The Single Structural Reason Why ChatGPT, Claude, and Gemini "Hit a Wall" in Labor and Social Security Attorney Work
Hello, I am
Yuki Fukiage, Representative of Lean Stack Inc..
DX support for labor and social security attorney offices "Yohakuru"
https://www.yohakuru.com/
■ Track record of AI/DX implementation in labor and social security attorney offices
Shinka Social Insurance Labor Consultant Corporation: 80% reduction in subsidy checking tasks, over 50% reduction in document creation and procedural tasks
Social Insurance Labor Consultant Office Asterisk: 95% reduction in work rules tasks
Social Insurance Labor Consultant Corporation Four Season: 80% reduction in payroll calculation tasks
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Introduction
In this article, I will articulate the structure behind "that sense of discomfort" that is quietly spreading throughout the labor and social security attorney industry.
ChatGPT was released, followed by Claude and Gemini.
With the enthusiasm that "the future is AI," I am sure that labor and social security attorneys have tried using them at least once.
I feel that the number of attorneys around me who are starting to think seriously about AI utilization is increasing.
And when you try using them, you are impressed at first.
The text is polished, and plausible answers come back quickly.
However, when you try to bring them into the core of your work, the same feedback comes back like clockwork.
"It works up to a point, but it stops at the crucial moment."
This is not happening because the attorneys are using them incorrectly.
ChatGPT, Claude, Gemini—
General-purpose AI has a clear structural limitation when it comes to labor and social security attorney work.
In this article, I will write all at once about the true nature of that limitation and how you can actually get AI to reach the core of your work.
I hope you find this helpful.
The same words that always come from the mouths of labor and social security attorneys
Recently, when I talk to labor and social security attorneys, I hear the same things over and over again.
"I was impressed with ChatGPT for the first few weeks, though."
"I tried using it for payroll, but I hit a wall immediately."
"In the end, I went back to manual work in Excel..."
They feel the limits of ChatGPT, then try Claude, and then touch Gemini.
After going through this cycle about three times, they land on the conclusion that "it doesn't reach the core of our work."
This is the pattern for almost everyone.
Or they compromise somewhere, haha.
And here is the interesting part: the attorneys always continue like this.
"It hits a ceiling right away when it comes to labor and social security attorney work."
From an expert's perspective, the answers ChatGPT gives look
"shallow," "off-base," or "just generalities." to them.
Even though it is touted as a "smart AI" by the public, it suddenly stops working when faced with labor and social security attorney tasks.
This is not because the general-purpose AI is bad, nor is it because the attorneys are using it poorly.
There is a proper reason for it within the structure of the general-purpose AI itself.
The one structural reason why general-purpose AI hits a wall
Let me start with the conclusion.
The reason ChatGPT, Claude, and Gemini hit a wall in labor and social security attorney work is not a matter of intelligence, but because they don't know "your office."
It comes down to this.
The "smart AI" that people in the world imagine is, in the end, just a matter of the amount of knowledge.
General-purpose AI knows the Labor Standards Act, social insurance systems, and tax laws inside and out.
It can even write papers.
Recently, I heard it's even solving previously unsolved mathematical formulas without breaking a sweat...
However, what is truly needed in the field of labor and social security attorney work is not knowledge.
It is context.
In some cases, it is referred to as context.
"This specific exception handling for payroll calculation at Company A, which is a client of our firm."
ChatGPT does not possess context this specific.
The vast amount of tacit knowledge in the minds of attorneys—rules for each client, past decision history, the president's preferences, and risks specific to the industry.
Structurally, general-purpose AI cannot reach this.
If you compete on the amount of knowledge, general-purpose AI knows many times more than a university student.
But if you compete on context, the limitations of a labor and social security attorney in the field become apparent quite easily.
That is why, in front of an expert, it can only return general theories and immediately hits a ceiling.
Since this is a structural problem, no matter how much you refine your prompts, it will not be fundamentally solved.
Three limitations of general-purpose AI seen in the field
I don't think abstract theories will resonate, so I will list three limitations actually seen in the field.
It cannot hold the calculation rules for each client in the first place
First, this is the biggest limitation.
Company A closes on the 20th and pays at the end of the month; housing allowance is fixed at 15,000 yen.
Company B closes at the end of the month and pays on the 15th of the following month; position allowance is based on a grade system and is taxable.
Company C uses a variable working hours system and requires weekly aggregation.
—There are as many of these as there are clients.
Are you going to write this into a ChatGPT prompt every single time?
No, that's impossible, isn't it?
Even if you did write it down, there is no guarantee that ChatGPT would understand it as you intended.
Even if you could write it out, you would end up thinking, "If I can explain it in this much detail, it's faster to just calculate it myself."
Due to the design of general-purpose AI, the premise is that you must explain the client's unique rules from scratch every single time you use it.
This is the first limit that prevents AI from being fully integrated into your workflow.
The "particulars" of each firm are not reflected
Next, this also has a significant impact.
Every labor and social security attorney firm has its own "style."
Preferences for the format of deliverables.
The phrasing used when reporting to the company president.
How far to delve into risky cases.
The tone used when responding to client inquiries.
These are the very essence of your management philosophy.
Even if you instruct a general-purpose AI to "write it like my firm would," it only returns vague, generic answers.
That is only natural.
After all, ChatGPT does not know "your firm."
Deliverables that lack the firm's unique character are immediately spotted by clients as "different from usual."
This is where general-purpose AI reveals its second limit.
General-purpose AI cannot bear the ultimate responsibility for legal compliance
And this is the biggest limit of all.
Labor Standards Act, social insurance, tax systems—the laws handled by professionals are amended almost every year.
General-purpose AI does not perfectly account for every single recent amendment.
Hallucinations (plausible-sounding lies) are also guaranteed to occur.
And, naturally, ChatGPT will not take responsibility.
"Because ChatGPT said so" will not work on a client even by a millimeter.
In the end, you have to check every single case.
If that's the case, it would have been faster to do it yourself from the start.
This is the real reason why many of you end up "going back to Excel" in the end, isn't it?
So, it's time to graduate from trying to "leave everything" to general-purpose AI.
If you've read this far, I think you've already guessed it.
Trying hard to make ChatGPT, Claude, and Gemini handle the core of your work was misguided from the start.
These are "general-purpose" AIs.
They are built to be somewhat useful to anyone in the world who uses them.
But labor and social security attorney work is the exact opposite of "general-purpose."
It differs from office to office.
It differs from client to client.
It differs from person to person.
Trying to handle this highly specific work entirely with general-purpose products is a structural mismatch from the start.
So, where is the answer?
Having an AI dedicated to your office.
This is the only way.
Instead of relying entirely on general-purpose AI, you need to switch your mindset to cultivating "an AI for your office."
What our company's service, "Yohakuru," does is exactly this.
By the way, "Yohakuru" comes from "creating space (yohaku) in your work"—get it? (laughs)
Calculation rules for each client, past decision history, office-specific formats, and risk management by industry.
You train all of this into the AI and the system.
Then, for the first time, you will get back "your office's answers" that ChatGPT couldn't reach.
What an office-specific AI brings is not just efficiency, but "assetization."
Let me go a level deeper from here.
Many of you are misunderstanding the true value of an office-specific AI.
Almost every professional thinks it's about "reducing work hours."
That's not it.
Saving time is just the entrance.
It's the prologue to the prologue.
The real core isthe accumulation of "knowledge" within the firmitself.
That is what it comes down to.
Even if a staff member leaves, business knowledge remains in the firm
This alone is huge.
Until now, the moment a payroll expert quit, all the rules for each client that were in that person's head disappeared.
If you have your firm's proprietary AI learn it, the knowledge stays with the firm, not the person.
Even if a staff member leaves, the work doesn't stop.
With this alone, the biggest risk in firm management disappears at a structural level.
The hurdle for hiring requirements is lowered, and the pool of applicants expands
In a firm where business knowledge resides within AI and systems, the skills required of new hires change drastically.
"5+ years of labor experience" becomes "someone comfortable with PC work."
There are cases where the pool of applicants tripled just by changing the wording in the job posting.
The size of the recruitment market itself expands at a structural level.
Even if the number of clients increases, operations continue without a drop in quality
And this is the biggest management impact.
When work was dependent on specific individuals, every time a new client was added, the problem of "who will handle it" and "who will learn it" arose.
If your firm's proprietary AI contains all the client rules, quality won't drop even if you add new clients.
You can handle more clients with fewer people at a stable level of quality.
The ceiling for revenue physically rises.
I'm not trying to hype this up, but I believe it's a race against time.
Because eventually, this will become the default standard.
Summary
ChatGPT, Claude, and Gemini are not bad tools.
They are truly intelligent in general areas like writing, summarizing, and research.
However, the moment you bring them into the highly specialized field of labor and social security attorney work, they simply hit a structural limit.
Dismissing them as "useless" or continuing to hope that they will "get smarter someday" are both off the mark.
General-purpose AI has its own range of capabilities.
And the core of labor and social security attorney work lies outside that range.
With a proper understanding of this, there is only one answer.
Recognize the limits of general-purpose AI and switch to an AI specific to your firm.
Only then does true AI utilization begin.
And that transition is not just about improving operational efficiency.
It is about building a management system where "knowledge" is accumulated in the firm, dependency on specific individuals disappears, hiring becomes easier, and quality does not drop even as the number of clients increases.
This gap will only continue to widen from here on out.
Will you continue to rely solely on general-purpose AI and keep hitting limits, or will you cultivate your own firm's AI and turn it into an asset?
I believe it comes down to how you make that decision.
Thank you for reading until the end today.
"I tried using ChatGPT for work, but I felt its limits when it mattered most."
"I'd like to hear specifically how to build an AI dedicated to my firm."
"Honestly, what makes Yohakuru so different from ChatGPT? I'd like to see a demo!"
If you feel this way, please feel free to consult us via the link below.
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See you in the next article.