Adding AI does not make a company faster if it cannot measure.
The same instruction gives different results depending on who writes it and on the day. While the data stays scattered across SaaS products, you cannot measure what happened. If you cannot measure you cannot fix, and if you cannot fix, improvement does not accumulate. Adding AI to work that rests on individuals only makes inconsistent work inconsistent faster.
When you cannot measure, you cannot tell improvement from luck. When the data is yours, what you fixed becomes the material for the next decision.
This wheel gets faster the more it turns.
When the data is yours, plan, do, measure and improve turn on data. One improvement makes the next one easier, and accumulated data makes the next decision faster. Only the first push is heavy.
The gap against a company whose wheel is already turning widens by multiplication, not by addition. In two or three years that gap becomes too expensive to close.
Each turn makes the next turn faster. If the data stays in someone else’s container, the wheel breaks at the measuring step. A broken wheel does not move, however hard you spin it.
Four steps to get the loop turning.
Inventory of work and data
Settle first what exists, where it is, and who holds it. Skip this and every later collection has to be done again.
Bringing it in, and joining it
Collect scattered data into your own database, so the same thing can be called the same name. Only once the names agree can anything be reconciled.
AI instruction inside the company
So the people on the floor can reproduce, by themselves, an operation shaped to their own work. Left outsourced, it stops the day the engagement ends.
Quality gates and automatic operation
Remove variance with verification gates, then put it on a footing where AI runs it daily. Do not make it depend on human attention.
Systems that are actually running.
When quotation and billing live in separate tools, the numbers drift at every transcription. We made contract management through sales management one continuous flow, so there is no room to drift.
Billing cannot be changed once locked, so settled numbers do not move afterwards. Every step refers only to master data.
We run the same mechanism ourselves, in real time.
Seven roles run as one flow. When something stops we know at once, and when we fix it the result changes that same day. What differs between companies is only the service they deliver, and the rest can be rearranged and moved.
We could build this improvement cycle because we build our systems ourselves. With off-the-shelf SaaS lined up, we would not decide how data is held or how it is measured. Because we built it, we measure what we want to measure and fix what we want to fix.
Only the role that watches from outside is kept off the shared path. On the same path it falls with everything else, and then no one notices.
Able to design the company itself.
Not simply an AI specialist. Someone who can design how a company works across law, governance, new business and IT, handling AI as a tool.
His mother served as representative director of the Japanese arms of AOL, Netscape and ICQ, and his father was an engineer at NEC. He grew up watching, at home, the internet change the shape of companies.
- Degree
- Juris Doctor (Keio University Law School)
- Undergraduate
- Sophia University Faculty of Law, top of department, graduated in three years
- Listings
- Listing preparation at 20+ companies, 6 of them listed
- Advisory
- 50+ companies supported on DX and corporate governance reform
- Languages
- Japanese, English, Chinese, Malay
We decide together where to start.
You do not know where the data is. There are too many SaaS products. You tried AI and it did not stay in the work. We start by taking inventory, then design the order in which to bring things in.