Monthly, our tech team gathers for an 'AI Efficiency Retrospective.' We skip the hype and focus on the facts: How much time did AI save us this month? What issues did it resolve? Where did we run into trouble?
This post captures key takeaways from our latest session — a genuine look at how our efficiency has evolved. If you're navigating AI adoption, we hope this serves as a helpful reference."
Every month, the technology department holds an "AI Efficiency Retrospective Meeting." No fluff — just one thing: How much time did AI actually save us this month? What problems did it solve? What pitfalls did we encounter?
This article is a summary of our most recent meeting and a true internal record of our efficiency changes. If you're also exploring AI implementation, we hope this gives you some useful insights.
AI tools are emerging one after another, but we noticed a problem: They're easy to use, but hard to use effectively.
Many times, people try using AI for some tasks, but either they only scratch the surface or they forget about it after a few attempts. So starting this year, our department established a rule — spend one hour each month dedicated to reviewing AI usage, with everyone sharing:
What did you use AI for this month?
How much did efficiency improve?
What problems did you encounter?
How do you plan to improve next month?
The format is very simple, but after sticking with it for a few months, changes began to appear.
For this review, we compiled everyone's AI usage in the department. Below are the three scenarios with the most notable efficiency improvements:
Before:
Writing a feature description, internal notification, or weekly summary took an average of 15–30 minutes, mainly stuck on "how to start" and "how to phrase things more clearly."
Now:
Using AI to generate a first draft + manual revision reduces the average time to 5–8 minutes.
Efficiency Improvement: ~65%
How we do it:
Use Notion AI or ChatGPT to generate a first draft, then fine-tune based on your own tone and context. Especially for repetitive content (such as project progress descriptions in weekly reports), using templates + AI fill-in basically takes 3 minutes.
Department data: This month, about 40 pieces of copywriting were produced, saving a cumulative total of about 10 hours.
Before:
Writing code was fast, but writing comments and organizing technical documentation was time-consuming. An interface document of moderate complexity took 30–60 minutes.
Now:
Using Cursor and Copilot to assist with generating comments and automatically generating first drafts of interface documentation reduces the time to 10–15 minutes.
Efficiency Improvement: ~70%
How we do it:
After writing the code, have AI automatically generate comments based on function logic. For interface documentation, use tools to read the code and generate a first draft, then manually supplement with additional information.
Department data: This month, documentation for 8 modules was organized, saving a cumulative total of about 5 hours.
Before:
When receiving raw data from Excel or database exports, manual pivot tables, pattern identification, and conclusion writing were required. An analysis report typically took 1–2 hours.
Now:
Using AI tools (such as ChatGPT Advanced Data Analysis) to upload data, letting it automatically perform statistical analysis, generate charts, and extract key conclusions — time reduced to 20–30 minutes.
Efficiency Improvement: ~60%
How we do it:
Upload anonymized data, let AI perform an initial round of automatic analysis, then manually review and supplement with business judgment. AI excels at pattern recognition; humans excel at decision-making — a clear division of labor.
Department data: This month, 15 data analyses were completed, saving a cumulative total of about 15 hours.
The efficiency improvement numbers look great, but the process wasn't entirely smooth. Here are a few pitfalls we've encountered:
When some colleagues first started using AI, they directly copied and pasted the content generated by AI, resulting in factual errors, mismatched tone, and even deviations in the direction of the content.
Lesson learned: AI generates a first draft. The step of manual review and adjustment cannot be skipped.
At one point, we tried seven or eight different AI tools. The result was that everyone used different tools, and no stable workflow was established.
Lesson learned: We later standardized on 2–3 core tools, letting everyone master them first, then expanding based on needs.
Initially, people mainly used AI for writing weekly reports or editing copy, which provided limited help for core business.
Lesson learned: During subsequent reviews, we specifically encouraged everyone to think about "which core links can be improved with AI." Now, core tasks like coding and data analysis are gradually being covered as well.
Based on this review, we've set three directions to try next month:
1. Apply AI to customer feedback analysis
Try using AI to perform sentiment analysis and topic clustering on customer feedback to more quickly identify high-frequency issues.
2. Build an internal AI efficiency knowledge base within the department
Document the useful prompts and workflows that team members have discovered, preventing repeated mistakes.
3. Everyone tries one "new AI scenario"
Encourage everyone to choose a task they haven't used AI for before, attempt to complete it with AI, and share the experience at next month's review.
Looking back at the past few months of experimentation, we have three key insights:
1. AI efficiency gains don't happen overnight — they require continuous review and iteration.
Using AI once doesn't mean much; the real value comes when it's truly integrated into your workflow.
2. The goal of efficiency improvement isn't just "saving time" — it's "doing more valuable work."
The time we save is used for deeper thinking and more refined product development.
3. Tools are just aids; human judgment remains central.
AI can write quickly and compute quickly, but determining "what is right and what is good" is ultimately something that still requires human judgment.
