Intersectoral Analysis of Enterprise Architecture Documentation Using Large Language Models
Year: 2025
Type: article
Abstract: Enterprise Architecture (EA) documents capture strategic IT plans of organizations and may exhibit patterns that vary by industry. This paper explores the use of a large language model (LLM) to automatically analyze a cross-sector corpus of EA and IT strategy documents. We develop an approach using GPT-4 to encode documents into semantic embeddings and cluster them to reveal shared themes and sector-specific divergences. Our preliminary results indicate that the LLM can surface industry-specific concerns – for example, banking and insurance documents emphasize regulatory compliance and security, whereas manufacturing focuses on product innovation and agility. Clustering the LLM-derived embeddings grouped organizations by these thematic similarities, confirming the hypothesis. Visualizations such as similarity maps and word clouds are presented to illustrate the distinct clusters. This study demonstrates a novel method for comparative analysis of enter- prise architecture strategies across industries using generative AI, with implications for cross-industry knowledge transfer and benchmarking.
Author: EL HASSANI, Samir
Language: English
Subfield: Management Information Systems
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W6930754767