Brands adapt marketing for AI agents and human buyers

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Brands adapt marketing for AI

Highlights

Marketers must optimize content for both AI agents and humans as AI increasingly evaluates, recommends, and purchases products before human interaction.
Newer companies can natively build brand systems for dual audiences, while established brands face costly rewrites to meet machine readability requirements.
Brands are advised to measure success by conversions and revenue, not content volume, as AI-driven shopping tools become more integral to purchasing decisions.

How brands can prepare for dual-audience marketing

As reported by Crunchbase News, marketers are facing a new commercial environment in which AI agents may evaluate, recommend and even buy products before a human customer reaches a product page. That changes how brands write, organize and test their marketing materials, especially as machine summaries and automated decision tools become more common in retail and digital channels.

The article says newer companies have an advantage because they can build brand systems with both human and machine readers in mind from the start. Established businesses, by contrast, may need to rework years of copy, internal rules and approval processes to make their content easier for AI systems to interpret without losing the brand's identity.

One practical step is to define a distinct brand voice in explicit written rules rather than relying on unwritten instincts. That includes documenting not only the language a brand should use, but also the words and claims it should avoid, helping both staff and AI tools produce content that stays within brand standards.

Product descriptions are also becoming more important because they can serve as the main source material AI agents use when comparing or recommending items. In that setting, a well-structured source description helps preserve a brand's positioning even if an agent later tailors the wording to different users.

Retail and performance implications

Brands are also being encouraged to test how their messages appear to both direct recipients and AI systems that summarize content first. Email is presented as a near-term example, with tools from providers including Gmail and Apple surfacing summaries that may shape a consumer's first impression before the full message is read.

That makes it more important for marketing teams to check whether promotions, differentiation and core brand traits remain visible in AI-generated summaries. The article argues that this is a low-cost habit companies can adopt now, even without deploying additional software.

The piece also warns against relying on output volume as a success measure as AI lowers the cost of content creation. Instead, brands are urged to focus on business outcomes such as conversions and revenue, while still accounting for the fact that human preferences remain central because AI agents ultimately act on behalf of users.

While the pace of AI-driven purchasing remains uncertain, the broader commercial implication is that companies that design for both audiences early may avoid more expensive changes later. That dynamic could be especially significant in retail and consumer marketing as automated buying and recommendation tools become more embedded in purchasing journeys.

In our earlier article, we examined how Ralph Lauren began tying part of executive annual payouts to AI literacy and enablement, reflecting rising pressure on retailers to show measurable returns from AI investment. We also noted that only a small share of public companies have formally added AI goals to incentive plans so far, even as retailers expand AI use across operations and customer-facing tools.

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