AI at Work: Role Expansion, Intensification, and the Entry-Level Problem

An eight-month field study inside a 200-person U.S. tech company, published in Harvard Business Review, found three consistent patterns when generative AI is adopted at work: task scope expands, work bleeds across time boundaries, and multitasking rises. The authors framed the results as surprising. Dr. Gleb Tsipursky, CEO of AI consultancy Disaster Avoidance Experts, disagrees and explains why organizations that treat these patterns as unexpected will mishandle them.

The core mechanism is straightforward: when AI absorbs routine, low-level work, employees shift upward into judgment, synthesis, and cross-functional coordination. Meeting notes and first drafts stop consuming prime attention. Analysts become translators between functions. Engineers spend less time on boilerplate and more time setting direction and mentoring. This broader scope effect is not a bug; it is the intended outcome.

Key takeaways:

• The intensification risk is real but manageable. When organizations treat the initial productivity surge as a permanent baseline without protecting recovery time, wellbeing erodes. The fix is operational: establish guardrails early that distinguish value-adding expansion from pure motion.
• The sharpest harm from AI adoption falls at the entry level. A Stanford Digital Economy Lab analysis found that workers aged 22-25 in AI-exposed roles saw a 16% relative decline in employment since widespread adoption. Revelio Labs separately found an estimated 11% drop in entry-level demand for every 10-point increase in AI exposure.
• Organizations that erase starter tasks without redesigning entry paths risk a workforce shaped like a ladder with missing lower rungs, efficient today but talent-starved in five years.
• The solution: apprenticeship-style rotations, supervised AI-first workstreams, and explicit mentoring to recreate the training function that routine work used to provide.

Read the full article on Patheos