Technical writing teams across industries are quietly revolutionizing how they prepare content for AI integration—but these innovations remain largely undocumented and untested.

This presentation shares early findings from an ongoing research study investigating documentation strategies that technical writers are developing to optimize content for AI model consumption. Through ethnographic interviews with technical writing professionals across diverse industries, we're creating the first comprehensive catalog of these emerging practices.

While organizations experiment with everything from structured content formats to prompt-ready documentation, competitive concerns keep effective techniques siloed while less effective approaches proliferate unchecked. This research serves as a neutral academic intermediary to collect and systematically document these strategies.

Key insights include:

• A taxonomic framework for categorizing AI-preparation techniques based on implementation complexity and intended applications
• Patterns emerging across different industries and organizational contexts
• Early identification of variables that determine strategy effectiveness
• Practical protocols for testing and validating documentation approaches

What attendees will gain:

• A systematic understanding of current AI-ready documentation practices
• Frameworks for evaluating and selecting appropriate strategies for their contexts
• Insights into how documentation structure influences AI performance
• Actionable approaches for preparing content that works for both human readers and AI systems

This session bridges the critical gap between rapidly evolving industry practices and academic understanding, offering content strategists proven frameworks rather than experimental guesswork.