As generative AI rapidly expands into business operations, aren’t many executives worried about the risks posed by employee usage? The rise of shadow AI, the risk of information leaks, and potential copyright infringement—if left unaddressed, these issues could significantly damage corporate value.
In response, a free “AI Literacy & Risk Diagnostic Tool” for businesses has been launched. This article explores the significance of this tool, while detailing the varying levels of AI adoption across companies and the specific actions executives should take immediately.
Why AI Risk Diagnosis Is Needed Now
According to a survey by the Nikkei, only 9% of Japanese companies are seeing results from generative AI that exceed expectations. In contrast, this figure exceeds 30% in the US and UK. I believe this gap isn’t simply a difference in technological capability, but a difference in “AI governance.”
Many executives worry whether their employees are secretly inputting customer information into ChatGPT or publishing AI-generated content externally without review. A survey by Lagxus also reveals a significant gap in AI adoption between large and small-to-medium enterprises, as well as between urban and regional areas.
At the root of this gap is a lack of criteria for judging “what constitutes a risk and how to address it.” The risk diagnostic tool effectively provides exactly that framework.
Three Risk Areas Visualized by the Diagnostic Tool
This newly offered free diagnostic tool visualizes the risks of employee generative AI use. Specifically, it covers the following three areas.
Information Leak Risk
The most critical risk is employees unconsciously inputting confidential information into AI. This is especially common in sales and customer support, where customer names and contract terms are often entered directly. The diagnosis analyzes the reality of these risks based on employee usage patterns.
Compliance Violation Risk
In some industries, the use of generative AI itself is subject to regulation. This is particularly serious in the financial and healthcare sectors. The diagnostic tool evaluates risks according to your company’s industry and business characteristics.
Quality and Brand Damage Risk
This is the risk of using incorrect information or biased expressions generated by AI directly in customer-facing materials or social media posts. The diagnosis also evaluates the state of your internal review systems and usage guidelines.
Concrete Steps to Bridge the AI Adoption Gap
The Lagxus survey highlights the gap between “large companies and SMEs” and “urban and regional areas.” Bridging this gap requires not just tool implementation, but improving literacy across the entire organization.
From my experience supporting AI implementation for over 38 clients, the key to success lies in “starting small and providing continuous feedback.”
Specifically, I recommend the following three steps.
Step 1: Visualize the Current Situation
Use this diagnostic tool to objectively understand your company’s AI usage and risks. Since it’s offered for free, the barrier to entry is nearly zero.
Step 2: Establish Simple Guidelines
Don’t try to create perfect rules from the start. First, enforce just two points: “Don’t input confidential information” and “Always have a human review the output.” I have a client who halved their shadow AI risk with just these two rules.
Step 3: Conduct Regular Literacy Training
Annual training is insufficient. I recommend holding 15-minute mini-training sessions quarterly to share the latest risk examples. Using the diagnostic tool’s results as training material makes the content more practical.
Considering Costs and Implementation Barriers
Some executives might think, “Another new tool to implement—what about the cost?” However, the biggest advantage of this diagnostic tool is that it’s offered for free.
On the other hand, implementing actual countermeasures based on the diagnosis results will incur costs such as:
・Developing internal guidelines: A few hours to a few days for internal coordination
・Literacy training: Nearly zero if conducted in-house; $300–$700 per session for an external instructor
・AI usage monitoring tools: A few tens to a few hundred dollars per month (over $700 for comprehensive tools)
However, the risk of a single information leak is said to be tens of thousands of dollars. Considered as a preventive investment, it’s well worth the cost.
My Personal Examples of AI Risk Management
I personally use three AIs—Claude, ChatGPT, and Grok—in my daily work. A key rule I strictly follow is “anonymizing input information.”
For example, when asking AI to review a contract, I replace company names and amounts with “Company A” and “X million yen” before inputting. This small extra step minimizes potential damage even if information were to leak.
I also strictly enforce the rule of never using AI output directly without final human review. Especially for social media posts and customer-facing documents, I incorporate a process to correct any factual errors or inappropriate expressions generated by the AI.
These rules can be implemented without special tools or significant investment. Starting with “visualization” allows you to begin risk management smoothly.
Summary: Make Risk Diagnosis Your First Step in AI Adoption
The more generative AI is adopted, the more important risk management becomes. However, abandoning AI adoption out of fear of risk is synonymous with losing competitiveness.
This diagnostic tool is an extremely effective first step. Now that it’s offered for free, it’s the perfect opportunity to objectively evaluate your company’s AI usage and build appropriate governance.
“Invisible risks” are the scariest. Start by making them visible. By establishing AI usage rules and systems tailored to your company based on the diagnosis results, you can achieve safe and effective AI adoption.
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