By Yuting Zhang
The industry is having an identity crisis. We’re clinging to the ghost of targeting past—obsessing over first-party data strategies, lamenting the loss of third-party cookies and desperately trying to recreate the surgical precision we once had in audience selection. Meanwhile, AI has been busy solving the targeting problem in a completely different way.
Here’s what many marketers don’t realize: AI-driven platforms and campaign types such as Google’s Performance Max and Meta’s Advantage+ campaigns aren’t just automating our old targeting methods—they’re using our creative assets as the primary targeting mechanism.
How AI rewrote the targeting playbook
Walk into any agency or brand marketing meeting, and you’ll hear the same conversations. “Our first-party data strategy needs work.” “We need better audience signals.” “How can we find more audience segments?”
What these conversations overlook is that AI has already rewritten the playbook of targeting, albeit not in the way we expected.
When you launch a Performance Max campaign, Google’s AI doesn’t just look at your audience selections and deliver ads to those people. Instead, the algorithms analyze how different users respond to your creative assets and use that data to find similar users who are likely to respond positively.
That lifestyle image of your product? The algorithm reads it as a signal to target aspiration-driven consumers. That feature-focused headline? The algorithm interprets it as targeting analytical buyers. Your video testimonial? The algorithm uses it to find people who value social proof.
The platforms aren’t using your creative to communicate with audiences you’ve already targeted. They’re using your creative to identify and target the right audiences in the first place.
How AI becomes the targeting department
Meta’s Advantage+ campaigns now let algorithms make most targeting decisions automatically. Google’s Performance Max uses machine learning to span their entire ecosystem with minimal advertiser control over audience selection. Even traditional campaign types increasingly rely on AI-driven delivery optimization.
The platforms don’t take away targeting controls out of spite—they do it because they found a better way. Their algorithms can process billions of signals about user behavior, content preferences and response patterns that marketers never had access to in the first place. But these algorithms need one crucial input: creative assets that generate clear performance signals.
I recently helped a friend launch a test prep tutoring business and needed to generate leads on Facebook and Instagram. We created a Meta campaign with instant forms, targeting multiple interests and behavioral segments around standardized tests, college admissions, and graduate school prep.
My friend’s initial creative focused on improving test scores and study strategies. Unsurprisingly, 90% of the leads were high school students preparing for the SAT. When my friend wanted more GMAT leads for business school applicants, I advised simply editing the creative language to specifically mention GMAT prep and business school admissions.
Two weeks later, 38% of leads were coming from MBA candidates—same targeting parameters, different creative language, completely different audience composition.
The AI-creative partnership marketers are missing
Most marketers haven’t grasped this fundamental shift. We’re still thinking about creative development as separate from targeting when these algorithms have already merged them into a single process.
This isn’t about abandoning targeting entirely. It’s about understanding where AI-driven targeting happens now. Your audience signals and first-party data matter, but they’re suggestions to the algorithms, not commands. These algorithms make the final targeting decisions based on which creative assets generate positive responses from different types of users.
This means creative development can no longer be an afterthought. We need to approach creative strategy the same way we once approached targeting —with hypothesis-driven audience segmentation, systematic testing and continuous optimization based on performance data.
Instead of asking “who should see this ad?” we should be asking “what creative will attract our ideal customers and train the algorithm to find more like them?”
The future is already here
The marketing industry needs to stop mourning the death of traditional targeting and start embracing the AI-driven future we’re already living in. This means:
• Investing in creative development capabilities that work with AI systems, not against them
• Treating creative strategy as AI training data for targeting
• Implementing systematic creative testing frameworks that help machine learning algorithms learn faster
• Analyzing creative performance for both conversion impact and the audience quality AI delivers
The platforms have already moved beyond the targeting paradigm we’re still clinging to. AI has found a better way to match ads with audiences, and it runs through creative performance signals, not just audience parameters.
The question isn’t whether AI will continue to reshape targeting, but whether we’ll master this new reality or watch competitors pull ahead while we chase yesterday’s solutions. Creative is the new targeting. AI is the engine. The sooner we embrace this reality, the sooner we can all build sustainable competitive advantages.
