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.
Independent institutional intelligence on AI adoption
AIGW Policy Observatory tracks how AI is governed, implemented and absorbed inside real institutions—connecting policy change, implementation evidence and workforce transition.
Current institutional intelligence
Policy signals, implementation evidence, workforce analysis, and institutional briefings for leaders navigating governed AI adoption.
AIGW Policy Observatory
Human presence is not human authority. Drawing on eight real-world AI deployments, this brief examines who can review, approve, override and make final decisions.
Read articleChina is building an emerging system for AI employment-impact monitoring, skills intelligence, reskilling and public employment services. What matters next is whether it improves...
Global AI Governance and Workforce Transformation Policy Observatory
A deep dive into how BNY moved from enterprise-wide AI access to employee-built agents, digital employees and an AI-enabled workforce operating model.
Global AI Governance and Workforce Transformation Policy Observatory
A policy and employer design framework for rebuilding entry-level work as AI automates traditional junior tasks and changes how early-career workers develop judgment, capability...
AI Governance Profiles
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.
Tracker · Evidence product
Track how real organizations are using AI to change tasks, workflows and roles — with every case linked to public evidence and classified by evidence strength.
LIVE EVIDENCE LANDSCAPE
Preview of the current dataset
Primary work change
Task Compression
Task Substitution
Workflow Redesign
Role Redesign
Each Signal appears once by its primary work change; the stages show the strength of the public evidence.
Assessments
Three assessments for different stages of AI development — from personal exploration to organizational transformation.
Explore → Develop → Transform
Explore · Individual
Discover the AI-era player profile that best reflects how you currently work with AI.
6–7 minutes · 7 primary Player Types + the additional Mission Integrator identity · Shareable result
Discover your player typeDevelop · Individual
See where your AI capability is strong, where it breaks down, and what to develop next.
17–22 min · 8 capability dimensions · Development report
Take the full assessment Learn about the AI fluency assessment
Transform · Institutional
See whether AI adoption is actually changing work — and whether there is evidence that it is creating value.
10–12 min · 6 organizational domains · Leadership snapshot
Explore the Quick ScanImplementation Evidence
Case-based analysis of how organizations move from AI pilots to governed adoption, workflow redesign, workforce transformation, implementation risk, and institutional capability.
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…
Institutional research
Research briefs, reports, and downloadable intelligence assets for AI governance, workforce transformation, and institutional implementation.
Human presence is not human authority. Drawing on eight real-world AI deployments, this brief examines who can review, approve, override and make final decisions.
governance
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
How DBS connects enterprise AI deployment to banking role redesign, reskilling and internal mobility, with evidence on operating-model transformation and changing hiring demand.
DBSFROM EVIDENCE TO ACTION
Helping people navigate AI-driven changes in work.
Stay in the Loop connects understanding, capability diagnosis, reskilling and real-world application to help people navigate how AI is changing work.
An AIGW Policy Observatory initiative moving from evidence to action.
Cumulative reach is not unique people. Participations may repeat; neither measure demonstrates capability improvement or outcomes.
Knowledge assets
Recorded expert dialogues and public convenings — citable knowledge assets from the Observatory’s programme.
Mr.Pat Yongpradit
AI Education Policy Perspective
Watch replay
Young Global AI Leaders from Chengdu, Chicago, Dubai, Nairobi, Lucerne
AI Education Policy Panel Discussion
Watch replayJoshua Dahn
Learning to Think in the Age of AI
Watch replayEvidence and Credibility
The Observatory combines public policy analysis, implementation cases, workforce evidence, and expert dialogue to produce intelligence that is relevant to institutional decisions.
Review Our Evidence and ApproachInstitutional Solutions
We work with enterprises, public institutions, and ecosystem partners to translate AI policy and implementation evidence into governed adoption, workforce capability, and institutional learning.
Executive interpretation of regulatory developments, governance expectations, implementation implications, and cross-border policy change.
Structured documentation and analysis of how organizations move from AI pilots to governed adoption, workflow redesign, and workforce transition.
Focused expert conversations that surface implementation challenges, emerging practices, and institutional learning.
Research-backed support for role transition, management capability, workflow redesign, responsible adoption, and workforce readiness.
Collaborative research on governance systems, institutional adoption, workforce transformation, and implementation evidence across jurisdictions.
