{"title":"Scaling Generative AI in Enterprise IT Operations: Challenges and Opportunities","authors":"Sriram Sagi","doi":"10.47363/jaicc/2024(3)209","DOIUrl":null,"url":null,"abstract":"This research paper delves into industries that can benefit from the implementation of AI, such, as IT operations, healthcare, finance and gaming. The potential of AI lies in its ability to enhance customer service create personalized content assist in research endeavors optimize investment strategies and create virtual environments. However businesses often encounter challenges when moving from AI proof of concept to implementation. These challenges involve scalability, integration with existing systems, data governance considerations aligning objectives and meeting requirements. To tackle these concerns head on this paper suggests the utilization of a converged infrastructure platform that combines computing power with network capabilities and storage resources using NVIDIA GPUs. It recommends evaluating existing AI proof of concepts and providing the infrastructure to transform each concept into an use case. The primary goal of this research study is to explore the process involved in converting AI proof of concepts into use cases while understanding its significance in harnessing AI capabilities, within organizations.","PeriodicalId":407351,"journal":{"name":"Journal of Artificial Intelligence & Cloud Computing","volume":"7 5","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-02-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Artificial Intelligence & Cloud Computing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.47363/jaicc/2024(3)209","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
This research paper delves into industries that can benefit from the implementation of AI, such, as IT operations, healthcare, finance and gaming. The potential of AI lies in its ability to enhance customer service create personalized content assist in research endeavors optimize investment strategies and create virtual environments. However businesses often encounter challenges when moving from AI proof of concept to implementation. These challenges involve scalability, integration with existing systems, data governance considerations aligning objectives and meeting requirements. To tackle these concerns head on this paper suggests the utilization of a converged infrastructure platform that combines computing power with network capabilities and storage resources using NVIDIA GPUs. It recommends evaluating existing AI proof of concepts and providing the infrastructure to transform each concept into an use case. The primary goal of this research study is to explore the process involved in converting AI proof of concepts into use cases while understanding its significance in harnessing AI capabilities, within organizations.
本研究论文深入探讨了可从实施人工智能中受益的行业,如 IT 运营、医疗保健、金融和游戏。人工智能的潜力在于它能够提高客户服务水平,创建个性化内容,协助研究工作,优化投资策略和创建虚拟环境。然而,企业在从人工智能概念验证到实施的过程中往往会遇到挑战。这些挑战涉及可扩展性、与现有系统的集成、数据管理考虑因素、目标一致性和满足要求。为了从根本上解决这些问题,本文建议利用英伟达™(NVIDIA®)GPU 融合基础架构平台,将计算能力、网络功能和存储资源结合起来。它建议对现有的人工智能概念验证进行评估,并提供将每个概念转化为使用案例的基础设施。本研究的主要目标是探索将人工智能概念验证转化为使用案例的过程,同时了解其在组织内部利用人工智能能力的意义。