Generative AI for Business Strategy: Using Foundation Models to Create Business Strategy Tools
This work addresses business strategy development for stakeholders by offering a novel application of AI, though it appears incremental in combining existing models for a new domain.
The authors tackled the problem of business decision-making by using foundation models like GPT4, NER, and zero-shot classifiers to analyze unstructured text data and generate signed business networks, resulting in tools that provide market insights and quantitative guidance for stakeholders.
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.