The Sense That Knowledge Work Would Eventually Be Commoditized
After working on the front lines of urban development and residential development at a major real estate company, I studied management at a U.S. business school and then worked as a management consultant on corporate strategy, quantitative analysis, and the formulation and execution of business plans for large companies. The template of so-called knowledge work — that is where I learned it thoroughly.
But at the same time, I felt a certain unease. Much of the work in consulting and investment banking — wouldn't it eventually be commoditized? Of course, AI as it exists today did not exist back then. Even so, I had a sense that work which depends on the gap between those who hold information, analytical methods, and management knowledge and those who do not would, at some point, see its value erode.
Of course, closing that gap had value. Bringing in cases that were not widely known, organizing complex situations, and putting management issues into words helped companies make decisions. But I could not believe that this would become a lasting advantage.
So at the time, I was drawn to fields where the methodologies of management theory and consulting had not yet fully penetrated — apparel, D2C, retail, e-commerce, and branding. In those fields, there is a front line that does not move on logic alone. Product planning, production, inventory, stores, e-commerce, logistics, customer service, brand expression. Human sensibility, on-the-ground judgment, and gritty operations always remain. I thought that if managerial thinking and front-line practice were combined, it might be possible to create genuinely new change.
What Moves Large Companies Is Organizational Dynamics, Not the Business
After that, I worked at operating companies in corporate planning, new business development, D2C, apparel planning, branding, and retail and e-commerce operations. But looking back over more than twenty years, I was not able to bring about the kind of large-scale change I had hoped for. E-commerce advanced, and D2C spread. Even so, most of the change stayed within the range of what was expected.
One reason is that inside large companies, organizational dynamics exert a stronger force than the business itself.
That said, this is not something to be dismissed out of hand. In Japanese corporate organizations, what was needed was the ability to read the interests of different departments, anticipate objections, lay groundwork repeatedly, and move the organization while building consensus little by little. Rather than breaking through head-on, you move the organization by inching toward it. That, in itself, was a specialized skill for producing results within a Japanese company. The Japanese "company" is that kind of creature.
But over-reliance on that skill has also slowed down the transformation of Japanese companies. In many large companies, before facing change, the first question is how to get it through internally. Discussion about how to improve the business is, before anyone notices, replaced by discussion about internal coordination. As a result, although words like CX and DX have been raised again and again, in practice the outcome has often stopped at improvements that are merely an extension of existing operations.
The problem is not coordination ability itself. Coordination is, properly, a means to push transformation forward. But when coordination becomes the goal, transformation gets rounded down into something small. Discussion meant to start something new turns into discussion about not hurting the existing organization. As a result, the company as a whole does not change much.
Large companies have capital, talent, brands, business partners, a customer base, systems, and logistics networks. These are major strengths, not something small and mid-sized companies or startups can easily imitate. But the way the organization moves in order to put those assets to work is heavy. Preparing documents, holding meeting after meeting, obtaining approvals, avoiding risk, protecting the existing structure of authority. In that process, the speed of change drops.
In the AI Era, the Remaining Value Is the Experience of Running a Business
And now, in the AI era, the unease I felt twenty years ago has taken on considerable reality. Part of knowledge work — research, analysis, writing, comparative review, document preparation — can now be supported by AI in a short time. The value of what you know, what you can organize, and what you can turn into documents has relatively declined, and the work that is easiest to formalize is being absorbed by AI first.
In the AI era, this structure becomes quite harsh. The certain standard of output that white-collar workers have carried until now is no longer the exclusive domain of a limited set of specialists.
So what value remains? It is the experience of actually running a business. Have you touched primary information — do you have a feel for customers, products, the front line, numbers, failure, and responsibility? And, based on that experience, can you use AI to change the shape of operations and the organization?
Experience That Was Written Off Now Holds Value Again
Here, the experience of veterans — who have tended to be written off as past their prime — begins to take on a completely different meaning. Of course, this is not to say that simply being older has value. People who only cling to past successes, and people who have run the organization on coordination and approvals alone, will, if anything, have a harder time in the AI era.
On the other hand, the experience of people who have been through the front line, failed, taken on responsibility, and understood the complexity of customers, products, and organizations firsthand is given value again by AI.
AI does not discard that experience as something old. It has the potential to structure it, extend it, and turn it into an engine of business transformation.
What becomes important in the AI era is the ability to steer the business and the ability to deeply understand front-line operations and the people on the front line. The judgment to question the existing shape of work, break it if necessary, and reassemble it into the next form. And the ability to understand how the front line actually moves. These two should never have been separated. A leader who drives transformation needs both the nerve to make decisions and the ability to understand the structure of the front line.
The Question Is Whether You Can Describe the Business End-to-End
Conversely, the coordination-type white-collar worker in the middle will have a harder time. Putting documents in order, running meetings, coordinating interests among departments, and crafting plausible-sounding explanations for internal audiences. This kind of work has, until now, carried great weight inside large companies. But once AI lowers the value of organizing information and producing documents, the question becomes what those people can actually decide and what they can actually change.
What matters here is whether you can describe your own company's business end-to-end. Where revenue is generated, which operations affect which divisions, and where inventory, information, or decisions are stagnating. Whether you can explain the mechanism of the business as a whole — including customer touchpoints, operations, systems, data, and the organization's quirks.
Ask AI, and it will produce operational improvement proposals, requirement definitions, flow diagrams, and proposal documents. But whether they can actually be used in reality is a separate matter. Front-line constraints, organizational quirks, customer reactions, the limits of the system, the sheer number of exceptions to handle. Without knowing these things, AI's output tends to become a proposal that is clean but unusable.
To make full use of AI, before the technique of how to ask, you need the ability to break reality down. Not stopping at surface-level explanations, but continuing to dig into why that operation still exists, why things stall at that department, and why it remains inconvenient for the customer. This ability is not mere logical thinking. It is a structural grasp backed by experience.
AI Narrows the Knowledge Gap and Widens the Experience Gap
AI does not make experience unnecessary. If anything, it makes the gap in experience clearly visible. Even someone with shallow experience can produce a plausible-looking answer using AI. But whether it lands on the front line is a separate matter. Conversely, when someone with front-line experience uses AI, the speed of judgment, design, execution, and revision rises dramatically.
AI narrows the gap in knowledge while widening the gap in experience. AI makes it impossible for large companies to hide their limits. And at the same time, it turns the experience of veterans who have been written off back into an engine of business transformation.
What will hold value from here on is not the person who knows. Not the person who can explain, nor the person who can produce documents. It is the person who knows the front line, asks "why" again and again, grasps the structure of the business as a whole, and can use AI to change the shape of work.
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