How Should SMEs Approach AI Governance?
On June 20, 2025, a seminar titled “AI Governance & Data Ethics for Beginners” will be held, led by a world-renowned designer. This seminar offers foundational governance skills as essential knowledge for using AI safely.
While large corporations are building governance structures alongside AI adoption, many SME owners think, “It’s still a future concern” or “It doesn’t apply to us.” However, with the spread of generative AI, the risk of employees using AI in their work is a reality for businesses of all sizes.
This article redefines AI governance not as a “defensive measure” but as a “design technology for business growth,” explaining concrete actions SMEs can take right now.
The Fundamental Question AI Governance Poses to SMEs
When hearing “AI governance,” many business owners think of legal measures like “preventing data leaks” or “establishing ethical usage rules.” But this is only part of governance.
Essentially, governance is a “high-level management design concept that arranges and integrates rules—legal, accounting, tax, etc.—from an overall optimization perspective to achieve business goals.”
The same applies to AI adoption. The first question should be, “What business outcomes do we want to achieve using AI?” Then, legal compliance and ethical use should be positioned as means to achieve that goal.
A common failure pattern among SMEs is making “using AI” itself the goal, ending up merely filling compliance checklists.
Shifting from “Prohibition” to “Design”
A typical response is “banning employees from using ChatGPT.” While this effectively reduces information leak risk to zero, a “0/100 thinking” that sees risk as either zero or full can mean voluntarily abandoning opportunities for business growth.
The essence of AI governance is to view risk as a continuum from 1 to 99 and design an acceptable risk range aligned with business goals.
For example, instead of completely banning customer data input into AI, you can set operational rules like “don’t input internal confidential information” or “always have a human review outputs,” allowing you to control risk while benefiting from AI.
Three Perspectives to Learn from the June 20 Seminar
In this seminar, a world-renowned designer will lecture on AI governance and data ethics. The approach is unique, incorporating design thinking rather than just technical measures.
SME owners should focus on these three key takeaways from the seminar:
1. Visualize AI’s “Decision-Making Process”
AI is a technology prone to black-boxing. You need to be able to explain internally what data it learns from and on what criteria it produces outputs.
Specifically, before introducing an AI tool, document “what data will be input,” “how output accuracy will be evaluated,” and “who takes responsibility for incorrect outputs.” This process itself implements governance.
2. Make Data Ethics Part of “Management Decisions”
Data ethics isn’t just a legal or compliance department issue. It’s a management issue that business owners should proactively engage with.
For example, when using AI for customer analysis, ethical decisions like “how much to respect customer privacy” can trade off against short-term sales growth. A system is needed where management can make such judgments based on clear criteria.
3. Design How to Use Experts
Building AI governance requires expertise in law, IT, data science, and more. But it’s unrealistic for SMEs to have all these experts in-house.
The key is not to ask experts for a “yes or no” but to ask for “conditions to achieve business goals.” For instance, instead of asking a lawyer, “Can we use this AI tool?” ask, “To achieve this business goal, what risks exist with this AI tool, and how can we mitigate them?”
Three Actions You Can Start Today
When tackling AI governance, SMEs don’t need a perfect system from the start. We recommend starting with these three:
Action 1: Create an AI “Whitelist”
Instead of deciding “what to ban,” decide “what to allow.” For example, list permitted AI tools based on criteria like:
・Tools confirmed not to require inputting confidential information
・Tools whose output accuracy has been verified
・Tools with clear terms of service and appropriate clauses on secondary data use
Share this whitelist internally and require approval for using any tool not on it.
Action 2: Establish an AI Output “Verification Process”
Create a system that doesn’t take AI results at face value. Especially for high-importance tasks, always include human review:
・Drafting contracts
・Customer email content
・Reference materials for management decisions
When reviewing, get into the habit of recording “why this output was deemed appropriate,” which helps with later verification and improvement.
Action 3: Conduct Regular “Governance Reviews”
AI technology evolves quickly, so avoid fixing rules once and for all. A system for periodic review is crucial. Review quarterly from these perspectives:
・Evaluation of newly introduced AI tools
・Checking rule compliance
・Reassessing risks due to changes in the business environment
By incorporating this review into management meeting agendas, you prevent AI governance from becoming a mere formality.
Summary: AI Governance as a “Growth Blueprint”
AI governance is never something you “have to do.” It’s a “blueprint” for maximizing AI’s power while controlling risk, all for your company’s business growth.
The June 20 seminar is an ideal first step to draw that blueprint. Understanding the essence of AI governance as a business owner and tailoring it to your company will determine your competitiveness in the coming era.
Why not start by taking stock of your company’s current AI usage?
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