Reports

AI consumer personas are reshaping segmentation, not just personalization

Marketers need to rethink segmentation as AI reshapes how consumers discover and decide

Patrecia Meliana

27 Mar 2026 • 6 min read

Klaviyo’s latest AI Consumer Personas Playbook introduces something more fundamental than a new way to personalize campaigns. It proposes a shift in how marketers should define and segment their audiences in an AI-driven landscape.

This article explores why traditional segmentation models are breaking down, how AI behavior is emerging as a new segmentation layer, and what this means for marketers trying to stay relevant as customer expectations evolve.

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Why traditional segmentation is breaking in the age of AI

Traditional segmentation assumes that customers with similar profiles behave in similar ways. AI is breaking that assumption.

The report shows a widening behavioral gap. Some consumers now rely heavily on AI tools for research and decision-making, while others avoid them entirely. That divergence creates fundamentally different paths to purchase.

For example:

96% of AI Holdouts don’t use AI when shopping, effectively opting out of AI-driven journeys
In contrast, AI Enthusiasts actively rely on AI and treat it as a decision-making partner. The report shows that 85% have used AI while shopping in the past six months, and 43% have already purchased a product based on AI recommendations.

This means two customers with identical demographics can behave completely differently depending on their relationship with AI.

The result is a segmentation blind spot. Traditional models cannot explain:

Why some users trust AI recommendations instantly
Why others validate every output before acting
Why certain audiences lose trust when AI is visible

Segmentation based on identity alone is no longer enough.

How AI consumer personas introduce a new segmentation model

Klaviyo’s framework organizes consumers into four personas: Enthusiasts, Evaluators, Skeptics, and Holdouts. But the real value lies in how these personas are constructed.

Klaviyo 2026 AI Consumer Trends - AI engagement map

They are based on two measurable dimensions:

Trust in AI
Frequency of AI usage

This creates a behavioral segmentation model that explains real-world differences in marketing performance.

AI Enthusiasts show strong positive sentiment:

81% say AI improves product recommendations
74% say it improves customer support
72% say it improves marketing relevance

At the same time, AI Skeptics show clear resistance:

Only 25% have purchased AI-recommended products
Just 19% trust those recommendations

And AI Holdouts go further:

Only 1-4% believe AI improves their experience
58% trust brands less when AI-generated content is used

But the most revealing group sits in the middle.

AI Evaluators use AI frequently, yet hesitate to rely on it:

54% are less likely than Enthusiasts to use AI for decision-making
74% feel neutral about AI-generated content
42% are unsure whether they can distinguish AI from human output

They don’t reject AI, but they don’t fully trust it either.

This is not a marginal difference. It is a segmentation-level divide. The same AI-powered campaign can perform exceptionally well with one group and fail completely with another.

What this shift means for modern marketing strategy

This shift has immediate implications for how marketing teams think about segmentation, targeting, and experience design.

Segmentation becomes dynamic and behavioral

Instead of grouping users by static attributes, marketers need to account for how customers interact with AI across the journey.

Personalization is no longer one-dimensional

The report highlights that personalization depth should align with trust levels. AI Enthusiasts respond well to predictive recommendations, while Skeptics and Holdouts require more restraint and transparency.

The customer journey itself is changing

The report notes that AI is effectively “splitting the funnel,” with discovery happening across LLMs, search engines, and traditional channels simultaneously. This makes it harder to rely on linear funnel assumptions.

Taken together, this suggests a broader shift: segmentation is no longer just about grouping audiences. It is about adapting experiences to different levels of AI comfort and expectation.

What marketers should do next

For marketers, the takeaway is not to abandon existing segmentation models, but to extend them.

Here are a few practical ways to start:

Layer AI behavior into your segmentation

Track signals such as AI-assisted discovery, interaction patterns, and response to automated content.

Adjust personalization depth, not just messaging

Move beyond “more personalization” and focus on the right level of AI involvement for each audience.

Test for trust, not just performance

Monitor negative signals such as disengagement, opt-outs, or reduced interaction with AI-driven experiences.

Optimize for AI-driven discovery

Ensure your brand is discoverable across AI tools, not just traditional search or paid channels.

Rethink content strategy for AI visibility

As AI tools become discovery engines, content needs to be structured and optimized for machine interpretation as well as human consumption.

Reduce volume, increase relevance

The report emphasizes that highly engaged AI users respond better to fewer, more relevant messages rather than generic high-frequency campaigns.

For teams that get this right, the opportunity is significant. Segmentation becomes more predictive, experiences become more relevant, and marketing becomes more aligned with how customers actually behave in an AI-first world.

AI is not just another channel or tool. It is reshaping how customers think, decide, and interact with brands.

Klaviyo’s AI consumer personas highlight a deeper shift: segmentation is moving from static identity-based models to dynamic, behavior-driven frameworks rooted in trust and usage. Marketers who adapt early will have a clear advantage. Those who don’t risk applying outdated models to a rapidly changing customer landscape.

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