A new Conference Board report maps out four possible futures for AI-driven labor disruption and the implications stretch well beyond Silicon Valley.
Artificial intelligence is moving through US workplaces faster than any technology in modern history, but its effects on jobs and wages remain stubbornly hard to measure.
New research by The Conference Board lays out four distinct scenarios for how AI could ultimately reshape the labor force, and calls on business leaders, policymakers, and educators to prepare now rather than wait for certainty.
The report identifies four potential paths: gradual augmentation, in which AI primarily helps workers rather than replacing them; concentrated gains, where a limited subset of industries and roles capture most of the productivity benefits; massive displacement, which could produce job losses on a scale rivaling the most severe economic shocks in US history; and uneven disruption, where significant job loss hits certain occupations while others grow.
The adoption gap
Through the end of 2025, approximately 18% of US firms and 41% of US workers reported using AI, with adoption particularly high among larger firms and in knowledge-intensive sectors such as professional services and finance.
Despite this rapid diffusion, individual worker productivity gains and employment effects have been slower to materialize and remain difficult to measure.
The report draws a pointed parallel to the so-called Solow Paradox – the observation by Nobel laureate Robert Solow in 1987 that the computer age was visible everywhere except in the productivity statistics. History eventually proved those gains real, arriving some two decades later. The Conference Board suggests AI may follow a similar delayed trajectory, though its reach into cognitive work could make the transition faster and more disruptive.
The Conference Board projects that within three years, 60 to 70% of jobs in the cognitive workforce – roles centered on knowledge and information tasks rather than manual labor – will involve collaboration between humans and AI, compared with just 15 to 25% involving human-only work.
What advisors need to watch
The report’s “concentrated gains” scenario is particularly relevant to the wealth management industry.
Unlike previous automation waves that mostly affected middle-skill routine jobs, AI displacement risks span the income spectrum, including high-income earners and knowledge workers that largely benefited from previous technological advances.
Workers in trades or manual labor may be less directly exposed to some forms of AI disruption and could see relative gains compared to disrupted knowledge workers. The Conference Board’s AI and Automation Risk Index, using Occupational Information Network data, estimated in 2024 that approximately 48% of tasks in STEM fields have high task exposure to AI, compared to just 23% of tasks in manual trades and production.
That inversion matters for advisors whose clients include technology professionals, financial analysts, and others in white-collar fields historically considered insulated from automation risk.
The report adds a geographic dimension: one analysis of 195 US metro areas found that just 30 accounted for nearly 70% of all job postings seeking AI-related skills, creating risks for cities with dominant industries likely to face AI-related disruption.
Meanwhile, within the advisory industry itself, the debate about AI’s role is actively playing out. As InvestmentNews has reported, AI is already eliminating back-office roles at advisory firms even as human advice remains valued. Separately, next-generation advisors are flagging concerns about entry-level career pathways – a worry the Conference Board’s report validates, noting that several studies have linked occupational AI exposure to lower employment for early-career workers.
Productivity gains – but with limits
The report acknowledges AI’s demonstrated productivity upside. A 2023 analysis of AI use in a customer support setting found an increase of 14% in the number of issues resolved per hour.
A separate experiment involving writing tasks showed that AI helped workers be more productive and improved worker satisfaction, with the biggest gains concentrated among workers with the weakest baseline writing abilities. AI has also shown significant productivity gains among software developers – a 26% increase in task completion – but again with higher rates of adoption and productivity gains among less-experienced developers.
However, the productivity story is not uniformly positive. A 2023 study found that giving management consultants access to AI increased the speed of task completion while also improving quality, but for tasks outside the frontier capability of the AI model, consultants with access to AI were 19% less likely to produce correct solutions than those without access.
The implication for financial services professionals is clear: AI tools may enhance performance in routine analysis while introducing new failure modes in complex or novel situations; precisely the scenarios where clients most need sound human judgment. As InvestmentNews has explored in depth, the question for advisory firms is not whether AI will change hiring, but which roles it will change and how fast.
Three things leaders should do now
David K. Young, President of The CEO Center at The Conference Board, says business leaders and policymakers cannot wait to see the labor-market effects before acting, because AI is advancing so fast.
“The challenge is not to predict with certainty whether AI will create jobs, eliminate them, or fundamentally change how they are performed. It is to prepare for each possibility,” he said.
The report calls for three broad categories of action. First, improving data collection and establishing early-warning indicators, including leveraging AI-related changes in job postings, job loss, and earnings to enable rapid responses to changing conditions.
Second, investing in worker training and education – with particular emphasis on pathways for highly educated and mid-career workers who may need to translate existing expertise into adjacent occupations rather than begin entirely new careers.
Third, modernizing unemployment insurance and public-benefit systems before a major shock occurs rather than attempting to build capacity during a crisis.
For CEOs in the financial services sector, the report has a specific message: leaders should reassess their talent-pipeline, succession, and knowledge-transfer strategies to ensure that reduced entry-level hiring does not weaken the future supply of experienced workers, and that senior departures do not erode critical institutional knowledge.
The report notes that most firms remain in the “early experimentation” phase of AI maturity. The window to shape the outcome, rather than simply absorb it, remains open.
The full report, AI and the Labor Force: Scenarios for Stakeholders, is available at conference-board.org.