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Generative AI for Business Strategy: Using Foundation Models to Create Business Strategy Tools

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arxiv 2308.14182 v1 pith:4UR5WZGH submitted 2023-08-27 cs.CL

classification cs.CL
keywords modelsbusinessfoundationartifactsdatagenerativelargemultiple
verification ladder T0 review T1 audit T2 compute T3 formal
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Generative models (foundation models) such as LLMs (large language models) are having a large impact on multiple fields. In this work, we propose the use of such models for business decision making. In particular, we combine unstructured textual data sources (e.g., news data) with multiple foundation models (namely, GPT4, transformer-based Named Entity Recognition (NER) models and Entailment-based Zero-shot Classifiers (ZSC)) to derive IT (information technology) artifacts in the form of a (sequence of) signed business networks. We posit that such artifacts can inform business stakeholders about the state of the market and their own positioning as well as provide quantitative insights into improving their future outlook.

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Cited by 1 Pith paper

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  1. Investigating Creativity in Humans and Generative AI Through Circles Exercises

    cs.HC 2025-02 conditional novelty 4.0 of 10

    Using the Circles Exercise, the authors report that both humans and generative AI concentrate their drawings in a narrow set of categories, and Chain-of-Thought prompting does not substantially broaden that range.

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