Botsitting: The Work Draining AI Gains

2026-06-26 · Source: The AI Daily Brief: Artificial Intelligence News and Analysis · Field: Business & Management — Corporate Strategy & Leadership, Human Resources & Workforce Development, Operations & Process Management · Depth: Intermediate, extended

Summary

The widespread adoption of AI in the workplace, with 87% of digital workers using it and 75% reporting 11 hours saved weekly, is paradoxically not translating into significant organizational performance improvements for 87% of companies. A report from Glean and the Work AI Institute, based on 6,000 surveys from December-January, identifies "botsitting" as the primary culprit. Workers spend an average of 6.4 hours per week on this hidden labor, which includes feeding AI context, checking outputs, debugging mistakes, and cleaning up errors. This time consumes a large portion of the productivity gains. The report also highlights "bot-shitting," where fatigued workers offload judgment and responsibility to AI, leading to unverified outputs and downstream rework. Transformative organizations, however, build a "human infrastructure of AI" by focusing on relevant metrics, providing AI usage visibility, implementing living governance, and investing in employee training and skills.

Key takeaway

For Directors of AI/ML or VPs of Engineering aiming for genuine AI transformation, recognize that individual productivity gains are often offset by "botsitting" – the hidden labor of managing AI outputs. You must proactively build a "human infrastructure of AI" by fostering discerning AI use, maintaining human accountability, and investing in continuous training. Prioritize transparent AI usage metrics and adaptive governance to prevent fatigue and the "bot-shitting" phenomenon, ensuring AI truly enhances organizational outcomes.

Key insights

"Botsitting" is the hidden labor of managing AI, consuming saved time and hindering organizational transformation.

Principles

• Individual AI gains don't automatically translate to organizational performance.
• "Botsitting" leads to fatigue and "bot-shitting" (offloading responsibility).
• Transformative organizations build a "human infrastructure of AI."

Method

The article describes a cycle: AI deployment -> botsitting rises -> fatigue -> bot-shitting -> unverified outputs -> cleanup. It also outlines a three-level approach (individual, team, organization) for building AI infrastructure.

In practice

• High AI achievers use AI discerningly and reinvest saved time in new skills.
• Teams keep accountability on people, not AI, and spread adoption peer-to-peer.
• Organizations prioritize relevant metrics and invest in AI training.

Topics

• Botsitting
• AI Transformation
• Agentic AI
• Work AI Institute
• Organizational Productivity
• AI Governance

Best for: CTO, Executive, AI Product Manager, Director of AI/ML, VP of Engineering/Data, Consultant

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Editorial summary, takeaway, and curation by AIssential. Original article published by The AI Daily Brief: Artificial Intelligence News and Analysis.