California Gov. Gavin Newsom issued an executive order to attempt to deal with the impacts of artificial intelligence on workers and businesses.
The order directs the state to explore policies that include severance standards, employment insurance and transition support for displaced workers, worker ownership models, universal basic capital concepts, expanded workforce training and better tracking of hiring and payroll trends to help respond to potential layoffs and economic disruption.
The order directs the certain state agencies to:
- Evaluate and support opportunities to expand and enhance worker ownership models to support capital growth and build wealth from productivity gains among workers, including employee-owned company structures.
- Support small businesses through educational and incentive opportunities on best practices and applications for using emerging technology.
- Identify ways the collective bargaining process has delivered positive outcomes for workers.
- Add more on-the-job training and AI preparation in higher education.
- Review policies that provide workers with a safety net, including severance and other forms of compensation such as stock or other forms of equity.
- Increase awareness and enrollment of employment insurance programs.
- Create an AI playbook to modernize job training programs, including expanding strategies for connecting dislocated workers with training and technical assistance.
The order mandates that within 90 days the Labor and Workforce Development Agency and other state agencies in consultation with academic and private industry partners must provide a review of emerging research identifying the potential workforce impacts of technological shifts, including AI’s impact on California’s labor market and potential disproportionate impacts on demographic groups.
“The prior wave underdelivered. The savings pool is smaller than assumed,” Bain warned. “And the investment case for the current wave was sized against projections rather than actuals.”
While some companies are funding fresh investment in generative and agentic AI with realized savings, the largest share (44%) cited targeted savings among their top sources of funding for the next wave of outlays, according to the report.
In a similarly cautionary report last year showing 95% of corporate AI pilots fall flat, an MIT research group concluded that the “primary factor keeping organizations on the wrong side of the GenAI Divide is the learning gap, tools that don’t learn, integrate poorly, or match workflows.”
The Bain report isolated a different problem.
“Despite a decade of investments in data modernization running well into hundreds of billions of dollars globally, the No. 1 reason AI programs underperform is that companies cannot reliably get access to their own data,” Bain said.
Its prescription: Instead of waiting to structure all of their data to make it ingestible by AI, companies should start with what’s available to feed into the models — and then use AI to help sort out how to structure the rest.
Companies that were meeting their savings targets reported running into barriers with data structure and accessibility at even higher rates than those missing their targets, but they were less likely to report organizational challenges such as insufficient budgets or competing priorities.
