Artificial Intelligence (AI) Strategy

Overview

Learn how to blend business, data, and AI, and set goals to drive success with an effectively scalable AI Strategy.

Unlock the power of AI strategy in the data-driven world. This course delves into the interconnectedness of business, data, and AI strategies, equipping you with the knowledge to create a strong strategic framework. You'll learn to differentiate AI from traditional software, set realistic business goals, and assess ROI for AI projects. Discover the key components of a successful AI strategy, emphasizing innovation and risk assessment. Finally, explore efficient AI scaling and the role of executive sponsors and champions in driving AI adoption. Get ready to navigate the AI landscape with confidence.

Syllabus

Fundamentals of AI Strategy

The chapter underpins the intricate relationships between business, data, and AI strategies. It then goes deeper into how an effective AI strategy begins with a clear vision and the role of a focused action plan in driving an organization's strategic objectives.

You will also learn the skills that go into making a successful AI strategist, outlining their responsibilities and contributions towards achieving the business goals.

Designing a Winning AI Strategy

This chapter sharpens the business acumen by distinguishing AI software from traditional software, ensuring the effective use of resources for pertinent business challenges. It further explains the key business drivers in identifying the most impactful AI initiatives and shares how to set the right AI goals.
Alongside explaining the significance of ROI, learners will understand the challenges and drivers of assessing ROI.

Components of AI Strategy

This chapter explains different components of a successful AI strategy, such as innovation and building the right culture for high-performing teams. It also underscores the importance of AI literacy, covering the pivotal do’s and don’ts of AI usage. While innovation is essential, understanding the potential AI-associated risks and asking the right questions is crucial to building a robust risk assessment framework for AI.

Time for Action

In this chapter, we discuss the role of feasibility workshops and emphasize initiating a focused PoC to gauge AI's potential before a full-scale rollout. We will also highlight what it takes to build scalable AI systems and the significance of MLOps in scaling it right.

Ultimately, the chapter underscores the influence of executive sponsors and AI champions in fostering AI adoption.