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.
Actionable Takeaways
- You’ll learn why AI adoption creates psychological challenges that traditional technology implementations often do not, and why treating AI like another CRM, ERP, or software rollout can lead organizations to misdiagnose resistance.
- Hear how fear, professional identity, and social stigma can produce very different forms of AI resistance, requiring more nuanced responses than simply mandating adoption or providing additional training.
- You’ll discover why some employees may already be using AI extensively while deliberately hiding that use from colleagues and managers, and what this “shadow AI” behavior can cost organizations in shared learning and business impact.
- Hear why starting with the highest-value work may not always be the smartest path to AI adoption. Gleb explains why beginning with tasks employees actively dislike can create an unexpected path toward broader experimentation and acceptance.
- You’ll learn why executive behavior matters in establishing the social norms around AI use, including how openly sharing successful AI applications can help shift AI from something employees conceal to something teams learn from together.
- Hear why middle managers may become one of the most important leverage points in AI adoption, translating executive intent into the everyday behaviors, experimentation, reinforcement, and learning that determine whether adoption actually takes hold.
- You’ll explore why judgment may become more valuable as AI becomes better at producing work. The conversation examines the difference between generating an answer and knowing whether that answer is good enough to act on.
- You’ll hear why AI creates a significant challenge for developing younger professionals, particularly when technology begins performing the work through which previous generations accumulated experience and learned to recognize quality.
- You’ll learn how the role of employees and managers could change as AI agents become more capable, moving from personally executing individual tasks toward directing agents, evaluating their output, managing handoffs, and improving human-machine workflows.
- Hear why there may be no final state of “AI maturity.” Instead, organizations may need to build the capacity for continual learning as AI capabilities, workflows, and the skills required to manage them keep changing.
Connect with Gleb Tsipursky
Disaster Avoidance Experts Website
Dr. Gleb Tsipursky LinkedIn
Connect with Mahan Tavakoli:
Mahan Tavakoli Website
Mahan Tavakoli on LinkedIn
Partnering Leadership Website
