The three preceding modules have built the complete theoretical and practical foundation for a personal AI practice. Module 4.1 addressed the information layer: how to organise files, construct context documents, and maintain the knowledge base that gives AI tools the grounding they need to produce relevant and accurate outputs. Module 4.2 addressed the model layer: how to understand the AI landscape, distinguish between the structural categories of models available, and select the tool most appropriate for a given professional task. Module 4.3 addressed the integration layer: how to connect AI tools to the platforms through which professional work is conducted, which connections are worth building, and how to maintain them with the governance discipline that professional responsibility requires.
What these three modules have not yet done is show what all of this looks like in practice, assembled into a coherent working system by a specific professional with a specific role, specific tools, and specific workflows that must be completed to a professional standard every week. Theory and principle are necessary foundations, but they do not, on their own, answer the question that most professionals ask when they have absorbed the conceptual material: what should my actual setup look like, and where do I start?
This module answers that question. It does so through five complete role-specific walkthroughs, each of which follows a professional as they build and operate the full personal AI practice described across Modules 4.1 through 4.3. The five roles are a management consultant, a paralegal, a claims analyst, a financial analyst, and an operations manager. Each walkthrough is grounded in the actual work of that role: the documents they produce, the tools they use, the workflows they repeat, the compliance obligations they carry, and the failure modes that matter most in their specific professional context. Each walkthrough moves from the professional's situation before any AI practice is in place, through the construction of their knowledge base, model selection, and integration choices, to the three core workflows they operate with AI assistance, the quality control practices they apply, and the common mistakes specific to their role that they must understand and avoid.
The walkthroughs are not aspirational descriptions of a perfect AI practice. They are grounded portraits of what a well-built personal AI practice actually looks like in each role, including its limitations, its verification requirements, and the boundaries where AI assistance stops and professional judgment takes over completely. The goal of this module is not to produce professionals who have memorised five role descriptions, but to give every professional reading this material a concrete and sufficiently detailed model they can adapt to their own role, their own tools, and their own working context, building from a starting point that has been designed with their professional domain in mind.