An article by Jadd Elliot Dib, Founder and CEO, PangaeaX

Over the past year, governments around the world have begun rolling out comprehensive AI regulations, signalling a clear shift from experimentation to oversight. AI governance can no longer be treated as an afterthought. The window to embed governance, risk assessment, and transparency into AI systems is closing fast. Companies that delay risk costly retrofits, regulatory penalties, and reputational damage, while those that act early stand to build trust and position themselves as responsible innovators.

Major regulatory developments

In the European Union, the AI Act began phasing in key provisions throughout 2025. These include prohibitions on biometric surveillance for law enforcement, transparency requirements for limited-risk systems and general-purpose AI, and a full set of obligations for high-risk AI systems expected to come into force in 2026.

In the United States, a December 2025 executive order introduced a national AI framework aimed at setting unified standards and reducing fragmentation across state-level laws.

Closer to home, the UAE introduced its Charter for the Development and Use of Artificial Intelligence in mid-2024, outlining principles around safety, privacy, bias mitigation, and human oversight. This framework is reinforced by federal data protection laws and supported by dedicated governance bodies, including the Artificial Intelligence and Advanced Technology Council. Together, these measures reflect the UAE’s intent to balance ethical oversight with innovation-friendly regulation.

Governance is the foundation

AI governance must extend beyond a compliance checklist. As regulations take effect, companies need clear frameworks that define decision-making authority, establish risk assessment processes, and ensure accountability across the AI lifecycle. This starts with a formal governance policy covering fairness, transparency, and security, supported by documented processes for data sourcing, model validation, and bias mitigation.

Effective governance also requires cross-functional oversight. Committees that bring together legal, technical, and business leaders help organisations balance innovation with regulatory obligations, track evolving requirements, and uphold ethical standards. When embedded early, governance reduces future compliance costs and transforms AI from a regulatory risk into a strategic asset.

Transparency and explainability are non-negotiable

Transparency in AI means shedding light on how systems operate and what data they rely on. Closely related is explainability: the ability to understand and articulate why an AI model produces a particular outcome, including the underlying logic, inputs, and potential sources of bias.

According to research from Stanford University, limited explainability remains a major barrier to scaling AI, particularly in regulated sectors such as finance and healthcare. Meanwhile, Microsoft’s 2025 Responsible AI Transparency Report found that more than 75 percent of organisations using responsible AI tools for risk management reported improvements in data privacy, customer trust, brand reputation, and confidence in decision-making.

As regulatory scrutiny increases, transparency and explainability are becoming baseline requirements rather than optional best practices.

Upskill the workforce

AI regulation does not stop with compliance teams. It reshapes skill requirements across the organisation. Employees need a working understanding of AI ethics, regulatory frameworks, and responsible deployment practices, alongside the technical skills required to use AI effectively.

Marketing teams must understand how AI-driven personalisation aligns with privacy laws. HR teams need to ensure recruitment algorithms do not introduce bias. Product managers must be able to document AI decisions and processes for regulators. Embedding AI literacy across functions not only supports compliance but also enables organisations to innovate confidently within regulatory boundaries.

Act proactively

As jurisdictions move from guidance to enforcement, companies must invest early in accountability frameworks, talent development, and audit trails. Guardrails should be built into AI systems at the design stage, not added after deployment.

Global regulations are increasingly mandating transparency, explanation, and human oversight. Organisations that embed these principles proactively will not only reduce regulatory risk but also differentiate themselves as trustworthy, disciplined builders in an increasingly competitive AI landscape.