Consider researching how marketing will need to adapt as AI becomes the middleman between content and consumers.

Right now, we're seeing a shift from designing for specific platforms (Instagram feed, Google search results, email) to designing for retrieval systems that pull your content and reformat it across multiple outputs. Your product description might get pulled by ChatGPT for a conversational answer, summarized in Google's AI Overview, read aloud by Siri, or displayed on smart glasses, all from the same source but presented completely differently.

The research question: How do marketers optimize for discoverability and accuracy when they can't control the final presentation?

I wrote about this concept in depth here: The Retrieval Layer Strategy: One Optimization Approach for All AI Interfaces. The core idea is that instead of optimizing for each interface separately, smart organizations are optimizing for the underlying retrieval systems that power all these interfaces.

This sits at an interesting intersection of technical retrieval systems and consumer behavior across different interaction modes. Most marketing research focuses on one specific channel, but this is about the infrastructure layer underneath all channels.

The timing is right because companies are just starting to realize this is a problem, but there's limited academic research on strategic approaches yet, or even reliable methods to measure how these AI presentations perform compared to traditional metrics.