Enterprise NLP: A Review of Technologies and Integration Challenges

Enterprise NLP: A Review of Technologies and Integration Challenges | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 9 September 2025 V1 Latest version Share on Enterprise NLP: A Review of Technologies and Integration Challenges Authors : Mahade Hasan 0009-0006-2778-1216 [email protected] , Farhana Yasmin , and Radjabov Sujhrob Authors Info & Affiliations https://doi.org/10.22541/au.175743430.04854635/v1 448 views 182 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Natural Language Processing (NLP) has rapidly evolved into a core enabler of enterprise digital transformation, empowering organizations to automate workflows, extract insights from unstructured text, and enhance data-driven decisionmaking processes. This paper presents a comprehensive survey of current NLP technologies, emphasizing their practical integration within enterprise environments. We review the development of large-scale pre-trained models such as BERT, GPT-3, and domain-specific variants, highlighting their capabilities in applications including sentiment analysis, intelligent document processing, regulatory compliance monitoring, customer service automation, and risk assessment across sectors such as healthcare, finance, and retail. Key deployment considerations such as cloud infrastructure, container orchestration, data pipelines, and governance strategies are critically analyzed to guide scalable and secure implementation. Furthermore, we synthesize existing costaware ROI evaluation approaches to assess the financial viability and business value of NLP deployments, integrating performance metrics, operational costs, and strategic benefits. Real-world industrial case studies are discussed, demonstrating best practices and challenges in deploying NLP systems at scale, including issues of model drift, explainability, data privacy compliance, and integration with enterprise IT ecosystems. Additionally, we explore emerging trends such as multilingual adaptation, voiceenabled NLP, and environmentally sustainable AI deployment strategies. This survey aims to provide researchers, practitioners, and decision-makers with a structured understanding of how NLP technologies can be effectively leveraged to deliver measurable business outcomes, enhance organizational efficiency, and drive strategic innovation in real-world enterprise settings. This paper reviews existing ROI evaluation approaches and deployment strategies for NLP in enterprises. Supplementary Material File (enterprise nlp a review of technologies and integration challenges.pdf) Download 7.37 MB Information & Authors Information Version history V1 Version 1 09 September 2025 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords enterprise applications intelligent document processing llm multilingual nlp nlp model Authors Affiliations Mahade Hasan 0009-0006-2778-1216 [email protected] School of Software, Nanjing University of Information Science and Technology View all articles by this author Farhana Yasmin School of Computer Science, Nanjing University of Information Science and Technology View all articles by this author Radjabov Sujhrob School of Software, Nanjing University of Information Science and Technology View all articles by this author Metrics & Citations Metrics Article Usage 448 views 182 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Mahade Hasan, Farhana Yasmin, Radjabov Sujhrob. Enterprise NLP: A Review of Technologies and Integration Challenges. Authorea . 09 September 2025. DOI: https://doi.org/10.22541/au.175743430.04854635/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . 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