- A survey by the Glean Work AI Institute of 6,000 digital workers shows that 75% believe AI makes them more effective.
- Participants estimate that AI saves them an average of 11 working hours per week.
- However, only 13% see a significant improvement in their organization’s operational efficiency.
- The author attributes this to “coordination neglect,” where businesses underestimate the costs of work coordination.
- A common mistake is optimizing individual tasks while ignoring the connection between teams and stages in a process.
- The article distinguishes between three levels of task dependency: independent, sequential, and reciprocal; the higher the dependency, the more AI benefits are canceled out by error correction and coordination.
- A Zapier expert recommends that businesses map out real workflows before deploying AI to identify handoff points and bottlenecks.
- The author warns of “invisible work,” as employees spend an average of 6.4 hours weekly supervising AI, adding context, and fixing outputs.
- A randomized trial in 2025 showed that programmers using AI thought they were 20% faster, but in reality, they took 19% more time.
- AI’s coherent presentation creates a sense of reliability, making it harder for humans to spot incorrect analyses.
- Many businesses evaluate AI success using metrics like token count, number of users, or licenses, rather than actual business outcomes.
- The survey shows that 83% of employees in firms that measure both quality and productivity evaluate AI as improving work quality, compared to only 68% in firms that measure only productivity.
- The author concludes that the greatest value of AI comes from redesigning processes, decision-making rights, and coordination mechanisms, not just individual productivity.
📌 AI does not automatically create efficiency at the organizational level despite saving employees time. According to a survey of 6,000 digital workers, 75% feel increased productivity and save about 11 hours per week, but only 13% see their business operating better. The cause mainly lies in the costs of coordination, supervision, and post-AI error correction. To capture real value, businesses need to optimize entire workflows instead of just focusing on individual productivity or AI usage metrics.
