Vision Making in the Age of AI

With the spread of generative AI and automation tools, companies can more easily improve efficiency, reduce costs, and process information faster. At the same time, people on the ground ask whether their work will be replaced and where the meaning of human work will remain.

This anxiety cannot be resolved by AI performance alone. It becomes stronger when the organization has not defined what it values in work. If the purpose of AI adoption remains vague, efficiency may improve, but employees cannot understand what future they are moving toward.

Vision making in the age of AI is not about presenting attractive words about the future. It is about deciding what to entrust to AI, what people will own, and what value the company will continue delivering to society. From BOEL's Design the Decision perspective, AI use begins with the design of organizational decision-making.

AI changes routine work and information processing significantly: drafting text, generating images, organizing data, responding to inquiries, forecasting, and supporting analysis. These areas will continue to become more automated. But not all work disappears. Rather, the value that people should create becomes more clearly questioned.

Human value lies in judgment, responsibility, empathy, contextual understanding, relationship building, and the ability to imagine the future. AI can present options based on past data and patterns, but people decide which choice the organization should make and what responsibility it will carry.

Therefore, organizations in the AI era cannot stop at asking which jobs will remain. They must put into words what value is created when people are involved, and redesign work so that value can be expressed.

When advancing AI use, the first decision is not which tool to choose. The company needs to separate what to automate with AI, what to augment with AI, and what people must protect.

The questions can be organized into three. First, which work requires accuracy and speed and follows a clear procedure? This is easier to entrust to AI. Second, where can AI support human judgment or creativity? Third, which areas involve responsibility, ethics, customer trust, or organizational culture and therefore require people to hold the final judgment?

Without this separation, AI adoption becomes a state of using AI because it is expected. Vision making creates decision criteria that prevent AI use from becoming the goal. The clearer the company's chosen future becomes, the more AI can augment organizational value rather than weaken it.

A PROJECTS case addresses the same challenge. THINKCYTE is a deep-tech startup that uses AI and high-speed imaging technology to analyze and identify cells quickly and accurately. In its early stage, it had advanced research technology and a future vision held by its co-founders.

BOEL connected that technology to society not merely as analytical performance, but as a brand that expands possibilities in research and medicine. By translating value that was not yet widely recognized into language and experience for imagining the future, the project created an entry point for society to understand highly specialized technology. -> [Read the project](https://www.boel.co.jp/projects/thinkcyte/)

The same applies to organizations in the AI era. Explaining technical functions alone does not move people. Whose possibilities does the technology expand? Which future does the organization choose? Only when that is put into words does AI connect to vision.

A vision for the AI era does not work if it remains only on posters or in management policy documents. It becomes an organizational decision axis only when it is translated into AI usage policy, work processes, evaluation systems, and customer touchpoints.

For example, if AI is used in customer support, the company must decide not only how to improve efficiency, but where people should remain involved. If AI is used in recruitment, transparency of evaluation and candidate experience must be protected by design. If AI is used in planning or production, the organization must decide who holds final responsibility and what will count as originality.

When these decisions are aligned, AI use becomes not a series of departmental experiments, but a brand experience. Customers and employees sense the company's attitude toward technology through daily touchpoints.

The first step is not to introduce AI tools all at once. Begin by auditing work and separating tasks that can be standardized, work that requires judgment, and work where relationships with customers or employees matter.

Next, define the value created by human involvement: quality of judgment, depth of dialogue, ethical responsibility, customer understanding, and the ability to imagine the future. Then create rules for AI use, clarifying where AI may be used, where it should not be used, who makes final decisions, who is accountable, and how data is handled.

Finally, turn that policy into a narrative that can be understood inside and outside the company. Connect the reason for using AI not only to efficiency, but to the future the company wants to realize. By reviewing it regularly and updating it with learning from the field, AI use becomes an organizational strength.

BOEL does not see vision making in the age of AI as simply organizing how to use AI. What matters more is deciding what AI will change, what the organization will protect, and what value people will continue to own.

AI amplifies weak decision criteria. If it is introduced while the purpose is vague, the workplace becomes confused and the meaning of work becomes harder to see. On the other hand, when the future vision is clear, AI can expand human creativity and judgment, and help deliver new experiences to customers and society.

Design the Decision is the method for connecting technology adoption to organizational decision-making. In the AI era, companies need more than the pursuit of the latest technology. They need to design which future they will choose and what roles people and AI will play toward that future.