AI in Government

AI offers governments powerful tools to modernise regulatory systems. From streamlining legal drafting to targeting inspections and analysing stakeholder feedback, AI can help make regulation more adaptive, inclusive and data-driven. But realising these benefits requires moving away from static, one-time rulemaking and embracing a more dynamic “adapt-and-learn” approach. The potential is vast — but so too are the challenges, including transparency, legal adaptability and the need for robust oversight.

The current state of play

AI is supporting both regulatory design and delivery in practical, if still early-stage, ways:

• Drafting regulations and related documents. Natural language processing tools are helping governments draft legal texts, identify inconsistencies and improve legislative clarity. AI can reduce manual work and improve quality control in complex regulatory environments.
• Enhancing the agility of regulatory assessments. AI supports faster, more continuous regulatory impact assessments. Predictive tools are helping estimate regulatory burdens, simulate policy outcomes and evaluate the potential impacts of new rules.
• Personalising advice to policy makers. AI chatbots can support regulatory design, for example, by providing best practice advice on how to design and implement a regulatory sandbox, or how to draft a regulatory impact assessment.
• Promoting stakeholder engagement in regulatory design. AI chatbots and analytics are making it easier to engage the public during consultations, respond to questions, and summarise large volumes of input — helping regulators shape more inclusive and responsive rules.
• Enhancing economic regulators’ functions. Economic regulators are experimenting with AI for tasks such as complaint handling, market monitoring and inspections. Some are developing tools to predict risks, automate reports and detect non-compliance in real time.
• Refining validation of risk criteria. Machine learning is being used to improve how regulators score risk, revise inspection criteria and prioritise enforcement — particularly when resources are limited and accurate targeting is critical.
• Enhancing risk modelling to improve targeting of inspections. AI models can analyse historical data and online content (including social media) to identify emerging risks. These models allow regulators to make more informed decisions about where and how to conduct inspections.
• Improving non-compliance identification. AI systems are being deployed to detect anomalies, classify applications and flag high-risk entities — enhancing compliance monitoring and allowing staff to focus on complex or suspicious cases.
• Supporting stock reviews of existing regulations and simplification efforts by identifying regulatory obligations in legislation.

Governments are still navigating implementation challenges such as outdated legal frameworks, low-quality data, and shortages of AI-literate personnel. Ensuring algorithmic transparency and avoiding skewed outcomes in automated decision-making remain major concerns, particularly when regulatory decisions affect rights or access to services.

Examples from practice

• United Kingdom: AI-assisted legal drafting and policy planning. The UK’s i.AI team has developed Lex (for drafting legislation) and Parlex (for predicting parliamentary reactions), helping officials improve legal clarity and anticipate political dynamics.
• Germany: Measuring regulatory burdens with machine learning. Germany’s Federal Statistical Office is developing AI systems to estimate compliance costs in draft legislation — freeing human analysts to focus on complex, high-impact rules.
• Brazil: Transport oversight powered by real-time AI. Brazil’s National Agency for Land Transportation (ANTT) uses an AI-enhanced system to monitor infrastructure data from 26 concessionaires, improving supervision of the road sector.
• Denmark: Online product safety via AI scraping tools. The Danish SAFE tool scans e-commerce websites in 16 countries to detect potentially unsafe products, based on visual and textual patterns linked to EU safety alerts.
• Israel: Automated approvals for non-profits. Israel’s Corporations Authority has reduced “Good Standing” approval times from 45 days to 1 hour by automating checks using AI, improving service while reserving human review for flagged cases.

Untapped potential and the way forward

AI’s use in regulation is still emerging, but it has the potential to drive regulatory efficiency for simplification, burden reduction and confidence in regulatory systems. Digital-ready legislation could enable AI across the policy cycle, from drafting to delivery and revision. Establishing or repurposing existing AI tools for regulatory delivery allows real-time monitoring and adjustment. To support this, governments need legal authority, data infrastructure, skilled staff and adaptive oversight, underpinned by transparency, trust and accountability. This will help ensure AI contributes to better rulemaking and delivery — not just faster regulation.

Learn more

Review a detailed section on AI in regulatory design and delivery here.