Azure OpenAI Service for Cloud Native Applications

Chapter 6. Elaborating Generative AI Business Cases

The first five chapters of the book focused on technical aspects related to cloud native architectures for generative AI, advanced capabilities with Azure OpenAI and other Azure services, and the operationalization of generative AI in the enterprise, including topics such as LLMOps and responsible AI. In Chapter 3, we even explored detailed technical approaches that leverage different Azure resources, with recommendations depending on the project scope and type of company data.

One of the main motivations for companies to adopt Azure OpenAI, and LLMs in general, is to generate significant advantages in the form of savings by automating language-based scenarios, or to create differentiation, to offer something better than their competitors, with the potential for increased revenue.

In this chapter, we will focus on the business considerations of building a generative AI project with Azure OpenAI Service, including project planning and evaluation topics such as cost scenarios and estimations, ROI, roadmapping, etc. We will cover the key aspects that will allow any technical implementation to become a sustainable and feasible generative AI initiative.

Premortem, or What to Consider Before Implementing a Generative AI Project

One of the most interesting managerial techniques is the premortem. A bit less known than the postmortem (in which we analyze a project after we have finished it), the premortem is done before starting the ...