Melissa Ostrower Discusses Considerations for Using AI Tools in Retirement Plans

Why It Matters

Improper AI oversight can expose plan sponsors to legal liability and jeopardize participants’ retirement outcomes, making fiduciary diligence a competitive imperative.

Key Takeaways

• AI can streamline plan administration but introduces bias and privacy risks
• Fiduciaries must conduct vendor due‑diligence under ERISA standards
• Regulators may treat AI‑driven decisions as fiduciary breaches
• Transparent algorithms help mitigate discrimination claims in retirement outcomes
• Ongoing monitoring is essential as AI models evolve over time

Pulse Analysis

The retirement‑plan market is rapidly embracing AI to cut operational costs, improve investment recommendations, and personalize participant communications. Vendors tout predictive analytics that can automate eligibility checks, contribution allocations, and compliance reporting. While these efficiencies promise multi‑million‑dollar savings for large sponsors, the technology also brings opaque decision‑making processes that can inadvertently embed bias or expose sensitive personal data. As AI becomes a core component of plan services, the stakes for fiduciaries have risen dramatically.

Under ERISA, plan sponsors owe a duty of prudence and loyalty to participants, which now extends to the algorithms they deploy. Ostrower advises that fiduciaries treat AI tools like any other vendor, conducting thorough due‑diligence that examines model validation, data‑security protocols, and the vendor’s governance framework. Key risk areas include algorithmic discrimination, inaccurate data feeds, and the potential for unintended regulatory violations. Regulators such as the Department of Labor and the SEC have signaled that AI‑driven decisions could be deemed breaches of fiduciary duty if they are not reasonably prudent or cause participant harm.

Practically, sponsors should demand transparency clauses that require vendors to disclose model logic, bias‑mitigation strategies, and audit trails. Contracts must include service‑level agreements for model monitoring, periodic re‑validation, and clear remediation steps for adverse outcomes. As AI models evolve, continuous oversight—through internal data science teams or third‑party auditors—will be critical. Looking ahead, industry bodies are likely to issue formal guidance on AI governance in retirement plans, making early adoption of robust oversight practices a competitive advantage for forward‑thinking employers.