Measuring Business ROI of Generative AI Adoption on Azure Cloud Platforms

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] ).