Log in or create a free Rosenverse account to watch this video.
Log in Create free account100s of community videos are available to free members. Conference talks are generally available to Gold members.
Summary
Artificial Intelligence (AI) tools like large language models present opportunities — and risks — for people working with digital content. Can AI help with tedious tasks, like encoding and categorizing documents, or rewriting text snippets? Will AI be so good at these tasks that Information Architects (IAs) are no longer needed? In this session, Jeff and Karen provide an overview of what "AI" means for IAs, explaining the differences between natural language processing, machine learning, and large language models. They also dig into a real-world example of using different systems to categorize web content from Reddit. IAs will come away from the session reassured that the robots pose no threat to their jobs.
Key Insights
-
•
AI can aid in categorizing content but requires human expertise to guide it effectively.
-
•
Clear and specific tasks yield better results when using AI tools.
-
•
Natural Language Processing alone cannot fully grasp the context and nuance in language.
-
•
The quality of the training data significantly impacts the AI's categorization accuracy.
-
•
Collaboration between AI and information architects enhances the validity of outputs.
-
•
Proximity search with text embeddings can be a faster alternative for categorization tasks.
-
•
AI tools should augment existing processes rather than fully automate them.
-
•
Iterative testing and validation are crucial in assessing AI-generated categorizations.
-
•
A balanced view of AI's potential includes recognizing its limitations and carbon footprint.
-
•
Understanding the different AI toolkits helps prioritize their application effectively.
Notable Quotes
"We've been crossing paths on various projects since like '07."
"AI is a family of techniques that can work with lots of data."
"Natural language processing basically figures out how to math words."
"Categorization systems can't just rely on AI; they need human insight too."
"With AI, clear and specific instructions are key to effective results."
"The robots are not here to take our jobs; they're here to work alongside us."
"The extent of AI's utility is limited to the quality of its training data."
"Simply automating a process does not guarantee accuracy or relevance."
"Visualizations are essential for understanding AI's categorization outputs."
"Information architects are more valuable than ever in the age of AI."
More Videos
"AI should support real human research, not replace it entirely."
Noz UrbinaRapid AI-powered UX (RAUX): A framework for empowering human designers
May 1, 2025
"These same rules of engagement apply everywhere."
Jim KalbachJazz Improvisation as a Model for Team Collaboration
June 4, 2019
"You can use this to define who you are going to hire next."
Holly ColeUnderstanding Experiences: When you have to do more than work
November 8, 2018
"When we utilize merge we can fully solve this problem."
Jack BeharHow to Build Prototypes that Behave like an End-Product
December 6, 2022
"Today’s theme is practical design ops, as I will talk about in just a moment."
Bria AlexanderOpening Remarks
October 3, 2023
"We had to look outside and ask if we were ready for AI, as the world was moving on without us."
Erika FlowersAI-Readiness: Preparing NASA for a Data-Driven, Agile Future
June 10, 2025
"Data is not just numbers; it tells us the story of student learning."
Kristin SkinnerFive Years of DesignOps
September 29, 2021
"Without these rules and these conventions, we wouldn't have the creativity on top of it."
Jim KalbachJazz Improvisation as a Model for Team Collaboration
November 6, 2017
"Not everyone needs to be a designer; people can be design thinkers."
Surya VankaUnleashing Swarm Creativity to Solve Enterprise Challenges
June 10, 2021
