Designing the decision layer: A rich career path for systems-oriented content designers

As AI systems become core to product development, content designers who have always gravitated towards systems are finding their leverage point by moving upstream: from refining surface-level outputs with product designers to shaping structures with ML engineers directly.

This talk explores one emerging path within our evolving discipline: a systems-oriented evolution of content design for practitioners who have always gravitated toward engineering collaboration, taxonomy, IA and design systems thinking.

Rather than focusing on how to use AI to write microcopy or error states, we’ll examine how content designers can shape the “decision layer” of AI systems: defining structured inputs and outputs, building reusable JSON repositories, and creating human-in-the-loop evaluation frameworks that influence model behavior. Oh, and how to convince leadership it’s a good use of your time and skills.

Attendees will leave with a clearer understanding of this specialization path and concrete action plans to propose considered system-level design work within their own organization.

In this session, you’ll learn how to:

• Identify where content designers can influence AI system architecture deep beneath the surface (including schema design, structured outputs, failure modes, and evaluation frameworks).
• Apply a practical collaboration framework for working with ML engineers, including how to define structured contracts (inputs/outputs), separate extraction from ranking and presentation, and design reusable JSON repositories that scale across teams.
• Recognize and navigate the psychological barriers that prevent designers from stepping into technical collaboration, and articulate the value of structured semantic design as infrastructure within their own org.