Harmonised AI standards to reduce fragmented global rules

How can international, market-driven AI standards reduce fragmented global AI governance for business?

As AI systems become integral to business operations worldwide, fragmented governance approaches create significant challenges for companies of all sizes.

When different jurisdictions develop their own AI policies, laws and regulations, businesses face:

1. Increased compliance costs arising from navigating complex regulatory landscapes
2. Market access barriers that limit where they can operate
3. Innovation constraints that slow cross-border collaboration.

These challenges are particularly acute for small- and medium-sized enterprises (SMEs) which lack the resources to manage complex, jurisdiction-specific requirements.

International, market-driven standards are consensus-based guidelines that define how technologies should perform, interact and remain safe. They provide practical guidance that works across multiple legal frameworks, essentially creating a common language for AI governance globally.

Potential overlaps, duplications and divergences in AI standards

Achieving internationally interoperable AI governance is significantly hindered by overlapping standardisation efforts, inconsistent terminology across different frameworks and limited awareness of existing AI standards.

These issues contribute to market fragmentation and a complex regulatory landscapes, with regional or national bodies – sometimes even within the same country – issuing overlapping or even competing guidance. At the same time, the use of standards processes to advance specific policy agendas rather than technical excellence, creates standards that may not serve broader global or business needs.

Without better coordination, these standardisation efforts risk adding complexity instead of reducing it, increasing compliance costs (which are especially burdensome for SMEs), and impeding cross-border collaboration and innovation.

ICC recommendations: How can policymakers make AI standards work globally?

1. Promote strategic alignment in AI standards-development to reflect market needs and avoid duplication.
2. Ensure domestic and local expert participation in shaping market-driven standards.
3. Prioritise global, industry-driven standards over national or regional-only approaches.
4. Champion multistakeholder collaboration through transparent, inclusive processes.
5. Leverage existing standards in regulation to streamline compliance and build trust.
6. Use standards in public procurement to support adoption and open markets to SMEs.
7. Support company participation with funding, incentives, and training.
8. Enhance awareness and education to build capacity for implementing AI standards.