When deploying AI tools, especially in sales, users exhibit no patience for mistakes. While a human making an error receives coaching and a second chance, an AI's single failure can cause users to abandon the tool permanently due to a complete loss of trust.

Consumers can easily re-prompt a chatbot, but enterprises cannot afford mistakes like shutting down the wrong server. This high-stakes environment means AI agents won't be given autonomy for critical tasks until they can guarantee near-perfect precision and accuracy, creating a major barrier to adoption.

A new, invisible form of labor called "botsitting"—feeding context, checking outputs, and debugging—consumes 37% of workers' AI time. This is more time than they spend actively using AI to complete tasks (36%), creating a significant, hidden productivity drain and burnout risk.

A Glean report identifies 'bot sitting' as the hidden labor cost of agentic AI. Knowledge workers spend over six hours per week on manual tasks like feeding agents context, checking outputs, and rerunning failed jobs, undermining the technology's promised efficiency gains.

Employees, burned out from the unrewarded labor of "botsitting" (managing AI), eventually hit a breaking point. This leads them to "botshit"—delivering AI-generated work they can't explain or defend. The root cause is systemic, not just individual laziness.

Unlike humans who debate flawed data in meetings (a "data brawl"), AI agents confidently present a single, incorrect number from bad data. This creates a "silent failure" where the error is persuasive and unnoticed, a phenomenon Salesforce's Gaurav Pathak calls "garbage in, gospel out."

The burnout from "botsitting" leads to "botshitting"—a slow surrender of agency where workers ship unverified AI outputs. This creates a vicious cycle of low-quality work, increased rework, and moral disengagement, with 40% of workers blaming AI for failures instead of themselves.

An attempt to use AI to assist human customer service agents backfired, as agents mistrusted the AI's recommendations and did double the work. The solution was to give AI full control over low-stakes issues, allowing it to learn and improve without creating inefficiency for human counterparts.

AI is increasing stress in customer service by automating routine cases and leaving humans with more difficult, emotional ones—often without proper training for this shift. This dynamic, causing anxiety and burnout, serves as a critical warning for how AI deployment can negatively impact employees if not managed holistically.