Artificial intelligence was widely expected to boost productivity across industries, but a growing number of workers and analysts are questioning whether that promise is actually materializing in everyday work. Instead of freeing people from repetitive tasks, some AI tools require extra time to correct errors, refine outputs, or adjust results that miss the mark. This extra effort can erode the time saved, blurring the line between genuine productivity gains and added overhead.
One common issue is that many AI systems produce outputs that look plausible but are technically flawed or contextually inappropriate. When users must spend time verifying and fixing AI-generated content, the efficiency benefit shrinks. For example, AI-generated reports, summaries, or code might require careful human review to make sure they are accurate, relevant, and aligned with organizational standards. This additional step can offset the speed advantage that AI initially promised.
Another factor is work fragmentation. When people switch between thinking, prompting AI, reviewing results, and correcting mistakes, it can disrupt deep concentration and flow. Frequent interruptions can reduce overall effectiveness, leaving workers feeling busier but not necessarily more productive. In knowledge work that relies on complex reasoning and creativity, this fragmented approach may slow momentum rather than accelerate it.
Despite these challenges, the article suggests that AI’s impact on productivity is not inherently negative — it depends on how it’s integrated. Organizations that align AI tools with well-defined workflows, provide training on effective use, and set clear expectations for quality can harness AI for real productivity improvements. The key is designing systems where human and machine strengths complement each other, rather than creating extra layers of work that cancel out potential gains.
