A writer who shaved their head in 2023 to protest weak AI governance now states that global agreements are struggling to match the speed of corporate AI adoption. The strategist, who has engaged in United Nations discussions on AI ethics, observes that while governments develop frameworks and adopt principles—such as the EU’s AI Act and UNESCO’s Ethics of Artificial Intelligence recommendations—companies are already deploying AI systems that operate without direct human control. This creates a disconnect: government-endorsed principles often fail to influence how businesses actually implement AI in practice.
Transparency is a prime example. Government commitments to explainable AI become meaningless when employees cannot track how automated decisions are made. Similarly, accountability promises lose force when no one is clearly responsible for the outcomes of AI-generated recommendations. The core difficulty, the strategist argues, lies in converting broad principles into daily operations—a responsibility that cannot rest solely with governments.
Beyond Transparency: Operational Challenges
Historically, companies treated AI as a technical issue, delegating oversight to IT departments. However, as AI agents become more advanced, interacting with software, accessing sensitive data, and executing workflows with varying levels of autonomy, the risks have grown. An AI handling customer service, for instance, could improve efficiency but also access unauthorized data, make financial mistakes, or violate company policies. When such issues arise, businesses cannot simply blame their technology providers; they remain fully accountable for deployment and supervision.
This shift raises critical questions: Who bears responsibility when an AI system causes harm? What data should an autonomous agent be allowed to access? When must human intervention occur? These are not technical challenges but leadership responsibilities. Company boards and executives must define acceptable AI uses, evaluate risks before deployment, and ensure employees understand both the strengths and limitations of the systems they depend on.
Rise of Autonomous Agentic AI
The proliferation of agentic AI, systems capable of independent action, has intensified the problem. Older AI models generated information or suggestions, but modern agents can execute decisions, communicate externally, and even override intended constraints. Without clear rules, access controls, and monitoring, the risks escalate. The more autonomous an AI system becomes, the more essential, and difficult to enforce, human oversight is.
An additional challenge lies in the global imbalance of AI governance. Current frameworks are primarily shaped by the U.S., EU, and China, yet the consequences of AI adoption will affect economies far beyond these regions. In Southeast Asia, for example, countries are adopting AI-driven growth strategies but lack the digital infrastructure, workforce training, or regulatory capacity to manage associated risks. A governance model designed for multinational corporations may not suit a small business in Thailand, Laos, or Vietnam.
Inclusion in AI governance is not merely an ethical issue; it is a practical requirement. Effective frameworks must account for differences in economic development, institutional capacity, language, and technology access. They must also ensure that women, small businesses, and marginalized communities have a meaningful role in shaping AI’s future. Without this, governance risks becoming a top-down mandate rather than a collaborative effort.
AI’s Threat to Social Cohesion
As AI integrates into healthcare, finance, and public institutions, its impact on social cohesion becomes more pronounced. Poorly governed AI systems can spread misinformation, deepen divisions, and reinforce discrimination, eroding public trust in technology. The strategist, who serves as a Global Women Peace Ambassador, emphasizes that AI governance is now linked to international stability. While the technology can support scientific collaboration and humanitarian efforts, unchecked use risks becoming a tool for division rather than cooperation.
The next critical step, according to the strategist, involves moving from broad declarations to measurable accountability. International agreements can set expectations, but businesses must translate those principles into actionable policies, employee training, and risk assessments. Executives should be able to explain which AI systems their organizations use, what decisions those systems influence, and how failures are addressed. Governments must provide practical guidance and enforceable standards tailored to different risk levels, while supporting smaller companies that lack extensive technical resources.
From Policies to Real-World Accountability
Educational institutions also have a role to play by preparing future leaders to treat AI governance as a fundamental management responsibility. However, the true measure of success will not be the number of agreements signed or conferences held. Instead, it will be whether AI operates safely, responsibly, and inclusively, delivering benefits to society without stifling innovation.
A defining difference from past technological revolutions is the speed of AI adoption. Unlike earlier disruptions, such as the internet or industrial automation, AI’s integration into decision-making happens in real time, with systems influencing hiring, lending, and public policy before governance frameworks can adapt. The strategist notes that in 2016, when the first major AI ethics guidelines emerged, most companies were still testing basic machine learning. Today, AI agents handle complex, high-stakes tasks with minimal oversight, widening the governance gap each year.
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