AIGW Policy Observatory — Global AI Governance and Workforce Transformation Policy Observatory. AIGW Policy Observatory tracks how AI is governed, implemented and absorbed inside real institutions—connecting policy change, implementation evidence and workforce transition.
精选机构洞察
Policy signals, implementation evidence, workforce analysis, and institutional briefings for leaders navigating governed AI adoption.
World Economic Forum · Qiqing He
China is becoming an important test case for what comes after AI adoption: how companies redesign processes, develop people and build workforce systems around the technology. The...
阅读文章This research note examines how artificial intelligence is changing the relationship between early-career work, learning and capability development. Drawing on five structured...
A guide to China’s 2026–2030 framework for AI adoption, employment transition, public-sector transformation and risk governance
As frontier AI firms and professional-services firms move deeper into enterprise deployment, a tempting assumption is emerging: if AI implementation is hard, the answer is to...
AI 治理概况
Country and jurisdiction profiles on AI laws, governance frameworks, institutions, enterprise implications, and workforce readiness.
Asia-Pacific
China’s AI governance system is not organized around a single horizontal AI Act. It is a layered governance stack built from cybersecurity, data security, personal information protection, internet information service regulation, algorithm recommendation rules, deep synthesis rules, generative AI service measures, AI-generated content labeling requirements, cross-border data controls, and technical standards. The result is a state-led, security-sensitive, platform-and-data governance model in which AI compliance is closely tied to content governance, data governance, filing or assessment mechanisms, technical labeling, and industrial policy.
North America
The United States remains a high-impact two-level governance jurisdiction: federal executive policy, agency authority, procurement, standards and enforcement operate alongside increasingly consequential state statutes and state attorney-general implementation. There is still no single comprehensive federal AI law, so enterprise exposure depends on federal/state/sector mapping.
Europe
The European Union remains the anchor jurisdiction for binding, cross-sector AI governance. Its model is distinctive because it combines a directly applicable regulation, a tiered risk framework, centralized Commission and AI Office oversight for general-purpose AI models, and member-state market surveillance for most AI systems. Since the previous review, the Digital Omnibus on AI has become binding and extended some high-risk implementation dates, while AI Act enforcement and Article 50 transparency obligations began operating on 2 August 2026.
实时证据图谱
当前数据集预览
主要工作变化
每条信号按其主要工作变化出现一次；各阶段显示公开证据的强度。
精选测评
先从快速、可分享的体验开始，或深入了解你的 AI 能力模式与发展方向。
实施证据
Case-based analysis of how organizations move from AI pilots to governed adoption, workflow redesign, workforce transformation, implementation risk, and institutional capability.
Workforce Transition Case
Salesforce
劳动力转型
Salesforce provides an unusually complete public view of how an AI deployment can change both work and workforce structure. Agentforce absorbed a large share of routine support interactions while human work shifted toward complex, proactive and consultative activity.
AI workforce transformation is now entering a more difficult phase.The first phase was mostly about adoption: which tools employees should use, how much productivity they could unl…
AI is moving from individual productivity assistance into managerial workflows.That distinction matters. A productivity tool helps an employee work faster, summarize a document, pr…
机构研究
面向 AI 治理、劳动力转型与机构实施的研究简报、报告和可下载洞察资源。
China is becoming an important test case for what comes after AI adoption: how companies redesign processes, develop people and build workforce systems around the technology. The article examines signals from Chinese enterprises, career development research and China’s emerging employment policy response to AI-driven workforce transformation.
AI
AI workforce transformation is moving beyond tool adoption. This policy brief explains why enterprises need workforce architecture — integrating strategy, workflows, roles, capabilities, governance, and trust — to scale responsible, human-centered AI adoption
governance
As AI adoption accelerates across sectors, the central challenge is no longer access to tools but the ability of institutions to redesign workflows, governance, and workforce systems around them. This flagship brief from the Global AI Governance and Workforce Transformation Policy Observatory examines why many organizations remain trapped in fragmented experimentation and outlines a practical framework for moving toward governed, scalable implementation.
technology知识资源
Recorded expert dialogues and public convenings — citable knowledge assets from the Observatory’s programme.
Mr.Pat Yongpradit
AI Education Policy Perspective
观看回放Young Global AI Leaders from Chengdu, Chicago, Dubai, Nairobi, Lucerne
AI Education Policy Panel Discussion
观看回放Joshua Dahn
Learning to Think in the Age of AI
观看回放
