ChatGPT vs. Experts: Can GenAI Develop High-Quality Organizational and Policy Objectives?

Abstract

This paper explores the efficacy of generative artificial intelligence (GenAI) for value-focused thinking, specifically the ability to generate high-quality sets of objectives for organizational or policy decisions. Overall, we find that most of the GenAI objectives are viable individually, but the sets as a whole are highly flawed. They often include nonessential considerations, omitting important ones. In addition, they are redundant and nondecomposable, often because of a tendency to include means objectives even when explicitly instructed not to. However, the sets of objectives can be improved by implementing best practices in prompting and with decision analysis (DA) expertise. The results provide further evidence of the importance of a human in the loop; in this case, GenAI tools are helpful for brainstorming objectives, but an expert with a background in decision analysis is needed before the results are used to support decision making. To facilitate this, a four-step approach incorporating the relative strengths of both GenAI and decision analysts is presented and demonstrated. History: This paper has been accepted for the Decision Analysis Special Issue on the Implications of Advances in Artificial Intelligence for Decision Analysis. Supplemental Material: The online appendix is available at https://doi.org/10.1287/deca.2025.0387 .

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