The Significance of Large Corporations Supporting AI Education for SMEs
The news that Korea Western Power is providing generative AI job training support for its partner SMEs is an extremely strategic move that goes beyond mere corporate social responsibility. It demonstrates a new form of “supply chain strengthening” in the AI era.
Traditionally, transactions between large corporations and SMEs focused primarily on cost and delivery times. However, with the advent of generative AI, the quality and speed of work, and even the way data is handled, are changing dramatically. Even if a large corporation introduces and utilizes AI internally, if its business partners’ processes remain outdated, there are limits to optimizing the entire supply chain. There’s even a risk that data incompatibility and communication gaps could create new bottlenecks.
The case of Korea Western Power is an attempt to preemptively solve this challenge through “education support.” Upskilling the productivity and digital literacy of business partners is an investment that accelerates one’s own operational efficiency. This can be seen as a symbolic case showing that the strategic use of AI in management is beginning to expand beyond a company’s own walls to encompass the entire ecosystem.
The “Next” Stage Indicated by Japan’s Subsidy Revisions
Meanwhile, in Japan, applications have opened for the SME Agency’s “Digitalization and AI Introduction Subsidy,” with changes from the previous system drawing attention. While the existence of this subsidy was known before, a close reading of the revision’s direction reveals that the level of “maturity” in AI utilization demanded by the government has risen a notch.
The focus has shifted from simply “buying AI tools” to more strongly questioning the planning behind “how to transform business processes and link it to sustainable competitiveness.” In application documents, there is a growing tendency to require designs that pair execution with verification, including not just the traditional “introduction purpose” but also “concrete measures for business flow change” and “methods for verifying effectiveness.”
This is a natural evolution based on the initial-stage challenge many companies experienced: “We bought ChatGPT accounts, but usage hasn’t spread.” The subsidy is expected to function not merely as initial investment but as a “catalyst” for transformation.
The Intersection of Education and Subsidy Policies
Korea’s education support and Japan’s subsidy revision. While they may seem like separate movements, the core they both point to is the same. It is the shift in focus from “tool introduction” to “transformation of human capability.”
Subsidies, as funding, are effective for introducing hardware or software. However, it is ultimately “people” who utilize those tools to redesign processes and create new value. Korea Western Power’s support can be seen as a more proactive approach that externally intervenes in this “human capability” aspect.
What Japanese managers should learn from this trend is the perspective of budgeting for “tool costs” and “education/transformation costs” as a set when considering AI introduction for their own company. For example, the importance of investing several hundred thousand yen in internal workshops or departmental practical support to maximize the effectiveness of introducing an AI tool costing 20,000 yen per month.
How to Overcome the Risk of “AI Dependence and Isolation”
Here, a third news item, “‘AI Dependence’ Creating Employee Isolation,” issues an important warning. It points out the potential risk that the proliferation of generative AI reduces communication with colleagues, and excessive individual reliance on AI could hinder the sharing of organizational wisdom and know-how.
This is precisely the typical side effect that occurs when only “tool introduction” is pursued. A paradoxical situation where individual productivity increases, but the strength of the organization as a whole becomes dispersed and personalized.
The key to overcoming this risk holds a clue in the Korean case. It is “the design of collective intelligence.” Instead of leaving AI to individuals, it’s necessary to design the process itself: how it should be used, and how the insights and outputs obtained are shared and verified within the organization.
At our company, we address this challenge by establishing “standard protocols for AI use” for each department. For example, when requesting a contract review from AI (Claude), we always share a “list of discussion points to consider” beforehand, and the output results are accumulated in the internal knowledge base (Notion) along with review logs. This systematically ensures both individual work efficiency and organizational learning.
Practical Steps: Building “Human Infrastructure” in Your Company
So, what should managers and CTOs start with concretely? Before large-scale education programs or major subsidy applications, here are three steps you can start tomorrow.
Step 1: Conduct Department-Specific “AI Potential Surveys”
Instead of a company-wide initiative, work with leaders from specific departments (e.g., sales, accounting, marketing) to break down daily tasks and list “concrete tasks” that could be replaced or enhanced by AI. Here, small units like “document creation,” “email drafting,” “data formatting,” and “idea generation” are effective.
Step 2: Designate Pilot Users and Provide an Experimental Environment
Nominate 1-2 “AI Champions” from each department. Provide them with paid accounts like ChatGPT Plus or Claude Pro and set a two-week experimental period for the tasks listed in Step 1. Evaluation criteria should include both “time-saving effect” and “changes in output quality.”
Step 3: “Visualize” Results and Design a Sharing Process
After the experimental period, create a simple forum for champions to share within the department or company (e.g., a 30-minute lunch meeting) how they used the tools and what results and challenges they encountered. The key is to share not just successes but also “this didn’t work well” failure knowledge. This sharing forum itself becomes the first line of defense against isolation caused by AI dependence.
How to Measure Return on Investment: The ROI of Education Support
Finally, let’s consider the most managerial question: “How should we measure the ROI of such human capital investment?” While the ROI of tool introduction is relatively easy to calculate (time saved × labor costs), the effectiveness of investment in education and organizational transformation tends to be ambiguous.
Here, we recommend setting two types of indicators: quantitative and qualitative.
Quantitative Indicators:
・Number of “business processes utilizing AI” (e.g., 10 processes per month)
・Number of times AI-generated outputs are shared internally / view counts (knowledge base access analytics)
・Number of AI-related questions posted internally and average time to resolution (indicating organizational learning activity)
Qualitative Indicators (measured via regular surveys or interviews):
・Reduction in the feeling of “being burdened with difficult tasks alone”
・Improvement in understanding of “how other departments work”
・Changes in willingness to take on new challenges
Whether Korea Western Power’s support succeeds depends on how such “indicators” within the partner companies change. Similarly, when making human capital investments in your own company, designing from the start how to measure the “change” in operations and organization, rather than just “training hours,” is what turns the investment into something meaningful.
Conclusion: Competitiveness in the AI Era is Determined by “Human Infrastructure”
The generative AI tools themselves are evolving rapidly and will eventually become commoditized. Within that landscape, the true differentiating factor will be the “human infrastructure” and “organizational processes” that effectively use the tools and amplify organizational wisdom.
Korea’s education support attempts to foster this in business partners from the outside, while Japan’s subsidies attempt to foster it within companies from the outside. And the risk of AI dependence and isolation is a symptom that manifests when that infrastructure is lacking.
As a manager, your next move might be to redirect a portion of this year’s IT budget from “tools” to “people and processes,” starting with small but certain experiments. An era is coming where the strength of both the supply chain and internal organization is determined by the weakest link of “human literacy.”
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