Operationalizing Data & AI Strategy

This capstone unit guides learners through deploying, governing, and scaling Data & AI initiatives—translating strategy into operational plans, budgets, and governance for foundation models and generative AI. It covers cost and infrastructure assessment, risk and vendor management, monitoring and maintenance, stakeholder pitching, hiring, and ethical safeguards to prepare students for real-world implementation and continual improvement.

Learning Objectives

• Analyze infrastructure, compute, and vendor options to produce a justified cost estimate and ROI-based recommendation for deploying foundation models and retrieval-augmented generation solutions
• Evaluate project risks and design a comprehensive risk management and monitoring plan that includes contingency strategies, ethical and societal safeguards, and measurable KPIs for operational oversight
• Apply agile and DevOps principles to construct a phased implementation and maintenance roadmap that specifies roles, hiring recommendations, vendor management actions, tools, and timelines
• Demonstrate persuasive stakeholder engagement by creating and delivering a concise project pitch and strategic wrap-up that communicates selected use cases, expected business impact, resource needs, and governance commitments