The Sengoku Era of Small Businesses: Exploring Enterprise Methods and Value Validation Through the Practice of Lean LaunchPad Nagaoka

The Unbundling of Functions and the Dawn of the “Sengoku Era of Small Businesses”

As I interview local business owners and run a small business myself, I increasingly find myself pondering a specific question: “What will happen to small businesses in the future?”

The backdrop to this is the advancement of digital technology and infrastructure. The spread of cloud services, social media, online payments, no-code tools, and generative AI is lowering the hurdles to acquiring a wide variety of management functions that were once indispensable for starting a business.

Writing, creative prototyping, website construction, market research, Customer Relationship Management (CRM), contract drafting, and simple script development—these tasks previously required outsourcing to specialists or establishing specialized internal departments. In the context of business administration, it can be understood that the integrated functions held within large enterprises have been “unbundled” (functional decomposition). As a result, an environment is emerging where individuals and small business owners can “assemble” (recombine) necessary functions into their own business organizations by combining multiple digital services, often for just a few thousand yen a month.

In particular, the rise of generative AI in recent years seems to be rapidly accelerating this change. The transformation of domains once abandoned because “we lack the specialized knowledge” into options where one can “first consult AI as an assistant to think and test” can be seen as an important turning point in the structure of small businesses.

However, a critical problem surfaces here: Is the fact that the hurdle to starting a business has lowered synonymous with it becoming easier to succeed in business?

I believe they are not necessarily synonymous. Rather, precisely because the barriers to entry have fallen, it becomes easier for numerous small businesses to enter the same markets. An environment is being formed where the structure of differentiation—”Why should our company be chosen?”—is rigorously questioned as businesses compete for the limited attention, time, and capital of customers.

In this article, I would like to dare define this new market environment as the “Sengoku (Warring States) Era of Small Businesses.”

This is not merely a metaphor for overheated price competition. It is a concept I am exploring in this essay to explain the structural environmental change where small businesses, armed with advanced management functions without relying on massive capital or organizational scale, stand side-by-side, searching for their own domains of survival.

The Commoditization of Hypothesis Generation and the Irreplaceability of Value Validation

When the initial cost of launching a business drops, the number of market entrants naturally tends to increase. For example, if you prompt generative AI to “propose new business ideas that could work in this region,” it will output a seemingly sophisticated business plan—including market analysis, target setting, and revenue models—in a matter of seconds.

Thanks to AI, the cost and hurdles of generating plausible business ideas have dropped significantly. However, no matter how well-structured a business plan may be, it remains nothing more than a hypothesis derived through data processing in a digital space.

Do the customers holding these problems actually exist? Do they recognize the problem as a critical pain point that needs immediate solving? And do they truly possess the willingness to pay for that solution?

Such realities cannot be proven solely by how accurately AI responds. While AI has made hypothesis generation easy, the process of Value Validation—testing whether that hypothesis holds true in the real world—cannot be easily replaced.

What becomes crucial here is choosing the right metrics, as emphasized in methodologies such as the Lean Startup. Many business owners fall into the trap of being misled by “Vanity Metrics,” such as website access numbers, social media impressions, and follower counts. While these may superficially indicate business progress, they do not necessarily prove actual business value or sustainability directly.

What small business owners must truly focus on are “Actionable Metrics,” such as actual purchasing behavior, retention or continued usage, and strong customer engagement. Precisely because generating hypotheses has become so effortless, the importance of Value Validation—questioning those hypotheses in the muddy reality of the field—has relatively increased.

A Shift in the Starting Point: The Pitfalls of AI-Centered Design and the Problem-Solving Process

Faced with the convenience of AI, business owners often unconsciously base their thinking on “what this technology can do.” Because they can generate images, they start a design business; because they can generate text, they start a writing business. While I do not deny that there are cases where innovation sprouts from technology (seeds), approaches where technological capabilities precede customer problems carry significant uncertainty.

In recent years, in fields such as design studies, the concept of AI-Centered Design (AICD)—where AI takes the initiative in analysis, optimization, and generation—is sometimes discussed in contrast to traditional Human-Centered Design (HCD). While AICD is effective in extracting patterns from massive datasets, placing AI’s processing capabilities at the center while leaving human context behind can lead to a backwards structure: forcefully fitting human problems into “outputs that AI can generate” as an afterthought. Therefore, rather than seeing HCD and AICD as a simple binary opposition, the perspective of “Relationship Design”—how to design the interaction between AI’s capabilities and human context—is being explored.

If we translate this into the context of local small businesses, it requires a return to the question: “Not what can we do with AI, but what can we do to solve the customer’s problem?”

At the same time, it is essential to ask: “Is that answer truly the business owner’s own answer?” Plausible marketing strategies or pricing generated through dialogues with AI are often nothing more than averages based on generalities. In the coming era, a stance that positions AI not as a complete substitute for decision-making, but as a catalyst to deepen one’s own inquiries, will be required.

The True Meaning of “Human-Centered” and Jobs-to-be-Done Theory

Even if we take the customer’s problem as our starting point, we must carefully approach the question: “Does being customer-centric mean doing whatever the customer says?” Concepts like Human-Centered Design (HCD) are often confused with “reflecting customer requests exactly as they are into products or services.”

However, thinking with humans at the center is not synonymous with accepting everything the customer says. Theodore Levitt’s metaphor, “People don’t want to buy a quarter-inch drill; they want a quarter-inch hole,” suggests that the essence lies not in owning the product, but in the utility it provides.

One way of developing this idea further is through Jobs-to-be-Done (JTBD) theory, advocated by Clayton M. Christensen and others. In JTBD theory, it is understood that customers do not simply purchase a product; rather, they “hire” the product to get a “job” done (a task to be resolved or progress to be made) that arises in their lives or work.

When a customer says, “I want this feature,” adding that request as a feature verbatim might sometimes lead you further away from solving the true problem. What is critical is grasping the underlying, latent objective (the job) behind the customer’s words: “Why are they asking for this?” and “What are they trying to accomplish?”

Customer Development and Its “Translation” for Small Businesses

To understand problems and objectives that even the customers themselves cannot clearly articulate, it is necessary to converse with actual customers and observe the field. The concept of Customer Development, proposed in the startup sphere, emphasizes not launching a completed product into the market from the start, but rather an ongoing process of continuously verifying hypotheses (Value Proposition) through dialogue with customers.

The challenge we face here is: “How can enterprise methodologies be translated and adapted for local small businesses?”

Knowledge frameworks like Customer Development, Lean Startup, HCD, and data utilization are by no means exclusive to large corporations or specific advanced tech companies. However, if the methodologies developed for larger organizations—such as market research with a budget running into tens of millions of yen, or massive system development—are brought directly into a small business, they might fail to function effectively.

What is needed is not a formal imitation of the methods, but a “translation” of the underlying principles into a local context. For example, while a large corporation might conduct a survey of 1,000 people, a small business owner can visit the actual environment of about 10 prospective customers and conduct deep interviews. Before developing a perfect system, they can show customers hand-drawn sketches or a prototype with minimal features (MVP: Minimum Viable Product) to measure reactions. Based on the learnings acquired, they can flexibly adjust the direction of the business (Pivot) if necessary.

It is precisely because small business owners have a close physical and psychological distance to their customers that they can incorporate this verification process into their daily interactions and operations.

“LLP Nagaoka” as an Object of Practice and Observation

In considering how enterprise methods can be translated and applied locally, a program I have focused on is the entrepreneurship development program “LLP (Lean LaunchPad) Nagaoka,” held in Nagaoka City.

Lean LaunchPad, developed by Steve Blank, is widely known as an entrepreneurship education method originally designed with high-tech startups and venture capital funding in mind. Therefore, it is sometimes perceived as being far removed from local business. This article also does not intend to present LLP Nagaoka as a “success formula for regional business” or claim that applying its methods guarantees success.

Rather, for me, LLP Nagaoka is an extremely fascinating practical case and object of observation for considering “how methodologies developed in the startup and enterprise spheres can be translated for local small businesses.” The process in which participating teams construct hypotheses with their own hands, have them shattered through dialogues in the field, and then correct their course—from my own participation and observation—contains universal insights for discovering true problems and building a business without relying solely on capital.

The Relative Value of “Raw Context” in the AI Era

As the environment where AI can instantly integrate and output massive amounts of existing information on the internet becomes more established, the question of “What value does primary, field-level information hold in the AI era?” grows in importance.

A momentary hesitation shown by the interviewee. Unspoken customs existing in the local community. Subtle changes in regular customers that only the store owner can notice. Such qualitative nuances and Raw Context are difficult for AI to automatically grasp unless the business owner appropriately provides them to the AI as data or context. Precisely because the outputs of search engines and AI tend to converge on “optimal solutions as generalities,” the relative value of Primary Information obtained by walking into the field and engaging in dialogue with customers is considered to be rising.

What is important here is not pitting AI against primary information. By leaving the organization of general information and the creation of draft hypotheses to AI, operational costs are lowered, and the time and energy generated can be concentrated on “field time directly facing the customer.” Then, the unique context acquired from the field can be brought back into dialogue with AI to update hypotheses.

Possessing the agility in decision-making to adapt a business and its hypotheses based on primary field information—this, perhaps, will become the critical strength of local business owners in the Sengoku Era of Small Businesses.

Conclusion: Towards a Media That Shares “Questions”

The Sengoku Era of Small Businesses does not simply mean a harsh era of intensifying competition. It can also be viewed as an era full of possibilities, where a single business owner can utilize knowledge and technology that once belonged only to large organizations, creating value in their own unique way.

Our platform, “Chuetsu Life,” has thus far covered attractive local stores and business owners, disseminating information. Moving forward, in addition to this role as an information platform, we wish to explore our role as a media outlet that observes the actual field of local businesses and shares the essential “questions” of business creation.

Who do we listen to?

What do we verify?

How small should our tests be?

What do we consider as results?

And what do we change based on those results?

The process of organizing these questions, building hypotheses, validating, and revising them is ultimately something the business owner must proactively undertake. However, there is no need to carry the burden of finding the answers entirely alone. Going back and forth between the general knowledge obtained from AI and the specific realities of the local field, having someone to think deeply alongside you about “What is truly necessary for this business, in this region?” should serve as one valuable option for navigating this era of change.

Chuetsu Life will continue to observe the field as a companion, recording what local business owners are thinking, what they are testing, and how their businesses are evolving.

References & Related Materials

Lean LaunchPad / Steve Blank

Steve Blank「A New Way to Teach Entrepreneurship – The Lean LaunchPad at Stanford」

Steve Blank「The Lean LaunchPad at Stanford – The Final Presentations」

Stanford University｜Lean LaunchPad

Stanford University「ENGR 245 – Lean Launchpad」

Stanford University｜Steve Blank⁠

NSF｜I-Corps

NSF｜Innovation Corps (I-Corps) Hubs Program

Christensen Institute｜Jobs to Be Done

Christensen Institute｜Jobs to Be Done

Theodore Levitt｜Marketing Myopia

Harvard Business Review｜Marketing Myopia — Theodore Levitt

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