The widespread adoption of artificial intelligence has led to significant benefits for organizations, but it also brings risks, with 95% of executives reporting negative consequences from their AI use. As businesses implement agentic AI, which operates autonomously, they face heightened compliance challenges and the need for new strategies to address these risks effectively.
A year after the introduction of the EU AI Act, 37% of UK marketers have significantly changed their approach to AI, emphasizing ethical practices and compliance. However, there are concerns that strict regulations may hinder creative experimentation and slow down innovation in the marketing sector.
As the EU AI Act is implemented, companies involved with high-risk AI systems must comply with new obligations aimed at protecting fundamental rights and safety. Key requirements include risk management, data governance, and transparency measures for users, with compliance deadlines set for August 2026 and 2027.
The European Commission’s final draft of the General-Purpose Artificial Intelligence (GPAI) Code of Practice has sparked discussions about its implications for AI regulation, revealing an epistemic gap in how “general-purpose AI” is defined. The EU AI Act’s rigid legal constructs may hinder adaptive governance in a rapidly evolving technological landscape, emphasizing the need for anticipatory frameworks that embrace uncertainty and flexibility.
China has implemented new AI labeling rules that require clear identification of AI-generated content across various media when distributed on Chinese platforms. Companies must adapt their content strategies to comply with these regulations to avoid severe penalties and reputational damage.
The EU AI Act categorizes AI systems into four risk levels: Unacceptable, High-risk, Limited, and Minimal. Genbounty offers a free Risk Classification Wizard to help teams quickly determine their system’s category and understand the corresponding obligations for compliance.
Last week, Colorado lawmakers decided to delay the implementation of the Colorado Artificial Intelligence Act (CAIA) until June 30, 2026, extending the timeline for businesses to prepare. The CAIA establishes liability for unintentional discrimination in AI systems, contrasting sharply with the federal approach that limits liability to intentional discrimination only.
The explosive growth of the crypto industry has brought about numerous regulatory challenges, making AI-native compliance systems essential for scalability and operational efficiency. These systems not only reduce false positives significantly but also enable real-time compliance checks across varying jurisdictions, thus future-proofing crypto portfolios against evolving regulations.
Private governance and regulatory sandboxes are essential for promoting democracy, efficiency, and innovation in AI regulation. This approach allows for agile and accountable experimentation that can outperform state-led initiatives while preserving individual liberty and fostering a vibrant market environment.
The article discusses the significant regulatory uncertainty surrounding global AI oversight and the importance of building governance frameworks to manage AI risks. Michael Berger from Munich Re emphasizes that organizations must actively define risk tolerance and diversify AI models to mitigate systemic biases and operational errors.