The Four Pillars of Corporate AI Transformation: Executive Awareness and Organizational Evolution

According to an article from Huxiu, a discussion with two Chinese AI experts has highlighted four core insights for successfully adopting AI in businesses. The conversation, rich with practical advice, covered topics ranging from transforming executive awareness to redesigning organizational structures. This article distills the essence of these discussions.

Executive Awareness Is Key

The most critical first step for domestic companies pursuing AI transformation is for their leadership to achieve an upgraded understanding of AI. This requires encouraging iterative optimization of organizational structures, followed by execution and gradual integration.

Many companies fail to thoroughly address the question, “Why are we adopting AI?” before implementation. A common error is entrusting the process entirely to the technical department or attempting to replicate another company’s success stories without tailoring them. Executives must grasp the essence and limitations of AI, clarify its intersection with their business models, and establish a clear direction for the entire organization.

Misjudging Implementation Scenarios Leads to

Underutilization

A key point raised during the discussion was the reality that many companies have invested millions in developing digital and AI systems that ultimately go unused on the ground. For example, sales representatives often have to resort to making phone calls to check production statuses, clearly demonstrating the failure of these system investments.

The root cause often lies in selecting inappropriate implementation scenarios. AI operates based on generalized semantic processing and probabilistic outputs. Since it inherently encompasses “hallucinations,” deploying it directly in highly precise and quantitative scenarios often fails to yield reliable results.

The experts proposed a two-step approach to address this issue. First, establish a foundational digital infrastructure, such as standardizing paper data formats. Second, implement large language models that quickly generate skill components, breaking down data silos between departments. For instance, developing a system where AI retrieves information directly from a database to answer sales inquiries could immediately enhance operational efficiency.

Transforming Paper-Based Processes into

Living Systems with Ontology

Traditional consulting methods often require two person-months to organize business processes, delivering the results in the form of documents or slides. These deliverables frequently fail to connect with actual production processes and are often left unused.

The latest approach introduced in the discussion involves using AI to build a business ontology—a model that abstracts real-world business rules, contracts, rights and obligations, and production components into a set of generalized semantic rules. This standardized “workbench” can reduce delivery time by half.

The organized data is no longer a static, paper-based process. Instead, it becomes a “living system” that can connect to Markdown or schema files and directly generate corresponding skill components for integration into production. Service providers can simultaneously reduce costs and secure profits, delivering tangible results to management.

The Need to Transition from Hierarchical to

Self-Organizing Structures

The most debated topic during the discussion was related to organizational and talent challenges. The hierarchical system that has dominated corporate structures for the past 200 years is based on breaking down goals from the top and fitting them into fixed processes. However, in the AI era, which demands high flexibility, this system poses a threat to a company’s survival.

Breaking down hierarchies and organizing human resources into capability-based units, with teams forming cross-functional collaborative matrices, has become necessary. Leadership should focus not on enforcing rigid systems but on introducing agile organizational structures and co-creation mechanisms. Departments should be encouraged to identify points where AI can be integrated into cross-departmental projects aimed at “cost reduction, efficiency improvement, and quality enhancement.”

The future of companies will hinge on whether technical experts can quickly learn the business or business experts can swiftly adapt to technology. The rapid fusion of these two domains will be the foundation of competitiveness.

Editorial Opinion

The insights gleaned from this discussion are directly applicable to Japanese companies. In the short term, prioritizing AI literacy among executives is crucial. In many Japanese companies, AI adoption is often driven by individual departments, while top-level commitment remains lacking. The assertion that “upgrading executive awareness is the first step” is particularly poignant when considering the realities of Japanese businesses.

In the long term, transitioning from hierarchical structures to self-organizing models is inevitable. Traditional seniority-based systems and siloed organizational structures in Japan fundamentally conflict with the agility that AI brings. Over the next one to three years, we expect an acceleration in flattening organizational hierarchies and transitioning to cross-departmental project-based systems. However, Japan’s unique culture of lifetime employment and slow decision-making processes may pose significant barriers.

As a publication, we emphasize the necessity for Japanese companies to pursue both “technology adoption” and “organizational transformation” simultaneously when implementing AI. Without organizations adapting to these shifts, technological implementation alone will yield only half the potential benefits. Compared to companies in the U.S. and China, Japanese firms are slower in enacting organizational changes. Bridging this gap will be a critical challenge.

References

• Huxiu Original Article — Published on June 27, 2026