This study examines the effectiveness of artificial intelligence (AI) tools, financial planners, and their combined use in promoting retirement saving behavior among 2,000 state and local government employees. The research introduces the Technology-Enabled Financial Help-Seeking (TEFHS) framework, extending traditional help-seeking theory to incorporate AI-based advice sources. Using logistic regression analysis, we find that all formal advice sources are associated with higher odds of retirement saving compared to using no advice. AI-only users demonstrate 75% higher odds of saving, financial planner-only users show 181% higher odds, and individuals using both sources exhibit 254% higher odds. These patterns support the TEFHS framework’s expectation that AI and human advisors work best when used together rather than in isolation, contributing distinct forms of capital to retirement planning decisions. Results indicate persistent demographic disparities, with women and Black participants showing lower saving rates despite advice access. The findings suggest that AI tools can expand access to retirement guidance for underserved populations while enhancing rather than replacing traditional advisory relationships. This research provides evidence-based insights for practitioners integrating technology into service delivery, policymakers considering AI-enabled employee benefits, and researchers studying technology adoption in financial services.

Accepted Oral Presentation