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

arXiv: https://arxiv.org/abs/2308.14182 · HTML: https://mlsanity.com/paper/2308.14182/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 10 references that are in the map.
- First posted on arXiv 27 Aug 2023.

## Builds on (most cited first, 2 of 2)

- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [Benchmarking Zero-shot Text Classification: Datasets, Evaluation and Entailment Approach](https://mlsanity.com/paper/1909.00161/) (2019)

## Built on by (most cited first, 4 of 4)

- [A Survey of Large Language Models for Financial Applications: Progress, Prospects and Challenges](https://mlsanity.com/paper/2406.11903/) (2024)
- [GreedLlama: Performance of Financial Value-Aligned Large Language Models in Moral Reasoning](https://mlsanity.com/paper/2404.02934/) (2024)
- [UCFE: A User-Centric Financial Expertise Benchmark for Large Language Models](https://mlsanity.com/paper/2410.14059/) (2024)
- [Dynamic Hedging Strategies in Derivatives Markets with LLM-Driven Sentiment and News Analytics](https://mlsanity.com/paper/2504.04295/) (2025)

## Abstract (arXiv)

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.