---
title: "Marketing to two audiences: What AI reads and what people rememberr"
description: Explore how marketers can engage both AI and human audiences by balancing clear information with compelling storytelling to enhance brand visibility and connection.
image: https://blog.olsonzaltman.com/hubfs/abeb60cf-cdc0-48e4-8a20-32e686ae5592.jpeg
---

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# Marketing to two audiences: What AI reads and what people rememberr

![Kacie Burton](https://7528315.fs1.hubspotusercontent-na1.net/hub/7528315/hubfs/raw_assets/public/mV0_d-cms-elevate-theme_hubspot/elevate/images/avatar-placeholder.jpg?width=48&height=48&name=avatar-placeholder.jpg)

 Kacie Burton

October 19, 2026

Marketing to two audiences: What AI reads and what people rememberr

3:02

The integration of AI technology into everyday life continues to reshape the commercial landscape, presenting new challenges for marketers.

 

McKinsey states that [more than 50 percent](https://www.mckinsey.com/capabilities/growth-marketing-and-sales/our-insights/next-in-growth/the-real-signals-from-cannes-lions-2026) of consumers’ paths to purchase are already influenced by AI. That stat will likely grow as new AI products and services become more widely available. Shoppers are increasingly relying on LLMs and agents for research and discovery.

 

This means that brand teams and marketers are tasked with figuring out how to get their products to show up as AI recommendations while still maintaining traditional efforts to appeal to the human consumer. The issue is that humans and AI prefer very different types of marketing.

 

[Research](https://www.jellyfish.com/en-us/blog/can-creativity-win-minds-and-models/) conducted by digital marketing agency Jellyfish and business school INSEAD found that LLMs review an advertisement systematically, preferring product description, context, and clear hierarchy and rankings. Humans, on the other hand, resonate more with ads that follow a narrative structure, deliver an emotional payoff, and creatively disrupt expectations.

 

In short, people respond to stories, while AI models respond to well-organized information.

 

That first half is the part we'd underline. We define meaning as the stories people tell to make sense of their experiences; everything else is just what happened. A product description gives an AI model the facts of what happened. The story is what makes a person remember a brand and reach for it again.

 

Kantar research, reported by WARC, [reached the same conclusion](https://www.warc.com/en/feed/humans-and-machines-see-advertising-very-differently-kantar-2ed54a8df1da45df8692e0b483f504f7). WARC refers to this as the “two-audience problem." It's also worth remembering who is typing the prompt. An AI agent shops on someone's behalf, and the request it receives ("find me a stroller that's safe but doesn't look like a tank") carries that person's priorities and worries. Brands that understand those deeper goals will have a better sense of what people are actually asking their agents for.

 

Marketers need to figure out how to appeal to both parties. The researchers agree that brand strength is essential for both high LLM visibility and human connection. Strong distinctive assets help brands stand apart from competitors, which in turn makes AI models more likely to prefer them.

 

Brand strength is a slippery phrase, though. Distinctive assets work because they stand for something in people's minds, such as a memory or a sense of who the brand is for. Those associations are mostly unconscious, which is why consumers struggle to explain them when you ask. Building a brand that holds up with both audiences starts with understanding what it already means to the people who buy it, and AI can't do that work on a brand's behalf.

 

We agree that brand-building is about to get more attention. Begin that investment by learning what your brand means to people, before deciding how to describe it to a machine. If you're working through what that looks like in your category, we'd be glad to talk.

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