Summary of the Key Points
This article highlights a concerning phenomenon in the workplace: AI can quickly produce reports, proposals, or PPTs that appear professional, but many of these are merely “shallow” creations, often referred to as “workshop AI waste.” These documents lack factual verification, professional judgment, and practical value. Not only are they of poor quality, but they also shift the workload onto others—those who use AI save time, while those who receive the results have to spend more effort verifying, correcting, or even trying to understand the intentions behind them, ultimately leading to decreased team efficiency. The article emphasizes that what the workplace really needs are not people who can generate answers quickly, but those who know where the answers come from, can assess their credibility, and are willing to take responsibility.
Detailed Analysis
1. “Workshop AI Waste”: Beautiful on the Surface, but a Hindrance to Teams
“Workshop AI waste” refers to content created by AI that looks impressive with its format and professional terminology, yet lacks substance. The data may have no source, competitor information might be fabricated, and customer pain points are just general statements. Even crucial details such as budgets and timelines are absent. For example, a twenty-page proposal written by AI can take ten minutes to create, but the person responsible for implementing it might spend hours filling in the gaps and correcting errors.
The real problem with this approach is not the low quality of the content itself, but the cost shift it imposes: the creator saves two hours, but the recipient has to spend four hours fixing issues. One person’s “efficiency” slows down the entire team because everyone has to deal with these half-finished products.
2. AI-Generated Weekly Reports Save Time, but at the Cost of Trust
Using AI to write weekly reports is indeed time-efficient: you simply provide the data to the AI, and in ten minutes, you get a report claiming improved user engagement or successful strategies. However, AI doesn’t understand the context behind the numbers. For instance, if the method of data collection has changed, or if there were any temporary events during the week, the AI won’t be aware of these factors and will just provide plausible explanations based on the available data.
Take Kobayashi’s example: he used AI to write his weekly report, saving himself time, but his manager spent an entire afternoon verifying the information. While AI saves the writer’s time, it erodes the reader’s trust in the accuracy of the content. The next time someone sees a report generated by AI, they will question its reliability and wonder if there are any hidden issues.
3. Vague AI Performance Feedback Leaves Employees Confused
Leaders use AI to write performance evaluations that sound formal, such as “strengthening overall thinking and improving cross-departmental collaboration,” but these statements are often empty. There are no specific examples of problems or consequences, nor are there clear goals for improvement. Amin’s experience illustrates this: the leader saved time by using AI, but she spent a week trying to figure out what the leader actually wanted her to improve. Useful feedback doesn’t rely on fancy language; it provides facts, identifies issues, and offers directions for improvement. While AI can polish the language, it cannot replace the leader’s critical thinking.
4. Teams That Rely Entirely on AI Become Document Porters
Some teams rely heavily on AI: planners use it to create proposals, project managers use it to summarize meetings, and assistants use it to format reports into PPTs. The process seems efficient, but no one actually delves into the content’s relevance. For example, a proposal might state that young users prefer a certain feature, yet no one has interviewed users or analyzed the data to back this claim. When clients ask for details, the team is at a loss because no one has taken responsibility for the information.
5. AI Is a Tool, Not a Replacement for Responsible Decision-Makers
While AI can be helpful, its use varies greatly among individuals. Some people use it effectively—organizing materials, comparing ideas, verifying facts, and adding additional context. Others, however, simply submit the initial AI-generated version without any further effort. AI can save you some work, but it cannot understand the real situation (e.g., user needs), assess the pros and cons of a proposal, or take responsibility for its outcomes. What the workplace really needs are people who know where the answers come from, why they’re reliable, and are willing to make decisions.
In Conclusion
AI is a valuable tool, but those who use it must be responsible. Don’t let AI become a scapegoat for poor work, nor should you become someone who merely uses AI to produce superficial content. The key in the workplace is not who can generate documents fastest, but who can solve real problems effectively.