What if one of the most basic premises in marketing—that our audience is human—is about to be fundamentally disrupted?

In the era of agentic AI, purchasing decisions will increasingly be made by machines. Brands that don't adapt risk becoming unseen—and ultimately obsolete.

Machines don’t shop like humans do. They don’t scroll through influencer posts, form emotional attachments from past experiences, or make spontaneous purchases based on persuasive language. Instead, they rely on logic, rules, and data signals to make choices.

As research begins to uncover how AI agents make buying decisions, a new picture emerges. It suggests that traditional brand strategies—such as appealing to lifestyle dreams, aligning with consumer values, or using emotionally driven visuals—may no longer carry the same weight.

This presents marketers with yet another shift in the landscape, where decisions are driven not by emotion but by algorithms. Let’s explore what this could mean for the future of commerce.

Rewriting The Rulebook

Marketing has long focused on building trust and emotional bonds with consumers. This includes crafting brand images, shaping perceptions, and delivering memorable experiences.

However, AI agents—far more advanced than today's chatbots—interpret brands in an entirely different way.

Salesforce reports that 24% of consumers are open to letting AI agents handle their shopping, a number that jumps to 32% among Gen Z.

These agents interpret products as structured data: price comparisons, feature lists, review ratings, and other machine-readable inputs.

They can scan social media sentiment, but unless that information is digestible and fits known patterns, it's unclear how much influence it will have.

Take household goods, for example—a category many people are comfortable delegating to AI. Would an agent selecting deodorant care about decades of brand storytelling and lifestyle marketing?

We don’t have all the answers yet. But early signs suggest that current models, such as OpenAI's Operator, respond well to ads rich in structured data.

Selling To Machines

To fully understand this transformation, we must address both technical and behavioral questions. On the technical side, we need to better grasp how AI agents process and weigh purchasing signals.

We also need clarity on how concepts like preference, ethics, and trust will be interpreted by these digital representatives. These agents will likely operate under constraints set by users, such as budget limits or ethical guidelines.

Unlike humans, AI doesn’t feel fear, greed, or FOMO. It won’t be swayed by clever branding or nostalgic memories. However, it may react differently to poor service—like ignoring a slow website rather than feeling annoyed.

It's also possible that sophisticated AI tools might hide their artificial nature. For instance, Operator can interact via email instead of APIs, mimicking human behavior. Marketing systems may need to detect whether engagement comes from a person or a bot to respond appropriately.

Moreover, more transactions may occur entirely between machines. Marketers must figure out how their systems can effectively "pitch" to these agents while maintaining transparency and consumer trust.

Smart Brands Will Do This Next

Over the next year, companies should assess how agentic search affects their SEO and paid search strategies.

Google took over a decade to reach one billion users. ChatGPT is expected to hit that mark in just three years.

Forward-looking businesses should already be experimenting with this emerging channel—to engage early adopters and gain insight into its potential.

Beyond that, brands must consider how to encode trust and reputation indicators into their messaging in ways that resonate with AI.

This could involve formatting product details into machine-readable structures, offering real-time feeds (like pricing and stock levels), and enabling broad API access to services and inventory.

Importantly, this shift requires collaboration beyond marketing departments. Data, product, and digital teams must ensure that when an AI agent evaluates a company, the message is consistent and clear.

It may sound complex, and it will certainly require strategic planning. But those who delay risk falling behind the wave of early-adopting competitors.

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