Generative artificial intelligence and evaluating strategic decisions

• 成果类型: Article
• 署名作者: Doshi, Anil R.; Bell, J. Jason; Mirzayev, Emil; Vanneste, Bart S.
• 署名单位: University of London; University College London; University of Oxford
• 刊物名称: STRATEGIC MANAGEMENT JOURNAL
• ISSN/ISSBN: 0143-2095
• DOI: 10.1002/smj.3677
• 发表日期: 2025
• 页码: 583-610
• 关键词: business models; generative artificial intelligence (AI); large language models (LLMs); strategic decision making; STRATEGIC DECISIONS

摘要

Research SummaryStrategic decisions are uncertain and often irreversible. Hence, predicting the value of alternatives is important for strategic decision making. We investigate the use of generative artificial intelligence (AI) in evaluating strategic alternatives using business models generated by AI (study 1) or submitted to a competition (study 2). Each study uses a sample of 60 business models and examines agreement in business model rankings made by large language models (LLMs) and those by human experts. We consider multiple LLMs, assumed LLM roles, and prompts. We find that generative AI often produces evaluations that are inconsistent and biased. However, when aggregating evaluations, AI rankings tend to resemble those of human experts. This study highlights the value of generative AI in strategic decision making by providing predictions.Managerial SummaryManagers are seeking to create value by integrating generative AI into their organizations. We show how managers can use generative AI to help evaluate strategic decisions. Generative AI's single evaluations are often inconsistent or biased. However, if managers aggregate many evaluations across LLMs, prompts, or roles, the results show that the resulting evaluations tend to resemble those of human experts. This approach allows managers to obtain insight on strategic decisions across a variety of domains with relatively low investments in time or resources, which can be combined with human inputs.