Hiring AI has already begun.
Not adopting AI — assigning AI employees. AI employees with names and roles work with one another, hand work on, and carry it through to a result. GIZIN FLEET is the operating layer that turns AI from a tool you use into a team you work with.
As of 2026, many companies have begun putting AI to work,but most stop at partial support for individuals.
Source: IPA, 「DX動向2026」 [DX Trends 2026], Figures 3-6 and 3-14. Among companies that have adopted, are trialling, or are considering generative AI: “used by individuals in their work” 63.3%, against “built into a department’s business process” 11.9% and “built into a company-wide service” 18.6%. Among companies that saw a meaningful effect from adopting AI: “work became more efficient or faster” 91.6%, against “revenue or profit improved” 3.9%.
But the real value of AI at worklies in AI driving the work itself.
Because with support for individuals, human hours are the ceiling — and only once the flow of the work has been moved over to AI do results come free of human hours.
What does it mean for AI to drive the work?
It means several AIs working together to complete the work. The same as people do.
Between “support for individuals” and “the flow of work being AI-led” there is a wide gap.
How do you get to the point where AI drives the work?
If we compare operating AI
“Press the accelerator and it goes; press the brake and it stops. Turn the wheel right and it turns right, turn it left and it turns left.” Matching what a human expects to what the AI does — something this obvious — becomes difficult the moment the workflow is AI-led.
Press the accelerator and it turns right; turn the wheel left and it reverses. Things like that easily happen. They happen because there are no rules of coordination.
GIZIN has a year’s track record as a company that runs its work AI-first. The rules of coordination, built up in practice.
- 01A human requests the problem to be solved, and AI designs the workflow.
- 02A human checks, decides and approves, and AI drives the workflow.
- 03Detect when AI has stalled, and keep the work moving.
- 04Hold back AI that runs away with itself: set the shape of the phases and keep control, so a wrong decision does not spread.
Systematized as “GIZIN OS Fleet,” and run day to day.
Beyond the above, many other means of management are in place so that AI can do the obvious things as a matter of course.
How This Differs from Claude Code and Codex
Running several AIs side by side, giving them roles, having them talk to one another — official tools like Claude Code and Codex already do much of this.
The real difference with GIZIN Fleet is what it is optimized for.
The official tools are optimized for getting AI to do the work. GIZIN Fleet is optimized for running a company with AI.
Optimized for a personified UX that lets you build AI employees — your own, and your customers'
Someone is assigned to you.
The AI you give your instructions to every day has a name, a role and a face, as the one responsible. Day by day it builds up experience — the knowledge of the work, the know-how — and gets better at it, until the two of you barely need to spell things out. The same holds for the AI employee assigned to your customers.
AI is kept from running away with itself.
Work is managed on cards. Before anything begins, four things are settled: what you want a human to decide, how far the work goes and where it stops, what counts as finished, and what went wrong last time. Only when all four are agreed does the instruction to start go out.
Failures are prevented by putting human judgment in the way.
You set checkpoints where AI cannot be left on its own — creative work, or anything going out that involves a contract or a sum of money. By deciding in advance which points are irreversible or expensive to get wrong, you can place a human check in front of them. A failure you can predict is a failure you can design out while you are still planning.
The results come back as numbers.
Over our own most recent 12 days, 1,445 items became cards: 309 were started and 331 were rejected. There were 177 cases where an AI reported the work as finished when it was not. Time stalled waiting on a human decision came to a median of 112 minutes. Whether the AI is working is something we read in numbers, not impressions.
What We Actually Provide
GIZIN Fleet is not a SaaS product or a piece of software.
How to divide the work, which AI employee to hand each piece to, how to commission it, how to inspect it, where to stop it, and what to keep on record. What we help you do is move that way of working itself into your own environment. We stay alongside your people, taking the time it takes, until they can do it without unease.
Does your company have rules that no other company has?
Teaching those to AI is the first step.
It would be a problem if what you taught were forgotten, wouldn’t it? AI has to be made to remember.
Once the work is under way, AI will come up with reflections and improvements from doing it. Having it remember those too is how the work gets better day by day.
Hand over work that is perfectly designed from the start, and make no mistakes. That would be ideal, but real work has many exceptions and a great many failures. This is where AI differs from a tool. Teach it, and what it can do grows.
The important point is to stop failures at the human’s final check, and not let them be carried out.
Let AI fail plenty and accumulate the know-how, and the failures gradually decrease.
As what it can do grows, so does the work.
Only here do you reach the stage of coordinating several AIs. You design which AI should take which work, and bring the flow of work down into the shape of phases.
Once you have this set to run on schedule, all that is left for a human to do is the final check.
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