI could supercharge this process by developing brand-new text options, images, and even videos to give users ads that are uniquely tailored to their tastes and motivations in real time.
Google, Meta, Microsoft, and other adtech providers already rely on modeled conversion data to give advertisers a more complete view of how their campaigns are performing. Unlike observed conversions (i.e., what real conversion data ad platforms can see), modeled conversions are an estimate of how many conversions a campaign produced to fill in the gaps when observed data is unavailable. Machine learning and AI do the heavy lifting here by making educated assumptions about conversion totals based on patterns and statistical analysis.
One oft-unspoken truth about digital advertising today is that AI has, in many ways, always been better at handling real-time campaign optimization than humans. Make no mistake, human oversight is still critical, and real people are still required to pull the right levers and push the right buttons to make campaigns perform. But the algorithms that set the bids and deliver the impressions work at a speed that we simply cannot—and with more data than we can handle for any split-second decision. That level of efficiency matters a lot when you’re part of an organization that relies on a lean marketing team and where every single dollar you put into your campaigns counts.
If you’ve ever used a Smart Bidding strategy in Google Ads, you’ve already ceded some control over campaign optimization to AI. This functionality eliminates the need to manually oversee most aspects of the advertising auction process (e.g., setting bids to ensure your search ads show in the top position) in favor of optimized bids designed to get you closer to the performance goal you’ve set.
