Botsitting: The Work Draining AI Gains

Key takeaways

A significant portion of AI-related work is hidden in 'bot sitting,' which involves feeding, verifying, and cleaning up AI outputs—this labor leads to fatigue and burnout.
Effective AI use requires not just adoption but careful management of human-AI interactions, emphasizing strategic oversight, continuous learning, and organizational governance.
Organizations that treat AI as a reasoning partner, promote transparency, and invest in training are more likely to see meaningful performance improvements.
The transition toward agentic AI will amplify current challenges like bot sitting and dependency issues, demanding new organizational systems and cultures.
Leadership plays a critical role—those who incorporate AI into team workflows and delegate coordination tasks to AI foster trust and boost adoption.

Summary

The Rise and Challenges of Bot Sitting in the AI Workplace

This section discusses the concept of 'bot sitting,' defined as the hidden human labor involved in making AI systems functional at work. Despite high AI adoption—87% of digital workers use it and report increased productivity—organizational performance gains lag behind individual improvements. Workers spend significant time 'bot sitting,' which involves feeding AI missing context, verifying outputs, debugging, and cleaning up errors. This unproductive labor is draining and can lead to burnout and moral disengagement, where employees shift blame onto AI and feel less accountable for mistakes. The report highlights that effective AI use depends not only on adoption but on how organizations manage this invisible labor.

Types of AI-Related Work and Worker Behaviors

AI usage in the workplace falls into three categories: learning and building workflows, actively using AI to perform tasks, and bot sitting. Interestingly, bot sitting is split between productive management—like verifying outputs—and unproductive tasks such as reloading context or comparing outputs across tools. Workers spend around 6.4 hours weekly on bot sitting, with some experiencing exhaustion that correlates with increased burnout and dissatisfaction. The phenomenon of 'bot sh**ing,' where workers overly trust AI outputs and stop verifying, is identified as a slow surrender of judgment and responsibility, posing risks of systemic errors and reduced oversight.

Impact of AI on Worker and Organizational Dynamics

The report emphasizes that the most impactful AI users are those who treat AI as a reasoning partner, effectively guiding and iterating on outputs. High AI achievers are more strategic: they focus on core tasks, reinvest saved time into developing new skills, and use AI as a tool for personal and team growth. Team dynamics reveal that AI adoption spreads more quickly when peers and cross-functional teams adopt it first, due to organizational messiness and the need for coordination. Managers leveraging AI for administrative tasks can better focus on coaching and development, enhancing trust and morale within organizations.

Transformative AI Adoption and Organizational Practices

Transformative organizations—those reporting significant improvements due to AI—focus on relevant metrics, transparency, and continuous governance. They incorporate AI into daily workflows, provide extensive training, and ensure workers understand and can see their AI usage data. These organizations foster trust through transparency and prioritize building systems around AI, rather than just vendor selection. They also reward AI skills and support ongoing learning. The report suggests that as AI technology advances toward agency and autonomy, these practices will become even more critical for realizing AI’s full potential in enterprise settings.

Show notes

As AI spreads through the workplace, workers are saving time — but also spending hours feeding context, checking outputs, debugging mistakes, and cleaning up the mess. Today’s episode digs into why “botsitting” may become one of the defining challenges of the agentic AI era, and what separates organizations that turn AI use into real transformation from those that don’t.

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