UX research practices matter more than ever in the age of AI slop. AI content tools produce generic output because nobody has told them what "good" means for your organization, and getting at that is exactly what UX researchers are trained to do.

Interviews, comparative evaluation, rubric design: your existing toolkit, applied to a new and urgent problem. The organizations that get this right will outcompete the ones still generating slop.

Prerequisite

No technical prerequisites. Attendees will get the most value if they work with web content in some capacity: content strategists, UX researchers, marketers, editorial leads, or Drupal site owners thinking about AI content tools.

Basic experience with brand writing guidelines, editorial style guides, or content strategy work is helpful but not required, and will let you apply the session's methods faster to your own team.

Target Audience

Content strategists, editorial leads, UX researchers, marketers, brand managers, and Drupal site owners responsible for content quality across multiple sites or contributors, especially those evaluating or already using AI content tools.

Outline

The problem

Your team's real quality standards aren't the documented ones. The brand PDF nobody opens. The style guide written ten years ago. The vague "be professional, be clear" that nobody can act on. Meanwhile the team ships content daily using tacit knowledge: the unwritten rules the editor with good taste has worked out over years. When those rules stay in people's heads, every decision is a negotiation, onboarding is slow, and stakeholders disagree in reviews without realizing they're applying different criteria.

The method

This is a content strategy problem, and UX research methods are the best way to solve it. We'll walk through how to extract tacit quality knowledge and encode it into structured evaluation criteria using stakeholder interviews, comparative page scoring, and collaborative rubric design. Examples come from real engagements with university & B2B content teams managing content at scale.

The payoff: Aligned Teams + Human-first AI

Encoded standards give your team a shared reference point for collaboration. They also give AI exactly what it needs to be useful.

For Drupal teams, the output plugs into the Context Control Center, feeding Canvas AI for content creation and AI Review for sitewide audits. Do the work for humans first and the AI benefits come with it: higher-quality content, and clear visibility into what existing content needs fixing.

Learning Objectives

1. Recognize the gap between documented content standards and the unspoken rules teams actually follow day to day
2. Techniques and templates for capturing tone and voice
3. How to use UX research methods to build better content guidelines
4. Templates and structures for organizing brand rules to use in the AI Context Control Center

Experience level
Intermediate