AI can function as an external brain. At the same time, it can also function as a translator.
Here, “translation” does not mean only translating Japanese into English.
- Translating anger into professional language
- Translating raw data into something you can say in a meeting
- Translating leaps in abstraction into explanations with visible steps
- Translating what you really think into wording that avoids unnecessary self-inflicted damage
- Translating a side-business idea into a generalizable article
- Translating complaints into checklists
- Designing and translating a question before asking another person
This kind of “translation” matters a lot.
1. Translating anger into professional language
The raw data might sound like this:
Why do I have to receive packages for other departments and then contact them myself? Don’t turn me into the company’s unclassified inquiry box just because nobody knows who to contact.
Said as-is, that is too aggressive.
With AI, it can be translated into something like this:
My number is listed at the visitor entrance as the contact for cases where the recipient is unknown. As a result, packages for other departments, inquiries from delivery companies, and visitor-related requests are being concentrated on me. I am not the company-wide first point of contact, so I would like the main contact information for each purpose and department to be displayed more prominently, with unknown-recipient cases routed to General Affairs, Security, or the main company number.
The substance is the same. What changes is how the other person receives it.
2. Translating a leap in abstraction into an explanation with visible steps
The raw data might be:
If you abstract the reason behind that correction, the same problem can happen elsewhere too. So if we don’t standardize it, what’s the point?
Said like that, the other person is likely to respond, “That’s not what we’re talking about right now.”
AI can translate it into something like this:
For this case, I will first make the correction as requested. Separately, if we organize the reason for the correction, it seems we can express it as a decision rule such as, “When ○○ happens, do △△.” Because that rule may also apply to other cases, I think it would be useful to separate it from the immediate correction and turn it into a checklist item from the standpoint of recurrence prevention.
Now the steps are visible:
- Handle the current case
- Identify the reason for the correction
- Extract the common rule
- Consider the impact on other cases
- Prevent recurrence
3. When someone says “think about it,” translate the scope first
The raw data might sound like this:
You told me to think about it, so I asked why and even considered standardization. And now you’re saying, “That’s not what we’re talking about”? How far was I supposed to think?
Said directly, this can easily turn into an argument.
AI can translate it like this:
For this case, is it enough to handle the individual issue only? Or should I also consider a rule that would prevent the same type of mistake? I would like to confirm how far the scope of consideration is expected to extend this time.
This makes the scope of “think about it” explicit.
4. Translating questions that could put you at a disadvantage
The raw data might be:
If I ask the person directly, they might think I don’t understand something I should already know. If they realize I don’t have that information, it could put them in a stronger position.
First, AI can be used for an internal check:
What background information should I already know before dealing with this matter? Separate the questions that could put me at a disadvantage if I ask them directly from the information I should verify on my own first.
Then, when turning it into something to ask another person, AI can soften it like this:
Just to make sure we are working from the same assumptions, is my understanding correct that the scope relevant to this decision extends through ○○? If our assumptions differ, it may cause rework, so I would like to confirm this in advance.
Instead of exposing yourself with a bare “I don’t know,” you turn the question into alignment on assumptions.
5. Translating the “answering phone calls” problem into a workflow problem
The raw data might sound like this:
Sales calls aren’t worth answering, and it’s annoying that every call gets transferred to me as soon as someone says it’s related to recruiting. Every phone call interrupts my actual work.
AI can translate it like this:
I am not refusing telephone support itself. However, there are many sales calls and transfers where the responsible person is unclear, and these frequently interrupt my primary work. Before transferring a call, I would like the caller’s company, purpose, responsible department, and whether a callback is required to be confirmed, with only the necessary calls passed along. I will continue to handle calls addressed directly to me, recruiting-related matters, and urgent requests.
Now it is framed not as refusing to work, but as improving the workflow.
6. Translating “I’m only doing the minimum” into boundaries of responsibility
The raw data might be:
I’m rooting for you, but I’m not doing it. It’s a money-losing project. I’m leaving on time.
AI can translate it like this:
I understand that the situation is difficult, but for this case I would like the responsible team to continue handling it. If there are points that need confirmation, I can respond within the scope of my responsibilities.
Instead of sounding cold, it becomes a clear boundary around responsibility.
7. Basic templates for this kind of AI translation
To turn raw data into professional workplace language:
Convert the following raw data into professional workplace language that is less likely to create unnecessary friction.
The goal is not to vent emotion, but to clarify responsibilities, recurrence prevention, and points that need confirmation.
Do not blame the other person, but also do not make me assume responsibility beyond my scope.
To translate a leap in abstraction:
Rewrite the following so that the other person is less likely to feel that the discussion suddenly jumped to another topic.
Explain it in this order: concrete action → reason → common rule → recurrence prevention.
Separate the response to the current case from the discussion of possible standardization.
To organize politically delicate questions or questions that could put you at a disadvantage:
Identify which questions about the following matter could put me at a disadvantage if I ask the other person directly.
Separate them into three groups: assumptions I should verify on my own first, questions I can ask directly, and questions whose wording should be softened.
Draft confirmation questions so that I do not appear to have no understanding of the matter at all.
8. In one line: raw data for AI, translated data for people
The core idea can be summarized like this:
Give AI the raw data. Give people the translated data.
Or put another way:
Before throwing what you really think directly at another person, use AI to translate it into professional language.
And the same applies before asking a question:
Before asking a person, use AI to align the assumptions first. Reduce the cost of clarification and the cost of information-positioning before handing the issue to another person.
Using AI this way is not about hiding what you really think. It is about preserving the substance while organizing the communication and the boundaries of responsibility.
