The End of One Model, the Beginning of Another
For more than 20 years, digital advertising was built on a powerful yet invisible backbone: third-party cookies. These trackers allowed advertisers to follow users across sites, refine targeting, personalize messages—and automate the entire programmatic system.
But that era is coming to an end. Browsers are blocking them. Users are rejecting them. Regulators are restricting them. In short: the model is collapsing. The real question is—what comes next?
In February 2024, we interviewed Thomas Spitz, a recognized expert at the intersection of AI, data, and marketing. His message was clear: artificial intelligence is no longer a peripheral innovation—it’s becoming a strategic backbone:
- Predictive targeting without identifiers
- Creative personalization at scale
- Real-time behavioural analysis
- Even autonomous ad content generation
As he put it: “AI doesn’t replace cookies—it redefines the entire framework.”
Could this be the real post-cookie revolution? Not a return to intrusive or imprecise advertising, but the opportunity to build an ecosystem that is smarter, more ethical, and more effective.
So, can AI truly become a sustainable alternative to third-party cookies? That’s what we’ll explore in this article.
What Third-Party Cookies Enabled—And What Happens Without Them
For two decades, third-party cookies quietly supported highly targeted, measurable advertising. They enabled:
- Cross-site tracking: following users across multiple sites to reveal interests, purchase intent, and behaviours.
- Retargeting: serving ads to users who had already shown interest in a product or service.
- Multi-touch attribution: mapping the entire customer journey and attributing credit across touchpoints.
- Performance optimization: providing continuous data to refine campaigns and maximize ROI.
Without cookies, the consequences are immediate:
- Loss of visibility into customer journeys
- Data fragmentation across publishers and platforms
- Difficulty proving campaign ROI without reliable measurement
In response, alternative approaches are emerging:
- Contextual targeting – focusing on the environment, not the individual
- First-party data – brands building their own datasets via subscriptions, purchases, and logins
- Alternative IDs – solutions like Unified ID 2.0 or RampID, offering anonymized, privacy-respecting identifiers
These are helpful, but none yet replicate the precision and power of cookie-based targeting.
What AI Already Delivers (and Why It’s Promising)
Loin d’être une solution à long terme, l’intelligence artificielle est déjà à l’œuvre dans l’industrie publicitaire. Elle comble, en partie, les vides laissés par la disparition des cookies tiers, en proposant de nouveaux leviers d’action plus respectueux de la vie privée et souvent plus performants.
Predictive Targeting
Rather than following an individual over time, AI analyses contextual signals in the moment—the content being read, time of day, device, location—to predict likelihood of engagement.
Through natural language processing (NLP) and supervised machine learning, AI models identify the contexts most associated with specific behaviours (e.g., a user reading smartphone reviews on a Saturday evening is highly likely to purchase soon).
This allows relevant ads to be served without unique identifiers—meeting privacy standards while keeping performance high.
Dynamic Personalization
AI transforms ad creative by generating and adapting ads in real time based on context.
For example: promoting a cold beverage during a heat wave—without needing to know the user’s identity.
Formats, visuals, and calls-to-action are adapted instantly, keeping ads relevant without intrusive data collection.
This aligns with regulations like Québec’s Law 25, which requires transparency, consent, and responsible governance of personal data.
Advanced Segmentation
Traditional segmentation (age, gender, location) is limited. AI enables:
- Clustering: grouping audiences with similar behaviours and contexts, without identifiers.
- Lookalike modelling without cookies: building new audiences based on behavioural patterns observed on platforms.
This maintains acquisition potential while protecting privacy.
Smarter Measurement
Without individual trackers, measuring campaign effectiveness is harder—but AI provides alternatives:
- Probabilistic attribution / Marketing Mix Modelling: assessing the overall contribution of channels to conversions.
- Identifier-free performance tracking: using statistical correlations, anonymized panels, and causal modelling.
These methods are less granular but more robust, and fully compliant with data protection requirements.
What It Takes for AI to Work
AI is not a plug-and-play solution. For it to succeed as a post-cookie alternative, four foundations are essential:
- Reliable First-Party Data
Structured, clean, up-to-date data from owned sources (websites, apps, CRM, e-commerce). - Robust Technical Infrastructure
APIs, interoperable programmatic platforms (DSP, DCO, CDP), and scalable cloud environments to process real-time signals. - Transparent AI Models
Trained on diverse datasets, tested through A/B or backtests, auditable to detect bias and explain decisions. - Strict Regulatory Compliance
Privacy by design: explicit consent, right to erasure, data minimization, auditability.
AI vs. Cookies: A True Alternative?
AI doesn’t replicate cookies—it offers something new.
Instead of tracking individuals over time, AI interprets real-time signals, contexts, and probabilities. Campaigns shift from chasing users across the web to understanding moments of intent.
This means ads become:
- More relevant (contextual, real-time)
- More respectful (no hidden surveillance)
- More adaptable (fitting context and timing)
But it also requires:
- Fine-tuned data governance
- Transparent, explainable models
- Brands capable of interpreting and acting on new insights
AI, then, is not just an alternative—it’s a paradigm shift.
Conclusion: A Chance to Do Better
The end of cookies isn’t just a technical challenge—it’s a chance to rebuild digital advertising on healthier foundations.
For brands, this means moving away from over-reliance on intrusive data collection, and embracing strategies that are contextual, flexible, and respectful.
As Thomas Spitz told us: “AI doesn’t replace cookies—it restores meaning to how brands connect with their audiences.”
At NÜ Programmatic, we help brands rethink their post-cookie strategies with responsible, AI-driven solutions that are both high-performing and ethical.
