As users delegate tasks to AI agents, a new targeting framework emerges. Instead of targeting based on keywords or past behavior, brands can target users based on the specific task they are trying to accomplish (e.g., "write a report," "plan a trip"). This allows for hyper-relevant, solution-oriented advertising.
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As consumers delegate purchasing to personal AI agents, marketing's emotional appeals will fail. Brands must prepare for a "Business-to-Machine" (B2M) world where algorithms evaluate products on function and data, rendering decades of psychological tactics obsolete.
The audience for marketing content is expanding to include AI agents. Websites, for example, will need to be optimized not just for human users but also for AI crawlers that surface information in answer engines. This requires a fundamental shift in how marketers think about content structure and metadata.
Previously, marketers told Meta who to target. With the new AI algorithm, marketers provide diverse creative, and the AI uses that creative to find the right audience. Targeting control has shifted from human to machine, fundamentally changing how ads are built and optimized.
Instead of batching users into lists for A/B tests, AI can analyze each individual's complete behavioral history in real-time. It then deploys a uniquely bespoke message at the optimal moment for that single user, a level of personalization that makes static segmentation primitive by comparison.
Modern marketing relevance requires moving beyond traditional demographic segments. The focus should be on real-time signals of customer intent, like clicks and searches. This reframes the customer from a static identity to a dynamic one, enabling more timely and relevant engagement.
Analyst Eric Sufert predicts OpenAI's ad model will not be anchored to the content of a user's query, which could compromise trust in the answer's objectivity. Instead, it will function like Instagram's feed, where ads are targeted based on a user's broader conversion history, independent of the immediate conversational context.
The evolution of search won't stop with LLMs. The next stage involves autonomous AI agents that complete tasks like booking travel on a user's behalf. Marketers must shift their focus from answering human queries to ensuring their products and services are discoverable and selectable by these agents.
The goal for advertising in AI shouldn't just be to avoid disruption. The aim is to create ads so valuable and helpful that users would prefer the experience *with* the ads. This shifts the focus from simple relevance to actively enhancing the user's task or solving their immediate problem.
The rise of AI agents means website traffic will increasingly be non-human. B2B marketers must rethink their playbooks to optimize for how AI models interpret and surface their content, a practice emerging as "AI Engine Optimization" (AEO), as agents become the primary researchers.
As AI agents and synthesized search become intermediaries, traditional channels are insufficient. The new imperative is ensuring your brand’s data is accessible to AI models as they reason and generate responses, directly influencing the outcome before it reaches the consumer.
