The Value of AI Advice: Personalized and Value-Maximizing AI Advisors Are Necessary to Reliably Benefit Experts and Organizations

TL;DR

This paper emphasizes the importance of designing personalized, context-aware AI advisors that reliably enhance expert decision-making and organizational value, addressing current shortcomings in AI advisory systems.

Contribution

It introduces a framework based on key pillars for creating reliable, personalized, and value-maximizing AI advisors tailored to specific contexts and expert behaviors.

Findings

AI advisors can undermine or enhance decision quality depending on design.

Personalized and context-aware AI advisors improve decision outcomes.

Lack of value-driven design contributes to AI advising failures.

Abstract

Despite advances in AI's performance and interpretability, AI advisors can undermine experts' decisions and increase the time and effort experts must invest to make decisions. Consequently, AI systems deployed in high-stakes settings often fail to consistently add value across experts and organizations and can even diminish the value that experts alone provide. Beyond harm in specific domains, such outcomes impede progress in research and practice, underscoring the need to understand when and why different AI advisors add or diminish value. To bridge this gap, we stress the importance of assessing the value AI advice brings to real-world contexts when designing and evaluating AI advisors. Building on this perspective, we characterize key pillars -- pathways through which AI advice impacts value -- and develop a framework that incorporates these pillars to create reliable, personalized, and value-adding advisors. Our results highlight the need for value-driven development of AI advisors that advise selectively, are tailored to experts' unique behaviors, and are optimized for context-specific trade-offs between decision improvements and advising costs. They also reveal how the lack of inclusion of these pillars in the design of AI advising systems may be contributing to the failures observed in practical applications.