受众中心
AI 治理与劳动力转型机构合作
面向寻求共同产出证据、专家对话、劳动力转型项目与治理洞察的机构合作中心。
案例研究合作
联合出版与简报
From AI Adoption to Workforce Architecture: Building the Next Phase of Human-Centered Workforce Transformation
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
Salesforce and Agentforce case study
An evidence-based case study of how Salesforce deployed Agentforce in customer support, redesigned human work, redeployed and reskilled employees, and reduced support staffing requirements without a simple one-to-one story of AI-driven layoffs.
What China’s enterprise leaders reveal about the next phase of AI at work
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.
Human-in-the-Loop Is Not One Thing
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.
DBS: How AI Is Redesigning Banking Roles, Reskilling and Redeployment
How DBS connects enterprise AI deployment to banking role redesign, reskilling and internal mobility, with evidence on operating-model transformation and changing hiring demand.
From AI Pilots to Governed Adoption: Why Institutional Readiness Determines the Next Phase of AI Transformation
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.
专家对话与圆桌
Practical use of AI
Generative AI is transforming education, especially writing. In a conversation with Swiss AI founder Yves Zumbühl, Qiqing He discusses practical AI implementation through PaperCheck, a writing-focused tool. Key themes include adoption in real classrooms, integrity-by-design, narrow measurable use cases, and institution-ready standards for trust, privacy, and outcomes.
The Future of Assessment
How AI is shifting the paradigm from standardized testing to continuous adaptive assessment.
劳动力转型项目
AI in Education Across Five Countries: Implementation Signals, Policy Gaps, and What Comes Next
What readers will get A comparative view of how AI in education is unfolding across the United States, Kenya, China, the United Arab Emirates and Switzerland. A practical map of the main implementation bottlenecks: teacher readiness, policy clarity, trust, infrastructure and assessment reform. Concrete signals for AI practitioners, education leaders and policy actors seeking grounded lessons rather than hype.
The Missing First Rung
This research note examines how artificial intelligence is changing the relationship between early-career work, learning and capability development. Drawing on five structured conversations with early-career professionals and one strategic peer conversation, it identifies three emerging organizational signals: developmental tasks may be automated while low-learning work remains; stronger AI-assisted outputs may conceal weaker understanding and ownership; and broad responsibility does not necessarily translate into meaningful development. The note argues that sustainable AI-enabled productivity requires organizations to redesign work, learning and career progression together. It proposes practical questions for deciding which tasks should be automated, augmented, protected or redesigned, and how employees should be expected to verify, explain, judge and independently modify AI-assisted work. The findings are exploratory and are intended to establish a research agenda rather than make representative claims about Gen Z as a whole.
AI in Education Unplugged: Closing the Access Gap Through Education-Driven Design
Artificial intelligence is transforming education, but its benefits remain out of reach for many of the communities that could benefit most. Drawing primarily from an hour-long public interview with Dr. Seiji Isotani and secondarily from the OECD Digital Education Outlook 2026 interview chapter, this memo argues that policymakers should stop treating infrastructure build-out as a precondition for AI in education. Instead, they should design around the infrastructure that already exists—especially mobile phones, intermittent connectivity, and teacher-led delivery models. The Brazil case discussed by Dr. Isotani shows that this approach can work at scale: 500,000 students across 7,000 schools and 20,000 teachers received materially faster feedback on writing, with statistically significant improvement and no meaningful urban-rural or resource-based gap in gains.
Exiting the AI Pilot Trap: Responsible Scale for AI Literacy, Governance, and Workforce Readiness
This brief synthesizes evidence and expert dialogue on scaling AI literacy and governance beyond pilots. It argues for treating AI literacy as baseline infrastructure, aligning responsible-use policy with cybersecurity maturity, and preparing for workforce change as task-displacement first.
治理洞察更新
China’s 15th Five-Year Plan: How AI Is Reshaping Governance and the Future of Work
A guide to China’s 2026–2030 framework for AI adoption, employment transition, public-sector transformation and risk governance
What Anthropic–PwC Reveals About the Limits of Outsourcing AI Readiness
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 bring in a stronger partn…
What OpenAI, GitLab, Gartner and the EU AI Act Reveal About the Next Enterprise AI Bottleneck
Recent signals from OpenAI, GitLab, Gartner, and the European Commission suggest that enterprise AI adoption is entering a new phase. The challenge is no longer simply whether organizations can access powerful AI tools, but whether they can redesign workflows, govern agents, prepare semantic infrastructure, and operationalize transparency requirements.
AI’s Move Into Managerial Workflows: What Amazon and Accenture Reveal
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…
From AI Adoption to Workforce Architecture: What CHROs and Policymakers Must Build Next
Enterprise AI transformation is moving from an adoption challenge to an institutional readiness challenge.<div><br></div><div>The first phase of enterprise AI was defined by access to tools. The secon…
When AI Transformation Breaks Workforce Trust: Signal from WiseTech
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…
最新战略发布
AI in Education Across Five Countries: Implementation Signals, Policy Gaps, and What Comes Next
What readers will get A comparative view of how AI in education is unfolding across the United States, Kenya, China, the United Arab Emirates and Switzerland. A practical map of the main implementation bottlenecks: teacher readiness, policy clarity, trust, infrastructure and assessment reform. Concrete signals for AI practitioners, education leaders and policy actors seeking grounded lessons rather than hype.
The Missing First Rung
This research note examines how artificial intelligence is changing the relationship between early-career work, learning and capability development. Drawing on five structured conversations with early-career professionals and one strategic peer conversation, it identifies three emerging organizational signals: developmental tasks may be automated while low-learning work remains; stronger AI-assisted outputs may conceal weaker understanding and ownership; and broad responsibility does not necessarily translate into meaningful development. The note argues that sustainable AI-enabled productivity requires organizations to redesign work, learning and career progression together. It proposes practical questions for deciding which tasks should be automated, augmented, protected or redesigned, and how employees should be expected to verify, explain, judge and independently modify AI-assisted work. The findings are exploratory and are intended to establish a research agenda rather than make representative claims about Gen Z as a whole.
AI in Education Unplugged: Closing the Access Gap Through Education-Driven Design
Artificial intelligence is transforming education, but its benefits remain out of reach for many of the communities that could benefit most. Drawing primarily from an hour-long public interview with Dr. Seiji Isotani and secondarily from the OECD Digital Education Outlook 2026 interview chapter, this memo argues that policymakers should stop treating infrastructure build-out as a precondition for AI in education. Instead, they should design around the infrastructure that already exists—especially mobile phones, intermittent connectivity, and teacher-led delivery models. The Brazil case discussed by Dr. Isotani shows that this approach can work at scale: 500,000 students across 7,000 schools and 20,000 teachers received materially faster feedback on writing, with statistically significant improvement and no meaningful urban-rural or resource-based gap in gains.
Exiting the AI Pilot Trap: Responsible Scale for AI Literacy, Governance, and Workforce Readiness
This brief synthesizes evidence and expert dialogue on scaling AI literacy and governance beyond pilots. It argues for treating AI literacy as baseline infrastructure, aligning responsible-use policy with cybersecurity maturity, and preparing for workforce change as task-displacement first.