Employees spend more time managing AI than producing work

2026-06-10 · Source: Information and Enterprise Technology News | CIO Dive - Www.ciodive.com · Field: Business & Management — Corporate Strategy & Leadership, Operations & Process Management, Human Resources & Workforce Development · Depth: Intermediate, short

Summary

A Glean survey, based on 6,000 digital workers and insights from AI leaders, reveals that while AI saves employees an average of 11 hours per week, a significant portion of this time is consumed by managing the AI tools rather than producing new work. Despite 75% of knowledge workers reporting increased personal productivity, only 13% indicate that AI has substantially improved their company's overall performance. The "Work AI Index" report, published June 10, 2026, found that workers spend nearly 6.5 hours weekly on low-visibility maintenance tasks such as providing context, checking outputs, and correcting mistakes. For every hour of useful AI output, another hour is spent making it usable, with over a third of AI sessions failing completely. Successful enterprise AI adoption, according to the report, depends on robust "human infrastructure," including targeted training, contextualizing AI for enterprise use, and integrating governance into daily operations.

Key takeaway

For Directors of AI/ML evaluating enterprise AI deployments, recognize that simply increasing AI usage does not guarantee productivity or performance improvements. Your teams are likely spending substantial time managing AI outputs, checking for errors, and providing context. Focus on building robust "human infrastructure" around AI, including comprehensive training on specific use cases and integrating governance. Reinvest time saved by AI into higher-value human-centered work and skill development to achieve true organizational impact.

Key insights

AI's productivity gains are often offset by significant employee time spent on management and correction tasks.

Principles

• AI usage does not automatically equal productivity.
• Human infrastructure is key for successful AI adoption.
• Reinvest saved time into higher-quality human work.

Method

To improve AI use cases, leaders must ground AI in enterprise context, train employees, address shadow AI, and build governance into daily decisions.

In practice

• Train employees on specific AI use cases.
• Integrate AI governance into workflows.
• Address "shadow AI" as a signal of tool gaps.

Topics

• AI Productivity
• Enterprise AI Adoption
• AI Governance
• Workforce Management
• Glean Work AI Index
• Shadow AI

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

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Editorial summary, takeaway, and curation by AIssential. Original article published by Information and Enterprise Technology News | CIO Dive - Www.ciodive.com.