Designing Customer Journeys for People and AI Agents: A Dual Approach to Engagement

Customer interactions with brands are evolving as AI agents increasingly influence decision-making. Erik Yeh highlights that “Agents are not just another channel. They are becoming gatekeepers in the decision-making process,” signaling a shift marketers must address. This approach requires balancing content that resonates with people while structuring data for AI agents to interpret and use effectively.

Rethinking Customer Journey Design

AI agents function as intermediaries, gathering, filtering, and sometimes making purchasing decisions for users. This demands a dual-path approach: one that appeals to human emotions and decision-making, and another that enables AI to parse and act on information efficiently. Structured data formats like schema markup help AI understand context and relevance, while clear, logical content supports human engagement.

Harmonizing Human Experience with AI Interpretation

Designing journeys for both humans and AI demands a nuanced approach. Humans engage through emotions, storytelling, and intuitive navigation, while AI relies on structured data and clear signals. Content must be rich and accessible—addressing human needs while formatted for AI processing.

Structured data, such as schema markup, provides AI with context about products, services, and user intent, enabling accurate recommendations. Yeh’s insight that AI agents act as gatekeepers underscores their growing role in filtering and prioritizing content. Ignoring this shift risks losing visibility as AI increasingly mediates customer interactions.

The Role of Structured Data and Agent-Friendly Content

AI agents rely on well-organized, machine-readable information to perform effectively. Without structured data, compelling human-facing content risks being overlooked by these digital gatekeepers. Brands must create content that combines emotional appeal with data precision, using standardized formats to make key attributes accessible to AI.

Content should anticipate the questions AI agents might ask, such as product features, pricing, and availability, providing clear answers within both the content and metadata. This clarity helps AI systems filter and rank options accurately, influencing which products and services reach human decision-makers.

Practical Considerations for Marketers

Technical Implementation: Add schema markup for products, offers, aggregateRating, availability, shippingDetails, and FAQ. Use JSON-LD and validate with tools like Google’s Rich Results Test. Prioritize fields that agents frequently use: price, availability, delivery options, return policy, and unique selling points.

Content Strategy: Craft narratives that resonate with human visitors while maintaining logical structure for AI. Avoid ambiguity and ensure content communicates the brand’s value proposition effectively. Create “agent-friendly” representations of key propositions in structured snippets embedded on the page.

Audit Checklist: (1) Are product attributes complete and machine-readable? (2) Is pricing and availability accurate and up to date? (3) Are delivery rules and thresholds clearly marked in structured data? (4) Do landing pages include authority signals and social proof both in visible copy and schema? (5) Are FAQ sections present and marked up for conversational agents?

Testing and Measurement

Traditional metrics like page views and conversions remain relevant, but expand tracking to include voice query performance, AI-recommendation placements, and agent-driven referrals where possible. Use A/B testing for structured data variations and monitor how AI-driven placements change click-through and conversion rates. Conversational search testing—posing queries to chat and voice assistants—helps uncover gaps in how agents interpret your content.

Analysis and Implications

Designing for both humans and agents is not an either-or decision. Brands that excel will blend emotional storytelling with technical precision. Early adopters who prioritize structured completeness and clarity will gain visibility as agents increasingly shortlist and recommend options before humans engage directly.

Start cross-functional projects to map dual journeys: involve content, SEO, engineering, and product teams. Treat agents as another audience with measurable requirements, and update KPIs to reflect agent-influenced outcomes.

Conclusion

The convergence of human and agent journeys creates new demands and opportunities. By pairing compelling, human-centered content with rigorous structured data and clear value propositions, brands can stay visible and relevant as AI agents reshape the path to purchase. As Erik Yeh notes, “Agents are not just another channel. They are becoming gatekeepers in the decision-making process.” (Source: Search Engine Land)

Original article: https://searchengineland.com/your-next-customer-might-not-be-human-designing-journeys-for-people-and-ai-agents-466459