The Single Design Philosophy That Will Change AI Adoption in the Labor and Social Security Attorney Industry with Yohakuru
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 Labor and Social Security Attorney Corporation: 80% reduction in subsidy check tasks, over 50% reduction in document creation and procedural tasks
Asterisk Labor and Social Security Attorney Office: 95% reduction in work rules tasks
Four Season Labor and Social Security Attorney Corporation: 80% reduction in payroll calculation tasks
Click here for free consultations and demos regarding Yohakuru
↓
https://www.yohakuru.com/#contact
Introduction
In this article, I will write about something I have always wondered about regarding the labor and social security attorney industry.
That is, "Why hasn't it penetrated the labor and social security attorney industry this much, even though ChatGPT, Claude, and Gemini are being talked about so much in society?"
DX, AI utilization, and operational efficiency.
Even though these words have been flying around for a long time, the reality on the ground hasn't changed that much for many offices.
This is not because the labor and social security attorneys are lazy, nor because they are weak with IT (though that might be a small factor, haha).
To conclude, it is because there is a wall in the very structure of the industry that cannot be overcome by general-purpose AI alone.
And the service that went to break down that wall head-on is Yohakuru, operated by our company, Lean Stack.
In this article, I will write about the single design philosophy that Yohakuru is seriously using to change AI adoption in the labor and social security attorney industry.
Why work cannot be done with general-purpose AI alone in the labor and social security attorney industry
First, let me organize this.
ChatGPT, Claude, and Gemini.
They are undoubtedly smart.
They write beautiful text and handle research.
They can answer almost immediately if it is at the level of textbooks on the Labor Standards Act or social insurance.
They are at a level where they could even pass the entrance exam for the University of Tokyo's Science III as the top student, after all...
But the moment they try to bring it into full-scale labor and social security attorney work, almost all the experts say this.
"It works up to a point, but the answer drifts at the crucial moment."They say.
This isn't a problem of AI's intelligence.
Labor and social security attorney work is a job where, if you have Expert A, Expert B, and Expert C in the same industry, they will produce different outputs for the same case.
The law is the same.
The system is the same.
Yet, the deliverables produced are different.
General-purpose AI lacks the mechanism to incorporate the 'true nature of what creates those differences'.
This is the real gateway to why labor and social security attorney DX has not spread across the entire industry.
The invisible variable of 'the expert's values' that appears strongly in work rules
The easiest example to understand is work rules.
To be honest, work rules clearly reflect an expert's values.
Some experts write 'work rules that stand on the side of protecting the company,' while others write 'work rules that clearly define employee rights.'
How far to go in writing disciplinary provisions.
How strict to make the operational rules for leave of absence systems.
How to handle side jobs.
What design philosophy to have for fixed overtime pay.
These are all the expert's management philosophy.
The laws are, of course, the same.
But even if you create rules for the same company based on the same laws, the resulting work rules differ depending on the expert.
Here lies the first limitation of general-purpose AI.
ChatGPT does not know 'your philosophy on work rules'.
It has no way of knowing 'whether you want to place the center of gravity on labor or management' or 'which school of thought you belong to regarding how much risk you are willing to take'.
That is why, when you ask ChatGPT to create work rules,
it only returns generic advice that is neither harmful nor helpful.
The output ends up being something that 'would be the same no matter which office produced it.'
From the perspective of a labor and social security attorney, that is fatal for a deliverable.
The reality lurking in payroll: '100 different rules for every company'
Next is payroll calculation.
This is a more detailed matter than values.
Payroll calculation is completely different for every client.
Company A and Company B have different deduction items.
The design of allowances is different.
The closing dates are different, and the payment dates are different, too.
And that is not all.
Even within a single company, the rules are fragmented.
Full-time employees have this calculation rule.
Part-timers have this rule.
Temporary staff have yet another rule.
Executive compensation is handled differently still.
—These exist for as many clients as you have.
There are as many correct answers as there are companies,
and as many exceptions as there are employment types.
Even if you throw this at a general-purpose AI and say 'calculate this properly,' it will never work.
It is frankly impossible to reproduce the 'map in the attorney's head' via prompts for an AI that requires you to explain the rules from scratch every time.
By the time you have written it all out,
it is faster to calculate it yourself.
This is the reality that has been repeated over and over in the field of the labor and social security attorney industry.
In other words, labor and social security attorney work was a 'mass of tacit knowledge.'
If you have read this far, I think you can already see it.
The work of a labor and social security attorney is driven by the individual practitioner's experience, judgment criteria, agreements with clients, and the accumulation of past interactions.
Values, preferences, style, and judgment history.
Everything exists only inside the practitioner's head.
This is both the essential strength of the labor and social security attorney profession and its structural weakness as an industry.
This is because, as long as it is in their head, it all disappears when that practitioner retires.
Even if you hire a new recruit, it takes years to pass on that tacit knowledge.
And in the meantime, the veterans burn out first.
The fact that 'the practitioner's tacit knowledge has not been turned into an asset' is the true root cause of why AI has not penetrated the labor and social security attorney industry.
This single point is the real reason.
It wasn't that the tools were bad.
Industry knowledge was only stored inside people's heads.
Therefore, there was nothing to give to the AI in the first place.
This is the true nature of the structure.
The single design philosophy chosen by Yohakuru
Let me talk about Yohakuru here.
What makes Yohakuru decisively different from other AI tools is that it is fully committed to a single design philosophy.
That is the philosophy of not just 'making you use' a general-purpose AI, but 'loading the office's brain' itself onto it.
To put what Yohakuru does in one word, it is this.
The practitioner's values, rules for each client, past documents, judgment history, and preferences for formats
—by having the AI learn these, we create the office's 'second brain'.
It is the image of
giving the office itself another brain.
Tacit knowledge that previously existed only in the minds of veteran staff members accumulates as an asset within the AI.
When it comes to DX for labor and social security attorneys, most people in the world stop at the idea of 'buying and using good tools'.
However, Yohakuru's design philosophy is the opposite.
Instead of 'fitting the office to the tool,' it is 'loading the office onto the AI'.
This is exactly what makes it fundamentally different from other services in the industry.
What happens in an office when you change the design philosophy?
What happens on the ground when you commit to the design philosophy of 'loading the office's brain'?
Let's look at it in order.
Personal dependency disappears along with its structure.
First, there is personal dependency.
In traditional labor and social security attorney offices, operational knowledge 'evaporated' every time a veteran staff member quit.
With Yohakuru, operational knowledge accumulates on the AI side.
Even if the staff changes, the office's brain does not.
This alone eliminates one of the biggest risks in office management.
Recruitment hurdles drop to a whole new level.
Next, this is an even bigger story.
Recruitment in the labor and social security attorney industry is, frankly speaking, a thorny path.
Since labor and social security attorney work is almost a specialized profession, you don't just want clerical staff; you want to hire people with a certain level of labor knowledge and experience.
However, that demographic is being fought over in the recruitment market, and it's hard to get applicants.
Moreover, even if you do manage to hire someone, it takes years for them to learn the ropes.
In offices where Yohakuru is implemented,
the structure here changes dramatically.
Since the operational knowledge resides on the AI side, what you require from new hires will no longer be "five years of labor experience," but rather "someone who can use AI correctly."
When hiring requirements change, the total pool of applicants jumps significantly.
For labor and social security attorney offices struggling with recruitment, this is more effective than you might imagine.
The ceiling for revenue physically rises.
And finally, there is the impact on management.
The fact that dependency on specific individuals disappears and hiring hurdles are lowered means, in short,that the office becomes capable of scaling.that is what it means.
Even if you increase the number of client companies, quality does not drop.
Even if you bring in new staff, they get up to speed quickly.
Veterans can focus on high-value-added tasks.
In offices that have introduced Yohakuru,
this kind of structural transformation is actually happening.
When a labor and social security attorney office is redesigned with AI, the revenue ceiling physically rises.
This is the view that only offices that have changed their design philosophy have attained.
God is in the details.
Let me talk about something a little abstract.
The reason I have always said that general-purpose AI is not enough for the labor and social security attorney industry is that the work of a labor and social security attorney is work that deals withmoney and the law.that is why.
If you make a one-yen mistake in salary figures, the trust of the employees will crumble.
If you make a mistake in a single sentence of the rules of employment, it leads directly to management risks for the company.
In the field of labor and social security attorney work, fine settings, fine exceptions, and fine differences in values determine the quality of the output.
The phrase "God is in the details,"
I believe it applies precisely to labor and social security attorney work.
And the God that resides in those details
should not be kept inside people's heads.
Quitting. Forgetting. Inability to hand over work.
As long as we rely on what is inside people's heads, the total knowledge of the industry will never accumulate.
That is precisely why we must quickly build a system to turn it into an asset using AI.
I believe this is a design philosophy that
cannot be avoided when changing AI adoption in the labor and social security attorney industry.
Summary
Let me organize this one last time.
The real reason AI has not penetrated the labor and social security attorney industry is not the tools, nor is it how the attorneys use them.
There was no mechanism to load the attorney's values, preferences, and tacit knowledge into AI.
That is all it is.
General-purpose AI is certainly smart.
But labor and social security attorney work will never reach its core unless you load the 'attorney's mindset' into it.
Yohakuru is a service that set out to break this barrier head-on.
Load the firm's brain into AI and
turn it into an asset as a second brain.
Eliminate reliance on specific individuals, lower hiring hurdles, and
physically raise the ceiling on sales.
This is the one design philosophy that Yohakuru is fully committed to.
AI utilization for labor and social security attorneys is not a tool problem, but a design philosophy problem.
The landscape of the industry will change starting with the firms that realize this one point.
And making that the industry standard is where Yohakuru is seriously aiming to be.
Thank you for reading until the end today.
'Can my firm's specific preferences really be loaded into AI?'
'I'd like to hear specifically how to turn an attorney's tacit knowledge into an asset.'
'Honestly, I want to see a demo to understand what makes Yohakuru so different from other AI tools!'
If you are interested, please feel free to consult us from the link below.
We offer free demonstrations and show real-world examples of increased efficiency.
Click here for a free consultation!
↓
https://www.yohakuru.com/#contact
Well then, I will see you in the next article.