Employees Say They Spend More Time Managing AI than Producing Work

Why It Matters

The findings reveal that without proper support structures, AI adoption can create hidden labor costs, eroding the promised productivity gains and exposing firms to quality risks.

Key Takeaways

• AI saves ~11 hours weekly, but most time goes to management.
• Only 13% say AI markedly improves company performance.
• Employees spend ~6.5 hours weekly on AI maintenance tasks.
• Over one‑third of AI sessions fail, forcing rework.
• Success needs training, governance, and human infrastructure around AI.

Pulse Analysis

The Glean Work AI Index highlights a growing paradox in enterprise AI: while automation can reclaim roughly 11 hours per employee each week, the reclaimed time is quickly consumed by managing AI outputs. Workers report spending more time feeding context, verifying results, and correcting errors than they do on core tasks. This hidden labor, averaging 6.5 hours per week, dilutes the headline productivity narrative and explains why only a small fraction of employees—13%—see a tangible boost in company performance.

These insights underscore that AI tools alone are insufficient for transformation. Companies must build a robust human infrastructure that includes clear use‑case guidance, ongoing training, and governance frameworks. Treating shadow AI activity as a symptom rather than a failure point can reveal gaps in approved solutions, prompting better tool selection and policy refinement. By embedding AI into everyday decision‑making processes and establishing guardrails, organizations can reduce session failures—currently exceeding one‑third—and lower the rework burden that erodes efficiency.

Enterprises that succeed will repurpose the time saved from routine automation into higher‑value, human‑centric work, such as strategic analysis, creative problem‑solving, and upskilling. Investing in AI literacy, contextual grounding, and performance‑based metrics ensures that AI augments rather than replaces human effort. As AI adoption matures, firms that prioritize governance and continuous learning are poised to capture genuine productivity gains and maintain quality standards, turning AI from a vanity metric into a strategic asset.