Stating that “Artificial Intelligence has outpaced our ability to govern it” seems superficial and even unjust to the efforts different countries are putting into AI governance. As AI goes on to shape public services, economies, and social contracts, governments are experimenting with pilot programs or formulating complex regulatory ecosystems to ensure a citizen-first AI adoption approach. If we zoom out, we can start to see an emerging pattern or a maturity curve of AI governance. 

From countries still defining their first ethical guidelines to regions shaping global norms, every government sits somewhere along this curve. Understanding these stages will help track global policy progress. We will also discuss some common steps that businesses can prioritize to prepare for this variety of maturity levels and adoptions. 

A World at Different Speeds 

While there’s no clear way to classify rigid categories for AI maturity levels, we can see a spectrum based on how progressing with AI in terms of policy intent, institutional capacity, societal trust, and more. Each stage mentioned below reflects an evolving balance between innovation and oversight. You will see that different countries have moved quickly in different domains as per their socio-economic contexts, while lagging in others. 

Nascent Stage 

At the nascent stage, governments are mostly in the “thinking and planning” mode. You’ll see policy drafts, white papers, or high-level roadmaps, along with perhaps nonbinding guidelines or ethical principles. Many developing countries like India fit this stage where strategies are published or frameworks are proposed, but institutional capacity is still being assessed. Typical governance risks at this stage include ambiguity in regulations, fragmentation across departments, weak accountability, and excessive reliance on third-party vendors.  

Foundational Stage 

In this stage, governments begin to adopt binding sectoral regulation or risk-based rules in specific domains like healthcare, biometrics, public safety, etc. They start embedding AI risk assessment, human oversight, transparency, or fairness requirements into procurement or public contracts. Certain states in the U.S., including Colorado (SB 24-205) and New York (Local Law 144), can be placed around this stage. Uneven enforcement and resistance to scaling are major challenges at this stage. 

Systemic Stage 

Here, you see horizontal, risk-based regulation applied across sectors. Regulatory bodies take an integrated view of AI risk, with harmonized rules, conformity assessments, cross-sector coordination, and iterative review. The European Union strongly qualifies for this stage with the EU AI Act and the EU AI Office for national oversight frameworks and coordinated regulations. Major governance gaps here can be due to administrative complexities and resistance from the private sector. 

Trust-Anchored 

Governance at this level is adaptive, reflexive, and anticipatory. Governments not only enforce regulations but also constantly update and evolve them based on new AI developments. As a part of institutional readiness at this stage, governments may host national AI safety or alignment enforcers. Though no country seems like a strong contender for this stage but it is an aspirational level for establishing deep legitimacy and institutional trust among the citizens. 

Norm-shaping Stage 

This is the aspirational top-tier where government bodies can influence or even export governance norms internationally. This would need world-class, forward-facing institutions that can transcend national boundaries. In order for the global citizen to see AI systems as safe and reliable, this level would be ideal.  

Preparing for a Moving Target 

While governments move at different speeds on the AI maturity curve, businesses may not have the luxury of waiting, especially those operating across borders. Even businesses serving across U.S. states will face a patchwork of expectations, rules, and cultural attitudes toward AI. However, there are some universal priorities that we can start with. 

• Building AI Literacy: A huge amount of risks don’t come from malice but ignorance. Training programs should go beyond technical upskilling to include bias awareness, data ethics, and responsible use principles. Employees should be able to identify when AI is being used, understand how it affects decisions, and know the escalation paths when something feels off. In maturing governance systems, regulators often begin enforcement by assessing organizational awareness. 

• Maintaining Inventory: A well-maintained AI inventory, that lists every model, system, or automated decision tool in use, is the backbone of responsible AI management. It should include third-party tools and embedded features (the so-called “shadow AI” systems) that often escape oversight but carry the greatest risk. This inventory helps organizations trace data sources, ownership, and decision pathways, enabling faster compliance checks as new laws emerge.  

• Governance as per Use Case: AI governance cannot be one-size-fits-all. Risks vary dramatically depending on how and where AI is used. A chatbot, a credit scoring model, and a facial recognition system each pose very different ethical and operational challenges. Mature organizations assess risk at the use-case level, tailoring controls and reviews accordingly. 

• Focus on Outcomes: Regulators care less about what model you use and more about its real-world effects. This shift means organizations must evaluate whether AI-driven decisions are fair, explainable, and defensible. Governance teams should define metrics for acceptable error rates, bias thresholds, and audit triggers tied to outcomes that affect people’s rights or livelihoods.  

• Prioritizing Consent: Even before AI-specific regulations arrive, most countries already enforce data privacy laws that hinge on consent and lawful data use. Understanding these principles remains one of the fastest ways to identify and mitigate AI-related risk. Regularly auditing data flows, updating consent mechanisms, and making data collection purposes explicit are foundational practices that reduce exposure long before formal AI laws demand it. 

• Transparency Notice: Transparency is one of the few governance strategies that carries no downside. Providing clear notice when AI is involved in decisions like hiring, credit scoring, or customer engagement, and more. This builds credibility even in loosely regulated environments. Disclosure not only satisfies emerging legal standards but also creates a buffer of trust with customers, employees, and regulators. 

The Real Measure of AI Maturity 

AI governance is not a race with a single finish line. Every country’s path will reflect its political culture, institutional capacity, and societal priorities. But maturity matters because governance is as much about trust and continuity as it is about regulation. As governments evolve from nascent experimentation to norm-shaping leadership, their relationship with AI and with their citizens will evolve, too. The challenge is not just to catch up with technology, but to ensure that governance matures fast enough to keep humanity at the centre of intelligence.