Measuring Business ROI of Generative AI Adoption on Azure Cloud Platforms

Measuring Business ROI of Generative AI Adoption on Azure Cloud Platforms | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Measuring Business ROI of Generative AI Adoption on Azure Cloud Platforms Rahul Modi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9033610/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The fast business usage of Generative Artificial Intelligence (GenAI) has made hyperscale cloud platforms a key facilitator of AI-driven change, and Microsoft Azure has become one of the first enterprise-scale platforms. This paper will perform both empirical and theoretical analyses of the business Return on Investment (ROI) of GenAI implementation on Azure cloud computing platforms. The research design is a mixed-method study that combines both quantitative ROI modelling and cost-benefit analysis, as well as qualitative synthesis of secondary enterprise case studies and architectural analysis of the Azure-native GenAI services. Results have shown that the measurable ROI is mainly pushed by the improvement in productivity, optimization of operational costs, faster decision making and increased speed of innovation among business functions. The analysis also shows that close coupling among Azure OpenAI Service, Azure Machine Learning, and cost governance tooling geared towards FinOps will significantly decrease the overall cost of ownership and enhance scalability and compliance. Governmental structures, labor supply and demand, and incorporation of financial measures prove as key intervening variables in achieved ROI. The paper finds that GenAI implementations that are implemented strategically in the managed cloud infrastructure of Azure provide a positive ROI over time in cases when they are consistent with the business processes, enterprise architecture, and performance metrics. The results are added to the expanding literature on the rationale of cloud-based GenAI as a source of value creation in an enterprise and not an experimental technology ([1]; [2]; [3] ). Generative AI Business ROI Enterprise AI Adoption Azure Cloud Economics AI Governance FinOps Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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