AI was supposed to make work easier. For many Amazon employees, it has done the opposite.
A new study confirms what workers at the company have been saying: AI tools are increasing their workload rather than reducing it. Instead of automating tasks away, the technology is generating new ones - more reviews, more oversight, more processes layered on top of existing responsibilities.
The Productivity Paradox, Again
This is not the first time a new technology has made things worse before making them better. Economists call it the "productivity paradox" - the gap between what a technology promises on paper and what actually happens when you drop it into a real workplace. Email was supposed to save time. It created an endless stream of messages to manage. Slack was supposed to reduce email. It added another channel to monitor.
AI tools follow the same pattern. When a company rolls out AI-assisted code review, someone still has to review the AI's output. When AI drafts documents, someone has to verify they are accurate. When AI generates reports, someone has to read them. The work does not disappear. It shifts.
At Amazon specifically, the pressure compounds. The company is known for aggressive performance metrics and a culture that already pushes employees hard. Adding AI tools on top of that does not reduce the load - it raises the bar. Now you are expected to do everything you did before, plus manage the AI.
What This Means for AI Tool Adoption
The study should be a cold shower for any company thinking about AI rollout. The problem is not the technology itself. The problem is implementation. Most organizations are bolting AI onto existing workflows without removing any of the old steps. That is a recipe for burnout, not efficiency.
The companies getting real productivity gains from AI tend to do something different: they redesign the workflow around the tool rather than just adding the tool to the workflow. That means actually eliminating manual steps, not just supplementing them.
For anyone using AI tools in their own work, the lesson is practical: if an AI tool is creating more work than it saves, the issue is probably your process, not the tool. The gains come from replacing steps, not adding an AI layer on top of every existing one.