Why Market-Specific Governance and Ethics Boards are Key to Managing AI Globally

For companies deploying AI across global markets, governance is proving harder to standardize than many expected. Frameworks that work in one country often fail in another, leaving organizations exposed to oversight gaps. In practice, that can show up as content moderation failures in linguistically diverse regions, misalignment with local regulatory expectations, or growing friction with national authorities.

According to Shreyas Kanoongo, a public policy professional specializing in strategic policy development and regulatory affairs, these challenges stem from a fundamental misunderstanding of how AI governance works at scale. Previously a public policy and regulatory affairs leader at Cairn Oil and Gas, she emphasizes that there is no one-size-fits-all approach, as every country has its own national priorities and socioeconomic realities.

"There is no one-size-fits-all in AI governance. Every country has its own national priorities and socioeconomic realities," Kanoongo says. She advises enterprises to adopt market-specific governance paired with ethics boards to address cultural, societal, and regulatory realities while helping companies move beyond a single checklist. While many AI companies are headquartered in the U.S., their technologies are deployed across very different national and cultural contexts, which often creates a mismatch between how these systems are designed and what local governments and populations expect from them.

• Different strokes: The EU emphasizes proactive risk management over reactive penalties, aiming to protect rights, prevent harm, and set a global benchmark through the Brussels Effect. "They take precautions instead of focusing on penalties after compliance issues have arisen," Kanoongo says. The U.S., on the other hand, prioritizes economic and geopolitical goals, emphasizing technological leadership and innovation over strict oversight. "Yes, they want to protect their people from any possible risk from AI, but at the same time, the focus is more on innovation and supporting these companies."

• Competing philosophies: India’s AI governance is shaped by domestic priorities, using technology to solve what Kanoongo calls a "billion-person problem." The focus is on scaling services, improving welfare systems, supporting farmers, and bridging thousands of languages, enabling AI to reach remote areas where human outreach is not feasible. "AI is largely being seen as a way to reach large numbers of people," she notes. Singapore, however, takes a hybrid approach to AI governance, combining a pro-innovation stance with deep collaboration. "The model is built on bringing the government and private sector together for extensive consensus-building," Kanoongo says, aiming to involve every sector of society.

When companies encounter this wall of competing priorities and risks, many default to what they can control: legal compliance. Kanoongo’s recommendation is to stop thinking universally and start acting locally, decentralizing governance into market-specific functions to move beyond the limitations of a single checklist.

• A board for each market: Kanoongo argues that legal compliance alone isn’t enough. “Instead of focusing on just the laws, they need to understand the realities of the nation, its culture, and its society,” she says. To address this, she recommends creating market-specific ethics boards that can advise on local practices, helping companies move beyond a one-size-fits-all checklist and manage competing priorities more effectively.

While the rules of AI governance vary across countries, the goal is consistent: protecting people from algorithmic bias and abuse. Global companies face a steep challenge, navigating distinct national priorities, regulatory bodies, and cultural realities in every market. As Kanoongo concludes, "The difference is pretty huge. A global company must understand the realities of each nation, its culture, and its society, not just follow a single legal framework."