#188 - Why AI Can't Replace Qualitative Research with Sam Ladner of Workday

Apr 28, 2026

Sam Ladner, sociologist and senior principal researcher who studies the future of work, explains where AI truly helps and where it falls short. She discusses AI for transcription and closed coding. She highlights emotional, embodied moments AI cannot grasp. She covers handling outliers, designing AI-friendly codebooks, and when to keep humans in the loop for imagination and storytelling.

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INSIGHT

AI Is Good at Labor Not Sense Making

AI is great at labor-saving tasks like transcription and boring coding, which frees researchers to focus on deeper sense-making.
Sam Ladner calls qualitative research “unriddling,” arguing machines are correlation machines while humans do abductive, emotional interpretation.

INSIGHT

AI Can't Sense Emotional Human Moments

Emotional, embodied moments in research cannot be interpreted by AI because it lacks feeling, a body, and moral care.
Sam warns that AI will miss or flatten participant emotion and might report what stakeholders want to hear instead.

INSIGHT

Explain Every Outlier In Qualitative Work

Qualitative research must explain outliers rather than discard them as in quantitative work.
Sam emphasizes that if your interpretation doesn't account for the one very negative participant, the research is incomplete.

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Erin May sits down with Sam Ladner, Senior Principal Researcher of Strategy at Workday, to explore the evolving role of AI in qualitative research. Sam brings a refreshingly balanced perspective on where AI can genuinely help researchers and where it fundamentally cannot replace human insight.

Sam explains how AI has transformed labor-intensive tasks like transcription and closed coding, freeing researchers to focus on the deeper work of sense-making and understanding outliers. She emphasizes that while AI excels at mathematical correlation hunting, qualitative research is about unriddling complex human experiences that require thinking, feeling, and imagination. The conversation covers practical applications like using MAXQDA for AI-assisted coding, the importance of explaining every outlier in qualitative work, and why emotional storytelling must remain exclusively human territory.

Highlights

03:09 Why machines can't replace human sense-making
08:47 Human moments AI cannot understand
12:00 Explaining outliers in qualitative coding
16:27 Building effective coding systems for AI
21:33 Getting AI to do closed coding properly
29:26 When to use AI for low stakes research
37:23 AI cannot substitute for qualitative research

About Sam
Sam Ladner (she/her) is a sociologist who helps teams innovate, design, and learn. She is the author of Practical Ethnography: A Guide to Doing Ethnography in The Private Sector and Mixed Methods: A Short Guide to Applied Mixed Methods Research. She has worked on dozens of advanced software projects at Microsoft, Amazon, and most recently Workday, where she worked as a Senior Principal Researcher studying the future of work. She is now an independent researcher and consultant, writing her third book, tentatively titled Practical Foresight: Strategic Foresight in Applied Settings. She received her PhD in sociology from York University and lives in Toronto, Ontario, Canada.

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