LSE and the IMF just built a scoreboard for AI job disruption

What happened

The London School of Economics (LSE) recently initiated its global forum on AI and work, announcing several funded projects and new data sets. The objective is to measure the impact of AI on jobs with detailed data, moving beyond speculative conversations. Google.org is providing $1.5 million in funding to support LSE's research into AI and labor, specifically focusing on which tasks are being automated, where, and how quickly.

In addition to the LSE's research, Cohere Labs released the Agentic Task Ecosystem, a dataset comprising nearly 700,000 agent tools. An agent is defined as an AI capable of taking actions, not just providing answers. Plans were also announced for a global AI adoption index to track AI usage across industries, countries, and sectors over time. The IMF is also backing an AI Economy Data Hub, which aims to standardize data on AI's economic effects, including mapping inequality and disruption risk by country.

Why it matters

These initiatives mark a significant shift from anecdotal discussions about AI's impact on employment to a data-driven, evidence-based approach. When major institutions like the LSE and IMF standardize data through indexes and hubs, it directly influences policy, corporate, and investment decisions. The availability of precise metrics on AI adoption and job exposure will pressure industries and governments to react, potentially leading to changes in training budgets, investment in specific sectors, or even national policy adjustments.

The detailed task-level analysis funded by Google.org moves the conversation beyond broad industry generalizations. Instead of vague predictions about entire professions, the focus will be on specific tasks within roles that are prone to automation or augmentation. This granular understanding can help individuals and organizations adapt more effectively, identifying which skills need development and which workflows can be optimized.

The development of tools like the Global AI Adoption Index and the IMF's Data Hub will create a "scoreboard" that reveals which countries and sectors are leading in AI integration and which are most exposed to disruption. This transparency will highlight competitive advantages and vulnerabilities, informing strategic decisions at both national and corporate levels regarding workforce planning, education, and economic development.

What to watch next

• Will the Global AI Adoption Index become a widely cited metric influencing national and corporate AI strategies?
• How will governments and industries respond when specific data identifies their workforces or sectors as highly exposed to AI disruption?
• Which specific tasks and roles will the LSE's research pinpoint as most susceptible to automation versus augmentation, and how will that inform training programs?
• What new policy decisions will emerge as the IMF's AI Economy Data Hub provides clearer data on AI's impact on inequality and disruption risk across countries?
• How quickly will the Cohere Labs Agentic Task Ecosystem dataset be adopted by developers and researchers, and what new AI applications will it enable?

What this means for you

For business leaders and operators, these data initiatives translate into a more specific, actionable understanding of AI's impact. Rather than reacting to general fears about job displacement, you will gain access to evidence-based insights into which tasks within your organization are most ripe for automation or augmentation. This precision enables targeted investments in AI tools, workforce training, and process redesign, moving beyond broad AI hype to strategic implementation.

Your role as a leader will increasingly involve leveraging AI to optimize existing human decisions and processes, not just replacing staff. Use these upcoming indexes and data sets to inform your strategic planning: identify high-exposure tasks, re-skill your workforce towards judgment and complex problem-solving, and evaluate AI solutions based on proven workflow efficiencies rather than general capabilities. Preparing now means leveraging AI on your terms, ahead of external pressures.

Key takeaways

• LSE and IMF are building data tools to measure AI's detailed impact on jobs.
• Google.org is funding LSE research on task-level AI automation and labor impact.
• New data sets include Cohere Labs' Agentic Task Ecosystem and an IMF-backed AI Economy Data Hub.
• A Global AI Adoption Index will track AI usage and disruption across countries and sectors.
• These initiatives shift the AI and jobs conversation from speculation to evidence, driving policy and corporate decisions.

FAQ

What is the London School of Economics doing related to AI and jobs?

The London School of Economics (LSE) has launched a global forum on AI and work, announcing funded projects and data sets aimed at precisely measuring AI's impact on jobs. This includes a $1.5 million grant from Google.org for research into which tasks are being automated, where, and how fast. The LSE's work is designed to provide detailed, evidence-based insights into AI's effects on the labor market.

What is the IMF's role in measuring AI's impact on the economy?

The IMF is backing an AI Economy Data Hub, which aims to standardize data related to AI's economic implications. This data hub will track various aspects, including mapping inequality and disruption risk by country. The goal is to provide a consistent, institutionalized data source that can inform policy, corporate, and investment decisions regarding AI's influence on global economies.

What is an "agent" in the context of AI tools?

In the context of new AI tools like those in Cohere Labs' Agentic Task Ecosystem, an "agent" is an AI that can take actions, not just answer questions. It is described as an AI that can perform tasks on behalf of a user. The Agentic Task Ecosystem is a dataset containing nearly 700,000 such agent tools, indicating a growing trend towards AI systems that can execute actions autonomously.

How will the new AI indexes and data sets affect business decisions?

The new AI indexes, such as the Global AI Adoption Index, and data sets like the IMF's AI Economy Data Hub, will provide concrete numbers that can steer business decisions. When an index indicates an industry is lagging in AI adoption or a role is highly automatable, it creates pressure for businesses to adapt. This data can inform training budgets, investment in AI solutions, and strategic workforce planning, moving businesses from speculative AI discussions to evidence-based action.

What is the "Agentic Task Ecosystem" and why does it matter?

The Agentic Task Ecosystem is a dataset released by Cohere Labs, comprising nearly 700,000 agent tools. It matters because an "agent" is defined as an AI that can take actions, not merely answer questions. This dataset signifies a growing trend in AI development toward systems that can execute tasks autonomously. It provides a resource for understanding the scope and capabilities of these action-oriented AI tools.