Ep 752: Using Job Architecture To Drive Value From AI

Skills, tasks, jobs, activities. These terms get used interchangeably across HR and talent acquisition, but they mean fundamentally different things. Skills are attributes of people. Tasks are components of work. Jobs are bundles of activities. 



Having clarity here matters more now than ever. As AI begins reshaping how work gets done, organisations need a precise understanding of their workforce at the task level. Without clear taxonomies, it becomes impossible to understand how to effectively implement AI for automation and augmentation. So how should companies be preparing to take the most advantage of the inevitable shifts AI will bring?



My guest this week is Ben Zweig (  / ben-zweig  ) , CEO of Revelio Labs (https://www.reveliolabs.com) and author of the new book Job Architecture. In our conversation, he explains how to build effective taxonomies cheaply and scalably with LLMs and why this foundation is critical for navigating change. Ben also teaches Data Science and The Future of Work at NYU Stern and talks through an invaluable framework for assessing the likelihood of AI-driven job displacement.



In the interview, we discuss:




• Why grouping people is the core of any HR analysis.





• What we get wrong about skills, jobs, tasks, and activities





• Why skills aren't the right unit of observation to analyse jobs





• AI automates tasks and activities, not jobs and skills.





• The vital importance of taxonomies





• Using LLMs to build taxonomies cost-effectively at scale.





• What are the advantages of doing this properly?





• The three forces that help measure the potential for AI-driven job displacement





• What does the future look like









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