Summary of Key Points
OpenAI has released GPT-5.6 and ChatGPT Work, advancing AI from answering questions to directly producing work results such as documents, PPTs, and prototypes. However, this advancement comes with significant challenges: while the coding world has robust error-correction mechanisms (errors, testing, rollback), office work lacks such verification systems. As a result, AI tends to “actively fill in” unconfirmed information, creating seemingly perfect outputs that actually hide hidden errors and increase the cost of manual review and rework. For AI to truly enhance efficiency, companies and individuals need to establish clear work standards (similar to “test cases” for coding) rather than relying solely on one-click generation tools.
1. From “Chatting and Answering Questions” to “Doing Work Directly” – The Surprises Behind the Need for Rework
OpenAI’s latest updates are not just simple model upgrades; they transform AI into a “versatile worker.” Chat is used for chatting and searching, Work generates documents, PPTs, and web pages, while Codex manages code and local files—all integrated within a single application. It sounds promising, but the author’s personal experience shows otherwise. She asked AI to create a project prototype, which was completed in 10 minutes. However, during review, many details (such as who the buttons were for and where the rules came from) turned out to be unconfirmed information filled in by AI. This led to additional time spent on verification, editing, and explanation. In other words, while AI speeds up the process, it doesn’t actually save effort.
2. The Coding World Has Error-Correction Tools, but Offices Don’t – AI in Offices Tends to Make Random Assumptions
Why do AI systems make fewer major mistakes when coding? Because code has clear standards: incorrect code won’t compile, failed functions will be detected during testing, and changes are recorded (with diff files), allowing for easy rollback to previous versions. Office documents, on the other hand, lack such clarity. Rules guessed by AI in a PRD (Product Requirements Document) may not be highlighted as errors, and a wrong PPT can still be presented professionally. Moreover, there are often no clear criteria for evaluating the quality of work (e.g., whether a proposal is good depends on leadership preferences or past experience), leaving AI to produce what it believes to be the best answer, making it difficult to identify mistakes.
3. AI “Refinishes” Vague Requirements – Perfect on the Surface, but Full of Hidden Problems
Another issue with AI is that it tends to fill in missing information without admitting confusion. For example, with vague requirements, AI can create a complete page or process within minutes, leading you to believe the requirements have been clarified when they haven’t actually been defined. Vagueness hasn’t disappeared; it’s just been packaged as a “finished product.” In the past, you could spot AI’s inaccuracies, but now, those inaccuracies might appear in a well-structured PPT only during review.
4. Offices Need “Test Cases” – Not for Learning Programming, but to Define What Constitutes Good Work
Here, “test cases” refer to clearly defining the criteria for completing tasks. For instance, when conducting business analysis, you need to specify data sources, calculation methods, and the verifiability of conclusions; when defining product requirements, you must clarify what is confirmed, what can be inferred, and what won’t be included in the current iteration; when creating marketing content, you need to list facts that must be verified, expressions that cannot be used, and who will approve them. These standards help expose errors early, preventing costly mistakes later on. OpenAI emphasizes “reference documents, progress tracking, and approval” in ChatGPT Work for this very reason—AI can execute the tasks, but it needs clear guidelines to know what constitutes “doing it right.”
5. What Companies Need Is Not Just AI Tools, but Clear Rules for AI to Follow
Domestic companies will likely quickly adopt ChatGPT Work’s features, but having the tools alone is insufficient. Many companies have piles of documents with issues such as multiple ineffective systems, inconsistent definitions for the same indicators across departments, and files labeled “final versions” that no one knows are actually final. Providing this chaotic data to AI only results in more confusion. What companies really need are authoritative data sources, standardized knowledge, business rules, and processes for handling exceptions—these are the foundations upon which AI can effectively work, and they are more important than releasing new models.
In Conclusion
AI has become more powerful and can perform more tasks, but that doesn’t mean less work for humans. If work standards are vague and rules are unclear, the more AI generates, the heavier the burden of review becomes. Offices must establish clear “test cases” before AI can truly enhance efficiency. For individuals, the most valuable skill in the AI era is the ability to judge what is good work, what cannot be done, and how to verify it—these are the core competencies needed to thrive in this new environment.