Doshi, Anil R;
Bell, J Jason;
Mirzayev, Emil;
Vanneste, Bart;
(2024)
Generative Artificial Intelligence and Evaluating Strategic Decisions.
SSRN: Amsterdam, Netherlands.
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Abstract
Strategic 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.
| Type: | Working / discussion paper |
|---|---|
| Title: | Generative Artificial Intelligence and Evaluating Strategic Decisions |
| Open access status: | An open access version is available from UCL Discovery |
| Publisher version: | https://doi.org/10.2139/ssrn.4714776 |
| Language: | English |
| Additional information: | This version is the version of record. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | generative artificial intelligence, artificial intelligence (AI), large language models (LLMs), strategic decision making, strategic decisions, business models |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > UCL School of Management |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10195951 |
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