The Desire for AI Advice in Retirement Plans: A Latent Class Analysis

This study explores employee preferences for generative AI-driven financial advice in employer-sponsored retirement plans using latent class analysis grounded in Rogers's diffusion of innovations theory. Based on a survey of 2,000 public-sector employees, we identify five distinct adoption segments ranging from AI-Integrated Consumers (18.9%) who actively embrace both AI tools and traditional advisors, to Traditionalists (9.1%) who resist both technologies and professional advice. Contrary to expectations that AI adoption would reduce reliance on human advisors, our findings reveal a complementary relationship: employees with the highest AI adoption rates demonstrate 72% engagement with financial professionals compared to only 15% among technology-resistant segments. The analysis reveals that Employer-Driven AI Users (27.7%) represent a critical bridge segment, suggesting workplace-sponsored AI tools can facilitate adoption among employees who might otherwise resist independent AI exploration. Additionally, 40% of employees show low engagement with traditional financial professionals but varying degrees of openness to AI-assisted guidance, indicating significant potential for these tools to serve underserved populations. These findings challenge binary assumptions about AI versus human advice and provide actionable insights for plan sponsors implementing targeted, segment-specific strategies. The results demonstrate that successful AI integration in retirement planning depends on understanding diverse employee preferences and positioning technology as a complement to, rather than replacement for, human expertise in financial decision-making.