The “Rule Absence” Risk That Becomes Harder to See as AI Adoption Grows
According to a report by ITmedia, approximately 40% of small and medium-sized enterprises (SMEs) have not established internal rules regarding AI usage. This figure clearly highlights the reality that many companies are incorporating AI tools into their daily operations while postponing risk management.
As AI adoption accelerates, security measures and governance structures often lag behind. In particular, SMEs tend to have managers who view AI as a “convenient tool,” overlooking the risks lurking in the shadows.
However, this state of “rule absence” is not merely a management system issue. It must be recognized as a management challenge directly linked to business continuity and corporate value.
Why Are AI Rules Not Established in SMEs?
The Structural Blind Spot Created by Prioritizing “Convenience”
Many AI tools can be started for free or at a low cost. Generative AI like ChatGPT and Copilot dramatically streamline daily tasks such as email creation, document preparation, and data analysis. Managers and on-site staff often tend to adopt these tools with a “let’s just try it out” mentality.
This “ease of use” is the biggest factor in postponing rule development. Unlike traditional IT system implementations, AI has low adoption costs and can be started by specific departments alone, lowering the priority of company-wide rule-making.
“Invisible Risks” Blunt Decision-Making
The risks of AI differ in nature from conventional information leaks or system failures. For example, entering confidential company information into AI could lead to that data being used for training and potentially appearing in responses to other companies. Additionally, there are cases where AI-generated incorrect information is sent directly to customers.
These risks are difficult to see immediately, which often diminishes managers’ sense of urgency. However, once a problem occurs, it can escalate into a loss of corporate trust and legal liability.
Specific Management Risks Caused by the Absence of AI Rules
Information Leaks and Intellectual Property Infringement
When employees use AI for work, they may input customer information, partner data, or internal strategic documents. If the AI service uses this data for learning, there is a risk of information leaking to competitors.
Furthermore, it is possible that AI-generated text or images infringe on third-party copyrights. These risks can develop into legal liability issues for the company, leading to significant damages and loss of trust.
The “Black Box” Problem of AI
The reasoning behind AI decisions is often difficult for humans to understand, and using those results directly in operations poses a significant risk. For example, if AI makes a decision in hiring that unfairly disadvantages applicants with certain attributes, it may be impossible to explain the reason.
This “accountability” issue can severely damage a company’s social credibility. Caution is especially needed when using AI in areas that directly impact people’s lives, such as customer service and performance evaluations.
Three Actions Managers Should Take Immediately
First: Define “Basic Principles for AI Use”
Start by establishing a company-wide “Basic Policy on AI Use.” Specifically, clearly define the following items:
– Prohibition of inputting confidential information (customer data, partner information, internal strategy documents, etc.)
– Obligation to verify AI-generated content (thorough fact-checking and editing)
– Specification of permitted AI services (allowlist approach)
– Guidelines for handling personal information
It is crucial to write these rules in plain language that all employees can understand, avoiding complex technical jargon.
Second: Implement “Phased Rule Development”
Trying to create perfect rules all at once can lead to resistance and confusion in the field. Start by prioritizing measures in the highest-risk areas.
For example, an effective approach is to initially rule on just two points: “prohibition of inputting customer information” and “obligation to verify AI-generated content,” then gradually expand as you monitor the operational situation. Also, regularly review the rules to keep pace with AI technology evolution and emerging risks.
Third: Build a System of “Education and Monitoring”
Even if rules are established, they are meaningless without a system to enforce them. Conduct regular in-house training or e-learning to create an environment where all employees correctly understand AI risks.
Also, consider introducing tools to monitor AI service usage. Specifically, utilize cloud service management tools or dashboards to visualize which AI services employees are using and how.
Establishing AI Rules is Proof of “Management Decision-Making”
The absence of AI rules is not simply a lag in management systems; it is the result of management “failing to define” IT risks. This is a prime example of the “price of management running away from IT,” as stated in our editorial policy.
Managers have a responsibility not to blindly welcome AI as a “convenient tool,” but to confront its risks and build appropriate governance. Rule-making is not about “restricting” AI, but about making “management decisions” to use it safely and effectively.
As a concrete first step, take even 30 minutes to audit your company’s AI usage. Which departments are using which AI tools for what purposes? Rule development begins with understanding this reality.
AI adoption is now an unavoidable management challenge. However, leaving its risks unaddressed will not only waste valuable DX investments but could also threaten the very survival of the company. Now is the time for managers to seriously commit to establishing AI rules.
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