It's time to wrap-up!

1. It's time to wrap-up!

Congratulations! You've completed this comprehensive course on AI Strategy.

2. The trifecta

We have covered quite a lot, starting from how AI strategy aligns with the business and data strategy.

3. AI strategist as an orchestrator

We then revisited what makes a good strategy and how an AI strategist can help put that strategy in place through its role as an orchestrator of the entire AI lifecycle.

4. Projects that justify ROI

Delving deeper, we explored how AI differs from traditional software and discussed the characteristics of business problems that warrant AI technology. Understanding the cost and return dynamics is central to AI strategy. As each organization would have a different approach to deriving the returns, we have discussed the factors that help build a lens to assess the ROI.

5. Innovation culture

By now, we understand that AI projects are inherently iterative and exploratory. Hence, it is important to highlight the role of promoting an innovation culture that allows the teams to experiment without fearing any negative consequences.

6. Getting the components together to scale

Having discussed the role of culture, high-performing teams, and data availability, we underscored the risks associated with AI, including privacy issues and ethical concerns. Finally, we checked the idea's feasibility by conducting a PoC that gives a glimpse of what type of challenges could arise during scaling. We also learned how MLOps is key to scaling AI projects efficiently and effectively. While your business is uniquely placed to leverage AI, I hope the framework shared in this course helps you design your winning AI strategy.

7. Congratulations!

Congratulations!

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This exercise is part of the course

Artificial Intelligence (AI) Strategy

BeginnerSkill Level

4.8+

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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.

Exercise 1: Different types of strategies Exercise 2: Charting the course Exercise 3: Which strategy?Exercise 4: Pillars of an Effective AI Strategy Exercise 5: Navigating the strategic water Exercise 6: The pillars of AI strategy Exercise 7: Role of an AI Strategist Exercise 8: Onboarding an AI strategist Exercise 9: A successful AI strategist

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.

Exercise 1: When should one think of AI?Exercise 2: Adapting to the AI revolution Exercise 3: Understanding software paradigms Exercise 4: Uncovering core business drivers Exercise 5: Foundations of a successful AI plan Exercise 6: AI suitability test Exercise 7: Distinguishing AI-friendly scenarios Exercise 8: Setting realistic business goals Exercise 9: AI goals with the SMART lens Exercise 10: A test of time and ambition Exercise 11: Assessing the ROI of an AI initiative Exercise 12: AI's dual-faced ROI Exercise 13: Tracing revenue roots

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.

Exercise 1: Building an AI culture Exercise 2: Diverse AI attempts Exercise 3: Exploration & experimentation Exercise 4: Building high-performing AI teams Exercise 5: Finding the gap Exercise 6: The essential trio Exercise 7: Getting the right data Exercise 8: Getting the right mix Exercise 9: Quality saves cost Exercise 10: AI risk assessment Exercise 11: Privacy and ethics Exercise 12: Compliance with regulations Exercise 13: Compliance Exercise 14: AI project lifecycle

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.

Exercise 1: It all starts with a PoC Exercise 2: Decision points for AI diagnostic tool Exercise 3: Evaluating essentials of an AI initiative Exercise 4: Scaling beyond PoCs Exercise 5: Sorting AI scaling obstacles Exercise 6: Scaling an AI system Exercise 7: MLOps Exercise 8: Navigating MLOps Exercise 9: Sorting architectural practices Exercise 10: Barriers to adoption Exercise 11: Assigning roles in AI transformation Exercise 12: Blueprint for AI transformation Exercise 13: It's time to wrap-up!

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