The Success of AI Adoption Hinges on “Employee Daily Life”

The Reality of Workplaces Where AI Use is Becoming “Normal”

A recent survey presents a reality that executives cannot ignore. A striking 60% of young employees report using generative AI “weekly” in their work. Furthermore, they state that a company’s level of AI utilization influences their choice of employer and can even be a reason for changing jobs. This is not just a technological trend; it’s a shift shaking the very foundations of human resource management.

In my own company, we have built a system of three AI agents using Claude, ChatGPT, and Grok, achieving a reduction of 1,550 hours of work annually. From this experience, I can say that AI’s true value is realized not as a “convenient tool,” but when it permeates the organization as a “partner in employees’ daily decision-making and creativity.” This news suggests that this permeation has already begun.

The Path to “Full Deployment” Seen in Kobe Steel’s CoE Support

Meanwhile, exemplary cases of systematic AI adoption in large corporations are also emerging. KOBELCO SYSTEMS is supporting Kobe Steel’s Center of Excellence (CoE) activities to promote generative AI use, contributing to PoC (Proof of Concept) advancement with an eye on full-scale development and the establishment of a utilization foundation.

The core of this news lies in the utilization of external resources through “support.” For the question many executives have—”How do we start with AI adoption?”—Kobe Steel established a specialized internal organization (CoE) while entrusting the “initial, most difficult phase” of its launch and foundational setup to external experts. This is an extremely pragmatic decision.

Reflecting on our own case, there is indeed an initial investment in AI utilization. However, by leveraging support services like those from KOBELCO SYSTEMS, that hurdle is significantly lowered. For a monthly cost in the range of several thousand dollars, you can “borrow” specialized knowledge and practical know-how that your company lacks. This is a far less risky option than immediately hiring expensive AI engineers.

How to Design the Bridge from PoC to Full Deployment

A particularly noteworthy aspect of the Kobe Steel case is the “advancement of PoC with an eye on full-scale development.” Many companies end with a PoC, falling into the “PoC graveyard” where valuable proof-of-concept results are never integrated into live business systems. To prevent this, technology selection and architecture design must consider the production environment from the very beginning.

Specifically, it is crucial to clarify the following three points in the early stages:

1. Identifying Integration Points: Determining into which existing systems (ERP, CRM, internal Wiki, etc.) and how the results output by the AI will flow.
2. Designing Data Flow: The method for safely bringing production data into the PoC environment, and the quality check and approval flow for data generated by the AI.
3. Estimating Scale: Estimating costs (especially API call fees) and response speeds for scenarios involving 10 users versus 1,000 users.

Solidifying these designs early with the help of external expert insight becomes the greatest significance of establishing a CoE.

Regional Expansion of Video-Generating AI Signals the Rise of “Industry-Specific” AI

Another interesting trend is the news that DLE and Shizuoka Broadcasting System (SBS) have partnered to begin exclusive sales of the video-generating AI “Shabekuri AI” within Shizuoka Prefecture. This indicates that as the proliferation of general-purpose AI tools (like ChatGPT) reaches a plateau, the next wave will be “industry-specific” and “task-specific” AI solutions dominating the market.

“Shabekuri AI” is a tool that automatically generates videos of people speaking from text scripts. For local SMEs and municipalities, creating promotional videos remains a high barrier. This tool specializes in that challenge, aiming to meet detailed needs like “natural mouth movements” and “speech patterns suited to the region,” which are difficult to achieve with general-purpose AI.

What executives should learn is that the time has come to elevate their company’s AI strategy from the dimension of “how to use ChatGPT” to “whether AI solutions specialized for our industry/tasks exist in the market, or if we can build them ourselves.” Industry-specific AI has the advantage of clearer implementation effects and less employee resistance compared to general-purpose AI.

The Limits and Potential of Asking AI about “AI Support Companies”

An experimental article in Nikkei Cross Trend reports the results of asking a generative AI itself to “recommend AIO (AI Operations) support companies.” The results apparently did not necessarily yield a recommendation list aligned with reality. This contains an important implication.

AI responds based on publicly available information but cannot grasp the “actual track record,” “compatibility with clients,” or “latest partnership status” of support companies—the living information. In other words, while AI is powerful for “information gathering and organization,” the “final decision-making,” especially complex judgments like partner selection, remains the responsibility of humans.

The practical application this experiment teaches is to use AI as a “preliminary research assistant.” For example, have the AI gather and organize information using keywords like “AIO support company comparison points” or “AI implementation support case studies for manufacturing,” then based on those results, executives or responsible persons can actually approach and interview several companies. This combination enables the most efficient and accurate selection.

Three Areas Where Executives Should Invest in Human Capital Now

Synthesizing these news items, it becomes clear that “human capital foundation building” is as urgent, if not more so, as building the technological foundation. There is still a significant gap between young employees’ desire for daily AI use and an organization’s systematic adoption as a company. To bridge this gap, executives should immediately allocate resources to the following three areas:

1. Transitioning AI Literacy Education to “Practical Application”

Basic education on “what is AI” is now a checkpoint that has been passed. What’s needed next are practical workshops tailored to company-specific tasks, such as “how to use Claude for checking our company’s contracts” or “how to streamline sales report creation with ChatGPT.” Internal organizations like Kobe Steel’s CoE should take on the role of continuously providing such practical skills to “AI champions” gathered from each department. Budget-wise, expecting an initial investment of around $6,500 for half-day workshops with external instructors per department is realistic.

2. Visualizing and Sharing Internal AI “Success Stories”

Some employees are likely already using AI on their own to improve their work. A system to collect and share these “grassroots success stories” internally is essential. Specifically, create an “AI Use Case Channel” on the internal Wiki or Slack, encouraging posts about which tool was used for which task and how much time was saved. Management can encourage active posting by offering small rewards (e.g., $30-$65 per outstanding case) for particularly excellent examples. This measure can dramatically boost the internal mood for AI use without a large budget.

3. Clarifying AI Usage Guidelines and “Safe Zones”

To enable employees to use AI without fear, rules (guidelines) on what is allowed and what is not are necessary. However, even more important is establishing “safe zones.” For example, providing a technically guaranteed environment where “all conversation logs from this internal chatbot are stored only on internal servers and will not leak externally.” Or declaring a cultural safe zone stating that “if any issues arise with drafts created using this tool, the employee who used it will not be held responsible.” This allows employees to engage in AI experimentation without fear of risk.

Conclusion: AI Adoption is a Talent Strategy, Not a Technology Strategy

The regional expansion of video-generating AI, the establishment of CoEs in large corporations, and the actual usage by young employees all point in the same direction. The full value of AI manifests only when it deeply integrates into the daily lives of each employee, changing the speed and quality of decision-making and creativity.

The role of executives is not to purchase high-performance AI models, but to cultivate the “soil” within the organization where employees can learn, experiment, and even fail and try again with AI. The first step in that soil cultivation is, as seen with Kobe Steel, to supplement the initial difficult foundational work with external expertise and concentrate internal resources on fostering a “culture of learning and sharing.”

The competition in AI utilization is no longer just for tech companies. It has become the core of the “talent strategy” for businesses of all industries and sizes.