AI adoption is often treated as a technology challenge: choose the right tools, train people to use them, and drive implementation. Dr. Gleb Tsipursky argues that this framing misses the harder problem. Unlike previous technology shifts, AI can trigger fear about job security, challenge professional identity, and create social stigma around how work gets done. For organizations trying to move from experimentation to meaningful business impact, understanding those human dynamics may matter as much as the technology itself.

In this episode of Partnering Leadership, Gleb Tsipursky joins Mahan Tavakoli to discuss his new book, The Psychology of AI Adoption at Work: From Resistance to Results. Drawing on behavioral science research, thousands of survey responses, focus groups, and more than 100 consulting engagements, Gleb explains why traditional approaches to technology adoption often fall short with AI. The conversation examines the very different reasons employees resist, quietly embrace, or even hide their use of AI, and what those behaviors reveal about organizational culture.

The discussion moves beyond adoption to a deeper question facing CEOs and senior executives: what happens when AI changes not only how quickly work gets done, but also how people understand the value they bring? Gleb explores professional identity, shadow AI, the critical role of middle managers, and why organizations may need to rethink where they begin their AI efforts. He also challenges the instinct to start by automating the highest-value activities, offering a counterintuitive approach designed to build trust, experimentation, and broader adoption.

Mahan and Gleb also explore a growing leadership challenge: AI can dramatically increase output while making judgment more important. If AI increasingly generates reports, analysis, presentations, and other knowledge work, the differentiating human capability may shift from producing the first draft to evaluating whether the output is actually good. That raises important questions about how organizations develop early-career talent, redesign onboarding and apprenticeships, and build judgment when AI performs more of the work through which judgment was traditionally developed.

Finally, the conversation looks ahead to an emerging workplace in which employees may increasingly manage AI agents rather than perform individual tasks themselves. Managers, in turn, may need to manage people who manage agents while coordinating increasingly complex human-machine workflows. The result is a conversation not simply about getting employees to use AI, but about the capabilities, culture, management practices, and continual learning organizations will need as AI becomes embedded in how work gets done.

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