The Context Problem: Why Most Brands Are Using AI Wrong

Managing the rapid evolution of AI tools in retail media requires balancing technical experimentation with strategic context. While Model Context Protocol (MCP) and other AI integrations offer powerful ways to manipulate data and create custom reporting, they demand rigid architectural guardrails to ensure client data privacy and prevent context window exhaustion. Moving beyond surface-level metrics like ROAS or ACOS, agencies must leverage these tools to uncover deeper business challenges, such as shareholder pressure or household penetration goals. Industry expert Dustin Wassner emphasizes that successful implementation shifts the agency role from a task-oriented service provider to a strategic partner. By prioritizing specific, context-rich data, teams can deliver actionable insights that address unique brand needs rather than relying on generic, one-size-fits-all reporting templates.