5 Myths About AI Governance and What to Do Instead
August 19, 2025
As artificial intelligence (AI) continues to transform industries at a breakneck pace, the need for effective AI governance has become impossible to ignore. Yet many businesses, especially those just beginning to adopt AI, are clouded by misconceptions that can delay important risk management and implementation efforts.
Wherever you are along your AI journey, understanding what AI governance truly involves is essential for long-term success. Let’s look at five of the most common myths, and what you should do instead.
Myth: “AI Governance is Only for Tech Companies”
The Reality
AI is no longer a tool exclusively for big tech firms. Today, banks use AI for credit scoring, hospitals when diagnosing and treating patients, retailers for customer insights, and logistics companies for supply chain optimization. As AI tools multiply, so too do the risks.
The Alternative
Recognize that AI governance applies across industries. No matter your sector, if you use AI of any kind, whether developed in-house or sourced from a third-party vendor, you should have controls in place to manage its risks. Start by identifying where AI systems operate in within your business and define clear lines of accountability. Leveraging industry-agnostic frameworks that focus on AI management systems (AIMS) can help you scale your governance in a structured and consistent way.
Myth: “AI Governance = AI Ethics”
Ethics and governance are often used interchangeably, but they’re not the same.
The Reality
AI ethics typically deals with principles (like fairness or transparency), whereas AI governance involves operationalizing those principles through policies, procedures, risk controls, audits, and stakeholder accountability.
The Alternative
Treat AI governance as a holistic management system that brings ethical principles to life through action. This includes setting governance policies, defining roles and responsibilities, and embedding AI oversight into your existing risk and compliance structures. While various frameworks can support this effort, it’s the commitment to operationalizing ethics that defines effective governance.
Myth: “We Don't Need Governance. Our AI Isn't High Risk”
You might think your AI tools are simple or low impact. But regulators and stakeholders may not agree.
The Reality
Even low-risk AI can result in privacy violations, bias, or reputational damage if left unchecked. What seems “low risk” today could quickly escalate under real-world conditions or scrutiny.
The Alternative
Take a risk-based approach to governance. Begin with an internal risk assessment to evaluate possible harms, even in seemingly low-impact tools. Based on the outcomes, implement proportionate safeguards such as regular audits, explainability thresholds, or human-in-the-loop processes. A structured approach allows you to manage risk pragmatically without over-engineering controls.
Myth: “Regulations Will Tell Us What to do When It's Time”
Many businesses are waiting for laws to be passed before acting. That’s a mistake.
The Reality
By the time regulations like the EU AI Act is enforced, organizations will need to show proactive alignment, not just reactive compliance.
The Alternative
Start preparing now by aligning your governance efforts with emerging best practices and voluntary standards like the ISO/IEC 42001 or the NIST AI Risk Management Framework. Establish internal policies that reflect your values and potential future obligations. Participating in external benchmarking or working with third-party assessors can also help your organization stay ahead of formal regulation while building trust with stakeholders.
Myth: “We Can Build Our Own Governance Framework”
DIY governance is tempting, especially for internal innovation teams. But it can run into issues when scaling.
The Reality
While custom policies might work temporarily, they can often lack the structure, credibility, and auditability of a recognized standard. More importantly, they may not hold up under regulatory or third-party scrutiny.
The Alternative
Rather than trying to reinvent the wheel, look to established governance models that have been designed for scalability and interoperability. These provide a strong foundation and reduce the trial-and-error period many internal teams face. Supplement this with internal training, clear documentation, and regular reviews to ensure your framework evolves alongside the AI technologies you use. Building internal capacity, particularly by certifying key team members in recognized standards, can reduce long-term reliance on external consultants and streamline the adoption of future regulatory or technical requirements.
Conclusion: Breaking Through the Noise
As AI becomes more deeply embedded in modern business, so too does the responsibility to govern it effectively. Falling for common myths, like the ones above, can leave your business vulnerable to both operational, and legal setbacks, as well as potentially harming your reputation.
Effective AI governance requires more than good intentions. It calls for structure, accountability, and consistency across the entire AI lifecycle. That’s where internationally recognized standards like ISO/IEC 42001 can make a meaningful difference by offering a practical framework for managing AI risk across systems, teams, and use cases.
Take the Next Step Toward Responsible AI Governance
If your organization is exploring how to manage AI more effectively ISO/IEC 42001 offers a clear, globally recognized path forward.
Equally important is making sure your internal teams have the right knowledge to implement and maintain governance systems with confidence. Investing in certification for key team members strengthens your in-house capability and lays the foundation for long-term efficiency, trust, and compliance.
Whether you're looking to lead implementation or ensure robust auditability, our ISO/IEC 42001 certification courses
are designed to support your journey.
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From "I need to be an AI expert" to "AI governance will slow us down," let's break down 5 myths that make AI governance seem more complex than it really is
You already know how to manage risk. Now it's time to manage intelligence. If you’ve worked in Governance, Risk, and Compliance (GRC) for any length of time, you’ve seen waves of transformation: cloud computing, automation, privacy reform. Each one reshaped the way organizations think about control and accountability. Now, artificial intelligence is the next wave. It’s changing how businesses make decisions, assess risk, and build trust. Many professionals look at AI GRC and think it’s a brand-new specialty. In reality, it’s the next chapter of what GRC was always meant to be — a system that keeps technology aligned with ethics, law, and business purpose. And if you’ve been working in traditional GRC, you’re already well prepared. You just need to apply your existing strengths to a new kind of system: one that learns, evolves, and occasionally surprises you.
How do you prepare for compliance with regulations that are both complex and still evolving? ISO/IEC 42001, the first international management system standard for AI, gives businesses a way to govern, monitor, and document their AI systems.
