Advertising has been sitting in a phase of prioritizing performance and, for decades, it has been hyper focused on efficiency, accurate targeting and optimization. AI promises to improve the ability to do all of this faster and smarter—but the perfection it creates is the true disruption AI brings.
Rising tides lift all boats, meaning performance is no longer the differentiator. We are ushering in the ability for every brand to hyper-target, smart segment, automate bidding, generate creative variations, use real-time analytic insights to pivot in an instant and on and on. This begs the question: what happens when efficiency is universal? Because if everything is high-performing, then who stands out?
Advertising is constantly iterating, but how we recalibrate is determined by understanding what is actually happening. The focus on AI used for execution is trivial because the real value swerve is actually found in those who ask the best questions, interpret insight in ways AI misses and who create something that resonates beyond a platform discovery algorithm.
The brand that will win in the future won’t be the ones chasing optimizations—but the ones who will be setting the new rules of winning.
The AI Loop Problem
AI powered platforms and one-off tools currently operate in a closed-loop learning system—a cycle where algorithms optimize based on past performance, then feed those results back into their training data. In theory, this creates a continuous improvement. In reality, it creates a system where AI starts learning from AI instead of humans.
In an AI-forward advertising ecosystem, brands are no longer optimized against competitors—they optimize into the same machine-learning patterns that every other brand is also feeding (unless they use a close environment, creating its own challenges). The entire system begins to flatten, and this can lead to at least three major consequences:
- Diminishing Role of Humanity
So much of the competitive advantage in advertising has come from human interpretation—the insights, the creativity and strategic nuance applied to consumer behavior. AI is set to strip this away for efficiency and speed. It was always the human intelligence that analyzed the why behind a trend whereas AI lacks nuance and functions to optimize based on what worked before. Consumer insights, once driven by emotional intelligence and cultural fluency, becomes mechanized pattern recognition. This risk pushes advertising further into using AI that simply cares about what an algorithm deems effective, not what actually builds long-term connection. - Automated Stagnation
Pattern recognition, which AI heavily rewards, is what leads us to lack of originality and resonance. If a particular aesthetic, call-to-action, or engagement tactic performs well, AI replicates it endlessly. Over time, creative risk-taking disappears, replaced by endless iterations of what has already worked—in essence, taking a lot of performance work today and magnifying it through AI. Instead of shaping culture, brands become trapped in a cycle of past performance. Every ad is optimized, yet indistinguishable. - Standardization of Success
When AI dictates what “success” looks like, every brand follows the same path. Every campaign is optimized toward the same conversion, awareness or consideration-based goals. If every brand is performing well, but no one is breaking out, then what does success even mean? Is it expected we simply throw dollars at a saturation play?
And this is why measurement has to evolve. Because in a world where AI ensures ads “work,” the real question isn’t just how well a campaign performs according to media metrics—it’s how deeply a brand embeds itself in consumer memory.
AI Doesn’t Create Demand—And That’s a Problem
AI doesn’t invent nor does it generate new desire or motivations—it merely amplifies what already exists. And that’s a problem when outside forces are actively reshaping consumer behavior.
Currently and dramatically, economic downturns, inflation, tariffs, and shifting consumer psychology all impact how and why people buy—and these are forces AI cannot anticipate and have no record of. When demand shrinks, AI doesn’t solve the problem. It just makes the fight for a shrinking audience more expensive.
A 2023 McKinsey study found that in uncertain economic conditions, consumers gravitate toward two dominant behaviors:
- Value-driven purchasing – Price sensitivity increases, meaning consumers default to cheaper options and essentials, recently seen in dupes and trading down patterns.
- Premium exclusivity – Wealthier consumers lean into “buy less, buy better,” favoring high-status purchases over mid-tier brands.
This creates a dangerous gap for brands without strong brand affinity. If they compete on price, they lose profitability. If they compete on status, they lack cultural credibility, or, the commonly used “authenticity” measure..
And this is where AI becomes a liability rather than a solution. Because if every brand is optimizing for performance, but the underlying demand has shifted, then AI is just making the competition for limited spending more expensive.
This is why brands must build long-term equity that exists beyond AI-optimized work.
Get Emotional About ROI
If efficiency is universal, AI is optimizing against itself, and AI doesn’t create demand, then how we measure success has to shift. Traditional performance KPIs—CTR, ROAS, CPA—are no longer indicators of competitive advantage. Every brand is optimizing at the same level. Every ad is “working.” The question isn’t whether an ad performs—it’s whether a brand is remembered.
Success in the AI era is only about efficiency when talking to your board or holding company, otherwise, it’s about brand influence. Brands that lead will be the ones that shape consumer behavior, not just respond to it. Measuring emotional impact, memory retention, and demand creation will be more predictive of long-term revenue than just tracking conversions and weekly ROAS.
New measurement models must go beyond engagement rates and start answering: Does the brand imprint? Does it spark conversation? Does it build demand? There are current methods being considered to keep efficiency in play but adjusting what ROI can mean across algorithms:
- Memory Retention and Recall:If efficiency is universal, then lasting mindshare is the differentiator. The brands consumers think about unprompted are the ones that win. Share of mind can be positively linked to share of wallet.
- Creative Distinctiveness or Novelty: AI-driven ads optimize for past patterns, leading to stagnation. Tracking how consumers respond to fresh, unexpected creative is critical to standing out. It is time for flexible brands that can be easily shaped by consumer preferences.
- Cultural Penetration and Talkability Momentum: The most valuable brands aren’t just seen… they are talked about. Measuring how often a brand enters or spawns organic conversations signals deep cultural traction. That culture will require being deeper reaching into niche spaces—something that has been spoken about ad nauseam but will only become more crucial.
- Sentiment and Trust:AI can’t generate demand—it only optimizes what exists. Consumer confidence and trust determine whether a brand gains pricing power, retains loyalty, and resists economic downturns.
Measuring emotion isn’t about “soft” metrics—it’s about understanding which brands consumers will actively choose in a world where AI dictates discovery in every digital space.
This is the shift: from measuring how efficiently creative and media perform to measuring how deeply a brand embeds itself in consumer psychology. This is an argument for a more complete view of ROI and to redefine what winning looks like.
For those who are able to become part of memory and part of choice—no algorithm can take that away.ROI in this landscape will be about staying relevant, staying trusted, and staying wanted.