Artificial intelligence (AI) is rapidly transforming how businesses operate, compete, and deliver value. But as its use increases, so does the global push to regulate it. With the EU AI Act now in force and the U.S. issuing executive directives and federal frameworks, companies worldwide are entering a new phase of AI governance.
These legal mandates represent a deeper shift in how organizations must build, deploy, and maintain AI systems responsibly. For companies scaling AI in production, the next twelve to eighteen months will prove vital. Strategic alignment with emerging global standards could be the difference between business growth and regulatory setbacks, while early movers will reduce compliance risks, enhance transparency, and earn long-term trust from stakeholders, regulators, and consumers alike.
Setting the Benchmark for Risk-Based Regulation
The European Union has set the global tone with the introduction of its AI Act last August. While it will be begin phasing in through 2027, some provisions are already enforceable including stringent rules be applied to general-purpose AI models, including large language models (LLMs) deemed to carry “systemic risks.” The law requires risk assessments, adversarial testing, detailed documentation, and disclosure mechanisms such as watermarking for generative outputs. Penalties are severe, up to €35 million or 7% of global revenue for non-compliance.
In the U.S., while there is no single comprehensive AI law, the regulatory architecture is taking shape quickly. President Biden’s 2023 Executive Order tasked federal agencies with adopting the NIST AI Risk Management Framework (RMF), requiring increased transparency, rigorous testing, and bias audits. In early 2025, a second executive order introduced chief AI officers across all federal departments, signaling serious intent to build lasting national oversight. States like Colorado and New York are leading their own initiatives with algorithmic accountability bills and transparency mandates, highlighting a move toward sector-based national regulation.
Navigating Worldwide Frameworks
From a global lens, Canada’s Artificial Intelligence and Data Act (AIDA), China’s Generative AI Measures, and Japan’s AI guidelines, all are contributing to a growing global patchwork of AI governance. The Council of Europe’s AI Convention and the OECD’s AI Principles aim to harmonize these frameworks, encouraging cross-border cooperation. Singapore and the UK, while taking lighter-touch regulatory approaches, are rolling out sandboxes and guidelines that emphasize ethical implementation and innovation-friendly regulation
Compliance Challenges Facing Organizations
Navigating this landscape presents several challenges, particularly for multinational organizations. One major issue is jurisdictional complexity. The EU’s top-down, risk-based approach is quite different from the U.S.’s agency-led and sector-specific model. Multinational companies must reconcile these differences while avoiding fragmentation of their compliance efforts. Another critical challenge is operationalizing AI ethics. While many organizations have ethical principles written into policy, few have embedded them into engineering processes and this gap between theory and practice hinders responsible development.
Additionally, supply chain transparency is becoming crucial. AI systems are rarely built from scratch; they rely on open-source models, external datasets, and cloud APIs. Regulations now require companies to document these dependencies, a task that demands thorough documentation and third-party risk assessments.
To overcome these challenges, companies must take proactive steps. First, establishing a cross-functional AI governance council is essential and should include representatives from compliance, engineering, legal, security, and product teams to guide responsible deployment and ensure alignment with global standards. Next, adopting the NIST AI RMF can help organizations manage risk across the AI lifecycle, ranging from design and development to deployment and post-market monitoring. This should be complemented by the updated NIST Privacy Framework 1.1, which ensures privacy-preserving architectures and data minimization practices.
Furthermore, technical infrastructure is just as vital. Building auditable pipelines and maintaining an AI Bills of Materials (AI-BOMs) enables traceability and accountability. For generative AI systems, companies must implement explainability and watermarking features to comply with requirements for transparency.
A Financial Firm Turns Risk into Resilience
For example, consider the case of a global financial services firm. Facing increased scrutiny over its AI-based underwriting and fraud detection tools, the company adopted the NIST RMF to formalize its workflows, leveraged MLflow to track model development and maintain reproducibility, implemented AI-BOMs to manage third-party risks, conducted mandatory fairness audits, embedded watermarking into all generative tools, and instituted organization-wide training on responsible AI development. These steps not only improved audit readiness but also increased stakeholder trust and market agility.
Ultimately, regulatory compliance is no longer a cost center, it’s a competitive advantage. Companies that treat compliance as a foundation for innovation rather than a constraint will gain access to regulated markets, reduce legal exposure, and boost brand equity. Trust is becoming a premium differentiator, and AI systems that are transparent, fair, and explainable will outperform opaque alternatives.
Vital Net Steps
The path forward is clear: the future of AI is regulated, and preparation starts now. Organizations must do more than publish ethical statements or issue internal guidelines. Rather, it’s imperative that they embed responsible design principles, compliance frameworks, and legal strategies into every part of the AI lifecycle.
The regulatory clock is ticking, and those who move early will not only avoid penalties but define the standards for ethical, scalable, and trustworthy AI.
