Boost Health Raises ¥150M to Eliminate the 'Personalization' of Human Capital Management and Prevent Over-reliance on Managers

Boost Health, provider of the 'BOOST' service that implements a next-gen 'Talent Success' model, has raised ¥150 million. They aim to advance their AI product and enhance support for enterprise clients, addressing the challenge of human capital management being overly dependent on individual managers.
資金調達NQ 79/100出典：PR Times

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  • 📰 Published: March 27, 2026 at 23:26
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Don't let managers shoulder employee success alone. Boost Health raises 150 million yen to eliminate "individual-dependency" in human capital management.

Boost Health Inc. (Headquarters: Chuo-ku, Tokyo; Representative Director and CEO: Ayaka Haga), provider of the "BOOST" service for implementing the next-generation management model "Talent Success," has raised a total of 150 million yen through a third-party allotment of shares with Genesia Ventures, Inc. and WPower Fund as underwriters. The cumulative amount raised is 250 million yen. Incubate Fund and DG Incubation participated in the seed round.

With this funding, the company will promote the advancement of AI products and strengthen its introduction support system (sales and customer success) for enterprise clients.

[Background] Why is investment in human resources becoming a management issue now?

With the spread of AI, the roles required of people in companies are changing significantly.

As routine tasks are automated, humans are increasingly required to make autonomous judgments and creatively solve problems. As a result, a company's competitiveness has come to depend heavily on "how many people can act autonomously."

Therefore, our company defines the human resource contribution to corporate value as follows:

Corporate Value (Human Resource Contribution) = Strength of Management Structure × Ratio of Autonomous Talent

Factor decomposition of corporate value (human resource contribution)

Companies have progressed in investing in "management structures," such as organizational design and evaluation systems. However, can it be said that sufficient investment has been made to increase the "ratio of autonomous talent"—ensuring employees can act autonomously and produce results?

This issue is becoming even more critical in the AI era. Organizations with a low ratio of autonomous talent risk having their work displaced by AI, while those with a high ratio will expand their competitive advantage by using AI as a tool.

So, how do you increase the ratio of autonomous talent?

To increase the ratio of autonomous talent, in addition to appropriate hiring, investment to ensure existing employees "succeed with high reproducibility" is essential. The accuracy of this support for employee success depends on three factors: Quality of intervention/support (Q), Individual Optimization (P), and Reproducibility (R).

For example, suppose there is a very supportive manager who provides appropriate advice in 1-on-1s and encourages action. The quality of intervention and individual optimization are high, but reproducibility is low. If that manager transfers, the effect disappears and is difficult to spread across the organization. Conversely, standard training can guarantee reproducibility, but individual optimization is limited.

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