AI workforce policy is entering a new phase as California, New York, and RAISE US build measurement, planning, and pilot systems to help state leaders, employers, educators, and labor organizations decide how to prepare workers for possible AI-related workforce disruption.

California is starting with a public tracker built on unemployment insurance claims. New York is building workforce planning, training, and employer reporting tools. RAISE US is organizing public- and private-sector partners around workforce transition pilots.

Since generative AI became widely available to the public, AI workforce discussions have centered on which jobs may change, which workers may be affected, and how quickly disruption could appear. The immediate challenge is building enough evidence to identify where support may be needed before the full labor-market impact is clear.

In short, AI workforce policy is beginning to move from prediction toward preparation. California is measuring possible AI-related unemployment patterns through a public tracker built on unemployment insurance claims, New York is developing workforce planning and employer reporting tools, and RAISE US is testing workforce transition models outside government.

AI workforce measurement is the use of labor-market data, employer reporting, training programs, and workforce pilots to identify where AI may be changing jobs, which workers may need support, and what policy responses should come next.

AI workforce measurement is an evidence-based approach to tracking how AI may affect workers, jobs, training needs, and labor-market policy.

Since generative AI became widely available to the public, workforce policy has been shaped by a difficult question: which jobs may change, which workers may be affected, and how quickly disruption could appear?

Governments have to prepare before those answers are clear. Forecasts can identify possible risk, but they do not tell a workforce agency which occupation, region, or worker group needs help first. That gap is pushing states toward labor-market data, employer reporting, and workforce planning systems that can guide a more targeted response.

California is starting with labor-market measurement. Its AI-Unemployment Tracker uses unemployment insurance claims and occupational AI exposure to monitor whether job-loss patterns are appearing in work more likely to be affected by AI. The dashboard is scheduled for monthly updates, giving the state a recurring view of claims by occupation, region, and worker group.

New York is building a planning and reporting structure around the same uncertainty. Governor Kathy Hochul launched the FutureWorks Commission, a group of experts, worker advocates, and business leaders tasked with recommending policy and private-sector responses to AI’s effects on workers. Because research estimates vary widely on job loss, adaptation, and economic disruption, the commission is meant to help the state identify real-time data strategies for monitoring AI’s workforce effects and decide what interventions workers, families, and small businesses may need.

New York is also considering a WARN reporting bill that would require employers to disclose when artificial intelligence or automation is a factor in covered workforce reductions. That reporting requirement would give policymakers more information when layoffs occur, instead of leaving AI-related workforce changes buried inside standard layoff notices.

California and New York are building different parts of the same policy response. California is measuring labor-market outcomes. New York is developing workforce strategy and employer reporting. Both approaches start from the same premise: AI workforce policy needs better evidence before governments can decide which supports should come next.

California’s tracker is the clearest example of that evidence-building effort. The system starts with unemployment claims, occupational AI exposure, and a monthly dashboard designed to detect early signs of workforce disruption.

California’s AI-Unemployment Tracker is a public dashboard designed to monitor whether job-loss patterns are emerging in occupations more exposed to AI. Researchers at the California Policy Lab’s UCLA site developed the tool with the California Employment Development Department, and the state describes it as a first-in-the-nation statewide online tracker of possible AI-related job loss trends.

The tracker was released under Governor Gavin Newsom’s executive order on AI and the workforce. California says the system is meant to serve as an early-warning tool for policymakers and the public, giving the state a recurring view of how AI may be intersecting with jobs and unemployment claims.

The dashboard uses unemployment insurance claims and compares them with occupations that have different levels of AI exposure. California plans to update the tracker monthly, so policymakers can watch whether claims begin to cluster in specific occupations, regions, sectors, or worker groups over time.

The key point: California is building a recurring labor-market measurement system. The tracker can identify patterns in unemployment claims among workers in AI-exposed occupations. It cannot prove that AI caused an individual worker to lose a job.

That limit is part of the system’s policy value. If claims begin rising in a specific occupation, region, or worker group, the state can examine where interventions may be needed most, including job-search support, retraining and upskilling opportunities, health-coverage guidance, and other essential resources. The tracker gives policymakers a starting point before they decide which support should come next.

California also places the tracker inside a wider workforce-readiness effort. The state says the executive order mobilizes state agencies, labor experts, economists, universities, and industry leaders to develop policies, gather data, and identify early warning signs of workforce disruption. California also cited workforce training resources, apprenticeship programs, and a statewide AI workforce strategy being developed with the California Workforce Association.

California officials described the tracker as a way to ground workforce decisions in data. Labor & Workforce Development Agency Secretary Stewart Knox said the tracker provides “a clearer picture of how AI is affecting working people and jobs” and shows “where we need to focus support and training.” California Policy Lab faculty director Till von Wachter said the tracker helps “replace speculation with evidence” as the state studies how to support affected workers.

California’s early data shows why those limitations matter. The system identified targeted labor-market patterns, but it did not find evidence of widespread AI job loss across the state.

California’s early data separates broad statewide conditions from narrower labor-market patterns. Across the state, the analysis found no evidence of rising unemployment insurance claims in AI-exposed occupations. Within that larger finding, the tracker still identified increases among some workers, occupations, sectors, and regions.

California says unemployment insurance claims increased among college-educated workers in occupations with high AI exposure after ChatGPT-3.5 became available to the public. The state also identified patterns in tech-heavy sectors and found a sustained increase among workers in occupations with high potential AI exposure in the San Francisco Bay Area.

In the tracker, AI-exposed occupations are jobs whose tasks are more likely to be affected by AI software. That classification helps California compare unemployment insurance claims across different types of work, but it does not prove why a worker filed a claim. A claim may reflect AI adoption, business conditions, outsourcing, management decisions, or another labor-market factor.

California Policy Lab senior researcher Ben Hyman described that distinction directly. “Right now, we are not seeing evidence of large-scale AI-related layoffs in California’s labor market,” Hyman said. “But we do see patterns in certain regions like the Bay Area, in certain tech-heavy sectors, and among highly AI-exposed workers with college degrees. It will be important to continue monitoring trends for those workers, as well as others, so that policymakers can respond appropriately.”

The data did not show large disproportionate increases by race, ethnicity, gender, or age among claimants in high-AI-exposure occupations. The early findings point to possible pressure in specific regions, sectors, and education levels, rather than a statewide pattern affecting one demographic group more than others.

California’s early findings give policymakers a way to monitor targeted pressure without overstating what the evidence can prove.

New York’s response starts with workforce planning rather than a public unemployment tracker. Governor Kathy Hochul launched the FutureWorks Commission to advise on policy and private-sector interventions that help workers, families, and small businesses benefit from AI, not only large corporations.

The commission will include experts, worker advocates, and business leaders. Its work includes identifying real-time data strategies for monitoring AI’s impact on workers and recommending interventions that protect economic security while preparing New Yorkers for AI-related changes in the labor market.

New York is pairing that planning work with targeted training programs. Empire State Development will launch AI Prep, a workforce development initiative focused on expanding access to AI skills and career opportunities. The program is aimed especially at low-income New Yorkers and people underrepresented in the technology sector who face barriers to training.

AI Prep is built around 2 tracks. AI Prep for Internships will provide undergraduate students with AI training tied to paid internship opportunities at leading technology companies. AI Prep for Jobs will connect low-income New Yorkers with AI careers through accelerated training programs and boot camps that can serve as alternatives to traditional degree pathways. New York has conditionally designated the State University of New York to lead the internship track and Pursuit to lead the jobs track.

The state is also building AI training for small businesses and entrepreneurs. Empire State Development will work with New York’s network of Entrepreneurship Assistance Centers, which operate in disadvantaged communities across the state, to provide AI education and adoption support. Welcome to Chinatown will serve as the AI Specialist EAC, acting as a statewide hub for AI-focused training, one-on-one support, EAC staff training, and a standardized AI curriculum that can be used across the network.

New York’s own workforce is part of the plan as well. The state completed an AI training pilot with more than 1,000 employees and is evaluating whether to expand training on responsible workplace AI use. The program is paired with a secure generative AI tool so state employees can apply the training directly.

The proposed Artificial Intelligence Workforce Impact Transparency Act would add an employer reporting layer. The bill would amend New York’s WARN Act by requiring employers to disclose when artificial intelligence or automation is a factor in covered workforce reductions. Unlike California’s tracker, which uses unemployment insurance claims and occupational AI exposure data, New York’s proposed reporting system would depend on employer disclosure when layoffs occur. Its stated purpose is to help the state track workforce changes tied to AI adoption and build data for future reskilling or economic development policy.

California and New York are building different parts of the same government response. California is measuring labor-market outcomes through unemployment claims and AI exposure. New York is developing workforce strategy, AI training programs, small-business support, state employee training, and a proposed reporting requirement that would add employer-provided data to the state’s understanding of AI-related workforce change.

RAISE US was created to help workers, employers, and states manage the transition to an AI economy. The nonpartisan national organization was launched by former U.S. Commerce Secretary Gina Raimondo and former Indiana Governor Eric Holcomb, and it will partner with governors, employers, workers, and training organizations on workforce strategies tied to AI adoption.

AI adoption may create new jobs over time, but workers and workforce systems need support during the transition. Raimondo described the gap as a missing people strategy for the AI economy, saying the country has a technology strategy for leading the global AI competition but does not yet have a people strategy.

RAISE US operates outside government, but it is designed to work closely with states. The organization says it will serve as a national hub that backs and connects other workforce efforts rather than duplicating them. It will use private and philanthropic capital to design and pilot corporate incentives for retraining and redeploying workers, support people through job transitions, and create training models tied to changing employer demand. It says success will be measured by whether workers land and keep good jobs.

The organization is built around 4 core areas. State partnerships will help governors reorient workforce and education systems for a changing labor market, including earn-and-learn apprenticeships, short-term credentials connected to employer demand, public funding tied to job outcomes, incentives for employers to retrain workers, and transition supports such as wage insurance and career navigation.

The employer coalition brings companies into the workforce transition process. RAISE US says employers have a direct view into where jobs are changing as AI is adopted, and the coalition will ask companies to help design pilots that test what effective worker transition looks like. The initiative includes both AI developers and companies adopting AI, which gives states a way to connect workforce planning with employer demand.

The education and training work will focus on AI-enabled, work-based training models that can expand access to alternatives to traditional education. RAISE US says it will use flexible capital to support providers and measure outcomes through employment, earnings, and advancement.

The Policy Lab will design and test workforce strategies, study what works, and turn findings into recommendations that can scale. RAISE US says the Policy Lab’s work will focus on ways to support workers through career transitions while encouraging employers to retrain and redeploy workers instead of letting them go. The organization says the Policy Lab’s work will not be funded by corporate contributions.

The first state partnerships are in Arkansas, Connecticut, Maryland, and Utah. Arkansas is using RAISE US support for Arkansas LAUNCH, an AI-powered career navigation platform that connects students and jobseekers to personalized learning and employer-linked career pathways. Maryland’s work includes expanding service-year pathways into fields such as healthcare and education, launching a competitive fund for career transition models, and creating an accelerator program for displaced workers pursuing entrepreneurship.

Connecticut Governor Ned Lamont said the partnership will help the state build policies, coalitions, and resources to help workers gain skills, support families through periods of change, and connect people to growing careers. Utah Governor Spencer Cox said the partnership puts government, employers, and educators at the same table.

RAISE US also brings major technology companies, employers, researchers, and philanthropies into workforce planning. Amazon, Anthropic, Microsoft, and the OpenAI Foundation are anchor partners. Bank of America is the primary corporate sponsor of the advanced manufacturing apprenticeship initiative. Additional listed supporters include ADP, AMD, Autodesk, Blackstone, Cisco, Cognizant, Deloitte, General Motors, IBM, Mastercard, ServiceNow, UPS, Workday, and several philanthropies.

RAISE US says it aims to raise $1 billion in multi-year commitments and has already secured more than half. The announcement ties those commitments to the organization’s workforce mission, but it does not break down how much comes through direct funding, corporate sponsorships, philanthropy, services, or other forms of support.

California and New York show how governments are building AI workforce policy through measurement, planning, training, and reporting. RAISE US adds a parallel layer outside government, where states, employers, educators, labor organizations, researchers, and philanthropies can test workforce models before larger policy decisions are made.

California, New York, and RAISE US are not presenting a finished answer to AI workforce disruption. Their efforts are still earlier in the policy process, focused on building the data, reporting, training, and pilot programs that can help governments and workforce partners understand where support may be needed before they decide what to scale.

California’s tracker gives policymakers an early-warning system for possible job-loss pressure in AI-exposed occupations. New York’s FutureWorks Commission gives the state a planning body charged with identifying real-time data strategies and recommending policy or private-sector interventions. The proposed New York WARN bill would add employer-reported data when AI or automation contributes to covered workforce reductions. RAISE US gives states and employers a place to test transition support, retraining, redeployment, apprenticeships, wage insurance, career navigation, and outcome-based training.

That kind of groundwork can shape practical workforce decisions. Policymakers need to know which workers need help, which regions are under pressure, when services should begin, and which interventions are worth funding. Workforce boards need evidence before directing training dollars. Employers need incentives and models for retraining workers before layoffs become the default response. Educators and training providers need clearer signals from employers about which skills connect to durable jobs.

The current response is still a framework, not a finished plan. Governments do not yet know how large AI-related displacement will become, which occupations will face the deepest changes, or which supports will work best. California, New York, and RAISE US are building the data and planning capacity needed to answer those questions with evidence instead of forecasts alone.

Effective workforce policy begins with understanding the problem before attempting to solve it. California’s tracker, New York’s planning efforts, and RAISE US’ cross-sector pilots all start from that same premise.

Q: How is AI workforce policy changing?
A: AI workforce policy is beginning to move from job-loss predictions toward measurement, planning, and worker preparation. California is measuring possible AI-related unemployment patterns, New York is developing workforce planning and employer reporting tools, and RAISE US is testing workforce transition models outside government.

Q: What is California’s AI-Unemployment Tracker?
A: California’s AI-Unemployment Tracker is a public dashboard developed by the California Policy Lab’s UCLA site and the California Employment Development Department. It uses unemployment insurance claims and occupational AI exposure data to monitor possible AI-related job-loss trends across the state.

Q: How does California’s AI-Unemployment Tracker work?
A: The tracker compares unemployment insurance claims with occupations that have different levels of AI exposure. In this context, AI-exposed occupations are jobs whose tasks are more likely to be affected by AI software. California plans to update the dashboard monthly so policymakers can watch whether claims rise in specific occupations, regions, sectors, or worker groups.

Q: Does California’s data show widespread AI job loss?
A: No. California’s initial analysis does not find evidence of rising statewide unemployment insurance claims in AI-exposed occupations. The early data identifies targeted patterns among college-educated workers in highly AI-exposed occupations, tech-heavy sectors, and workers in the San Francisco Bay Area.

Q: Can California’s tracker prove that AI caused layoffs?
A: No. The tracker can identify patterns in unemployment insurance claims among workers in AI-exposed occupations, but it cannot prove why a specific worker filed a claim. A claim may reflect AI adoption, business conditions, outsourcing, management decisions, or another labor-market factor.

Q: How is New York preparing for AI’s workforce effects?
A: New York launched the FutureWorks Commission to advise on AI workforce policy, data strategies, and private-sector interventions. The state is also developing AI Prep training programs, small-business AI education, state employee AI training, and a proposed WARN reporting law that would require employers to disclose when AI or automation contributes to covered workforce reductions.

Q: How is New York’s approach different from California’s?
A: California is using unemployment insurance claims and occupational AI exposure data to measure possible labor-market pressure. New York is focusing on planning, training, and employer disclosure. Its proposed WARN reporting system would depend on employers reporting when AI or automation is a factor in covered workforce reductions.

Q: What is RAISE US?
A: RAISE US is a nonpartisan national organization outside government that works with governors, employers, workers, and training organizations on workforce transition strategies. It plans to test retraining incentives, worker transition supports, employer-linked training models, apprenticeships, wage insurance, career navigation, and policy ideas with states and companies.

Q: How could this information shape future workforce policy?
A: Better measurement and reporting can help governments decide which workers need support, where services should be targeted, when intervention should begin, and which programs deserve funding. It can also help employers, educators, and workforce boards design retraining and redeployment programs around labor-market evidence instead of predictions alone.

California’s tracker, New York’s planning and reporting efforts, and RAISE US’ workforce pilots show AI workforce policy entering an evidence-building phase. Governments and workforce partners are creating systems to measure possible job-loss pressure, collect employer-reported layoff data, expand training access, and test worker transition supports before they know which responses can work at scale.

Policymakers are preparing for possible job losses and workforce transitions without claiming they already know the full labor-market outcome. California’s early data does not show widespread AI job loss across the state, but it does identify patterns in the Bay Area, tech-heavy sectors, and highly AI-exposed workers with college degrees. That gives policymakers a reason to keep monitoring specific areas without treating the early data as proof of broad AI-driven unemployment.

State leaders, workforce boards, employers, educators, training providers, and labor organizations should care because AI workforce disruption will not be solved by one institution alone. Governments can measure labor-market pressure and write reporting rules, but they need employer input on how jobs and skill needs are changing. Employers can identify changing tasks, but they need training partners and worker trust to support transitions. Educators and training providers can build new pathways, while labor organizations can push for worker input and transition support.

AI implementation is spreading while many workforce systems still depend on older assumptions about training, unemployment support, health-coverage guidance, and job transitions. Worker support is easier to design before displacement becomes widespread than after people have already lost income, benefits, or a path into replacement roles.

Leaders now have to decide how to balance forecasts with recurring evidence when building AI workforce policy. California is building claims-based measurement. New York is building planning, training, and employer reporting tools. RAISE US is testing workforce transition models with states, companies, educators, researchers, and philanthropies.

In short, AI workforce policy is beginning to move from prediction toward preparation. The priority is building enough evidence to identify where workers are affected, what support they need, and which responses deserve public or private investment.

The strongest AI workforce policies will come from leaders who build support before disruption makes choices for workers.

Editor’s Note: This article was created by Alicia Shapiro, CMO of AiNews.com, with writing support, AEO/GEO/SEO optimization, image concept development, and editorial structuring support from ChatGPT, an AI assistant. All final editorial decisions, perspectives, and publishing choices were made by Alicia Shapiro.