At this point, AI has matured into a core enabler across industries, advertising and marketing being one prime example, where the art of human connection demands constant innovation and adaptability. Thanks to rapid advancement of large language models (LLMs), marketers now stand at an inflection point: discriminative AI can not only crunch data at scale but generative AI can then simulate potential customers to refine campaigns with unprecedented speed and precision. Why does this matter for brands and agencies? And what does it mean as we speed ever closer to artificial general intelligence (AGI) – and someday, perhaps, artificial superintelligence (ASI)?
A World Beyond Traditional Segmentation
For decades, marketers have relied on segmentation to categorise customers by pre-selected demographic attributes like age, gender, or income. I have nothing against segmentation, it’s served us well and remains useful for broadly defining an audience. But it carries inherent limitations, because it starts with labels we choose. By bucketing people, traditional segmentation risks overlooking subtle psychographic or behavioral nuances that shape how real people think and act.
Enter clustering. With advanced, discriminative AI models capable of parsing everything from text-based surveys to anonymised customer data, marketers can identify naturally occurring audience groups without imposing rigid categories. This shift may seem subtle, but it’s transformative. Instead of asking, “Which demographic boxes do our customers fit into?”, the new question becomes, “What patterns exist within our data, and how can we use them to better understand and serve people?”
If doing a wedding seating plan, normally we plan tables out by family groups and friend circles, essentially bucketing people by demographics. But what if we took a different approach? At the reception drinks, we could let people mingle, find like minds and new friends and let them decide who they sit with. The wedding breakfast would have a different vibe as a result, probably better.
Clustering in 2025 has become more accessible than ever before. What was once the domain of PhD-level data scientists and prohibitively expensive platforms has evolved into manageable workflows for agencies and enterprises with robust AI partnerships. The result is a more organic, adaptive view of consumer audiences, one that can pivot quickly as market conditions, cultural trends, or brand strategies evolve.
Two Phases: Clustering and Custom Modeling
A new, two-phased approach to bring client audiences to life through AI is emerging:
Phase One: Clustering with Discriminative AI
It begins with a multi-step clustering workflow on anonymised audience data. Rather than bounding each cluster with preset labels, the algorithm detects inherent groupings, sometimes revealing unexpected “tribes” within a customer base. For instance, a travel company may discover an eco-savvy group of adventurers who prioritise sustainability above budget, or a retailer might unearth a trend-savvy segment devoted to local artisans. These insights often surprise stakeholders who initially assumed simpler divisions like “families vs. singles” or “luxury shoppers vs. discount hunters.”
The key advantage here is dynamism: clusters can update as new data arrives. That agility is vital in a market where consumer preferences and online behaviors can shift overnight.
Phase Two: Custom Models for Audience Simulation (“Silicon Sampling”)
Once clusters are identified, it’s possible to use clustered data as input for fine-tuned implementations of language models. These bring the different perspectives, motives, desires and worldviews of the clusters to life. This technique was originally dubbed “silicon sampling,” in academic research from 2022 to describe using AI as a proxy for human subgroups.
By prompting these models with relevant questions, creative concepts, or hypothetical scenarios, it’s possible to understand how each audience cluster might respond – similar to holding a focus group without scheduling any real-world participants. The applications are vast, here are some:
- Qualitative and Quantitative Analysis: Quickly gauge how a particular group feels about new product features or brand positioning, gathering iterative feedback in a fraction of the time it would take to field traditional surveys.
- Workshop Strategies: Need to brainstorm go-to-market approaches or messaging angles? Marketers can effectively “ask” each audience cluster for reactions or suggestions, sparking strategic ideas grounded in representative sentiment.
- Advertising Concept Testing: Instead of lengthy concept validation cycles, it’s now possible to run multiple ad scripts or visuals by these custom models, seeing which resonates best within each demographic or psychographic group.
- Mapping Customer Journeys: By asking the model to walk through a purchase decision or brand interaction, identify potential friction points and opportunities for personalisation.
- Ongoing Personalisation: This is the holy grail of marketing, dynamic messaging on a nearly individualised level. It’s very expensive to do but more possible as time passes. And because the clusters can be refreshed with updated data, these synthetic focus groups can evolve over time, making them a living source of audience insight.
This combination of agile clustering and near-instantaneous “conversation” with simulated audiences provides marketers with a powerful lens into real human complexity – while still ensuring data privacy by relying on anonymised, aggregated information.
The Road to AGI – And Beyond
As we push further into the 2020s, many experts, including big AI tech CEOs themselves, predict the imminent arrival of artificial general intelligence (AGI) – a system with reasoning and learning capabilities on par with human beings. The implications for marketing and consumer insights are enormous. An AGI could theoretically parse context, emotional subtext, and sociocultural nuance with fluidity far beyond our current models.
With the potential emergence of artificial superintelligence (ASI), the field might be revolutionised once again. Brands could harness real-time predictions of market sentiment across demographics, socio-political events, and even evolving cultural norms – potentially orchestrating hyper-personalised campaigns that adapt to individual consumer states on a moment’s notice.
In a near future where AI assistants increasingly manage our day-to-day lives and purchase on our behalf, it’s not hard to imagine that the silicon samples will no longer be just synthesised audiences but the audiences themselves. AI marketing to AI.
Yet these promises come with serious ethical considerations. Ultra-advanced AI, if left unchecked, might risk manipulative advertising that exploits subconscious vulnerabilities. This hypothetical future challenges marketers to develop a moral framework today, one that respects individual autonomy and remains transparent about data usage and AI-driven personalisation.
Ethical and Societal Considerations
While the move to AI-driven clusters and silicon sampling brings exciting opportunities, it also intensifies ethical and societal concerns:
Data Privacy and Consent
Even anonymised data requires rigorous safeguards. As models grow more powerful, the risk of re-identifying individuals from minimal cues increases. Responsible marketing demands robust data-protection policies, transparent data sourcing, and clear opt-in frameworks.
Bias and Representation
AI models learn from real-world data, which contain historical or systemic biases. If we rely too heavily on these models without human oversight, we risk perpetuating those biases in campaign design or audience interactions. Auditing model outputs for fairness and inclusivity is no longer optional, it’s an ethical imperative.
Transparency and Trust
As synthetic focus groups become more common, the line blurs between human insight and AI simulation. Do we owe it to consumers to disclose that part of our “conversation” occurs with a machine-trained proxy? Maintaining trust in customer relationships could hinge on striking the right balance between leveraging technology and respecting genuine human input.
Human Judgment Remains Paramount
Despite the sophistication of 2025-era AI, human strategists are crucial. Machines excel at pattern recognition, but empathy, moral reasoning, and cultural interpretation remain firmly in the human domain. The best results emerge when creative teams use AI tools as partners, not replacements.
In 2025, clustering and silicon sampling have redefined how marketers gain insight into consumer behavior. By tapping into advanced LLM’s, we can instantly “meet” our audiences, workshop strategies, and refine campaigns with a level of depth and agility unimaginable even three years ago. As AI heads toward AGI and, perhaps one day, ASI, our industry stands at the threshold of an even more radical transformation – one that demands caution, creativity, and a commitment to ethical standards.
Ultimately, the most resonant campaigns are those that combine data-driven technology with genuine empathy and curiosity for real human experiences. AI can bring our audiences to life, but it is our responsibility to guide these tools with integrity, ensuring that our quest for relevance and growth also upholds the trust and well-being of the people we serve.
