Editor’s note: The following is a guest post from Adnan Masood, chief AI architect at UST.
While some enterprises have long treated AI regulation as a forward-looking risk, shifts in legislation have pushed CIOs to rethink their approach.
In 2026, the landscape has moved from principles and proposals to enforceable timelines, targeted state laws and contractual expectations. The practical question facing leaders is no longer whether AI will be regulated; it is whether they can demonstrate lifecycle controls consistently, at scale and across vendors.
In late 2023, the European Union was still finalizing its AI act while the U.S. relied primarily on voluntary frameworks and sector enforcement under existing laws. Most organizations approached responsible AI as a policy and training program.
But now the EU AI Act is in force with staged dates that are reshaping procurement and product strategy. Simultaneously, U.S. states and cities have enacted enforceable rules in high-impact domains and regulators in healthcare and insurance have issued concrete expectations for lifecycle management.
In this new reality, leaders are expected to know where AI is deployed, classify risk, manage it across the lifecycle and produce evidence on demand.
The regulatory map
The EU AI Act is the most comprehensive law for AI to date, functioning as a global baseline for companies that sell into Europe or serve European residents.
Importantly, the act does not have a single go-live moment but instead follows a staged implementation. The act entered into force in 2024, while prohibited practices and AI literacy obligations began on Feb. 2, 2025. The obligations will continue to ramp up through 2027.
For CIOs, the effect is operational. Scope is determined by where systems are placed on the market, put into service or used — not by headquarter locations. CIOs with global responsibility need to be asking vendors to demonstrate risk classification and lifecycle controls as part of routine due diligence.
The U.S. remains a fragmented environment with no comprehensive federal AI law. Instead, enterprises face a combination of voluntary standards and frameworks that define reasonable care, targeted federal statutes addressing discrete harms and a growing set of state and local laws with operational requirements.
Additionally, international instruments are reinforcing a lifecycle governance posture.
The Council of Europe’s Framework Convention on AI puts forward obligations through a rights-based lens. Separately, the G7 Hiroshima Process issued voluntary guiding principles and a code of conduct for organizations developing advanced AI systems, emphasizing risk identification, evaluation and mitigation across the AI lifecycle.
Together, these instruments are pushing large enterprises toward common language for risk management, transparency and accountability — even when domestic law differs.
When regulation is uneven, CIOs should turn to these frameworks, which include the widely adopted NIST Artificial Intelligence Risk Management Framework.
Transparency becomes operational
Regulators have responded to the evolving capabilities of generative AI with transparency and response obligations. In the EU, transparency obligations apply to certain AI systems that interact with people and to certain AI-generated or manipulated content.
In the U.S., the Take It Down Act, enacted in May 2025, requires covered platforms to implement notice-and-removal mechanisms for nonconsensual intimate visual depictions.
For CIOs, the takeaway is that generative AI governance must include trust-and-safety mechanics: disclosure, content provenance where applicable, abuse reporting, response service level agreements, audit trails and re-upload resilience.
Enterprises deploying generative AI assistants, support agents, and content tooling will increasingly be held to transparency expectations by customers and regulators.
CIO priorities for 2026
For CIOs, the challenge is adhering to today’s regulations with governance approaches that will work if and when the next regulations land.
The answer is to build a single enterprise AI control system that can satisfy multiple regimes without creating multiple engineering realities.
This agenda is about scaling AI safely without slowing down innovation. The organizations that lead will treat compliance as a design constraint and governance as a product capability: it reduces customer friction, accelerates procurement, and prevents the costly operational pauses that follow avoidable incidents.
CIOs who succeed will do three things consistently: they will know where AI is deployed, they will manage risk across the lifecycle, and they will be able to demonstrate evidence without scrambling. That is what regulators are asking for, and increasingly, it is what customers and boards will demand before AI is allowed to scale.
