We now stand at a historic turning point. The era of compressed growth is fading, and a post-growth era has arrived where low growth and uncertainty have become the norm. The days when organizations could absorb inefficiencies through quantitative expansion are over. Organizations now face the urgent challenge of generating optimal performance within constrained resources. Simultaneously, the technological revolution, epitomized by artificial intelligence, is fundamentally reshaping the very paradigm of organizational management. Organizational management in the post-growth era transcends the mere reduction of costs or automation of processes.

First, at the organizational structure level, AI overcomes the limitations of hierarchical structures and enables the transition to networked organizations. AI-based decision support systems redefine the role of middle management, revolutionize information flow within the organization, and promote cross-boundary collaboration. However, this is not simply about AI replacing humans; it is realized only when the judgment of human managers and AI's data analysis capabilities are combined in a complementary manner. 

Second, at the organizational behavior level, AI provides tools to understand and manage member motivation, leadership, and group dynamics in new ways. Real-time performance monitoring, personalized feedback, and predictive talent management all become possible through AI. However, for these technological possibilities not to undermine trust and autonomy within the organization, human-centered values and AI efficiency must be balanced. How to interpret AI-generated data and connect it to member development and organizational growth remains the responsibility of human managers.

Third, at the organizational culture level, the AI era demands a shift toward learning and adaptive organizations. In the uncertainty of the post-growth era, organizational survival hinges on how quickly it adapts to change. AI can accumulate and share knowledge within the organization and strengthen mechanisms for learning from failure. However, a true learning organization isn't built by technology alone. It requires a culture where members embrace AI not as an object of fear but as a collaborative partner, and where continuous learning and experimentation are encouraged.

Ultimately, organizational management in the post-growth era boils down to the question of how to design and implement collaboration between humans and AI. AI is not a panacea, nor can it fully replace humans. Rather, a complementary relationship must be formed where AI compensates for human limitations, while humans handle contextual judgment and ethical reflection beyond AI's capabilities. This book will be a timely rain for all who dream of wise organizational management alongside AI development in the post-growth era.