Role of an AI Strategist

1. Role of an AI Strategist

We discussed how AI could act as a business differentiator, if used effectively. At the heart of this transformation is an AI strategist. In this video, we'll explore how their strategic vision and execution form the backbone of a project's success.

2. Acts as a bridge

As the orchestrator of AI initiatives, an AI strategist bridges the gap between business objectives and AI capabilities.

3. Lead brainstorming sessions

They lead brainstorming sessions that bring business, technology, and AI teams together to identify priorities and challenges, paving the way for discovering new AI initiatives. Each team member holds expertise in their respective domains but may struggle to grasp the practical concerns of AI fully.

4. The orchestrator

This is where an AI strategist comes in, at a critical intersection to bridge this gap. They align the team around the economic value that AI brings to the business. Essentially, they provide a reality check on the potential contribution of AI to the enterprise's digital transformation, focus on AI fundamentals, and filter the hype from the hard facts.

5. Project management

Great, we understand that an AI strategist forms the backbone for a successful transformation. But what skills must they possess? Given the responsibilities of a strategist, it is given that they must have excellent project management skills. It enables them to oversee projects to ensure seamless execution with the right talent and technology aligned with business goals.

6. Domain experts

An AI strategist guides AI transformation in alignment with a business's ambitious vision while fully understanding its capabilities and challenges. They comprehend how AI can strategically foster innovation and enhance long-term competitiveness. Equipped with a lens to gauge value, they can distinguish between good use cases for AI and those with no clear pathway to value. Their expertise lies in devising a phased transformation plan, adeptly balancing quick wins and foundational system building before tackling more complex projects.

7. Well-versed with AI workflows

While they don't necessarily need to be deeply tech-savvy, a nuanced understanding of AI workflows and associated engineering frameworks is essential. It enables them to consider the challenges associated with building, deploying, and integrating AI software into an enterprise's existing tech stack while designing the strategy.

8. Change-agents

But the strategist's role doesn't stop at technology implementation and scaling. They are instrumental in promoting widespread adoption and ensuring the realization of the expected benefits. This responsibility necessitates them to act as change agents, nurturing AI awareness across the organization.

9. Good communicator

Effective communication is key. It spans understanding leadership's vision for AI initiatives, collaborating with technical teams on AI scaling, and simplifying complex topics for non-technical stakeholders.

10. Understands risks that come with AI

AI Strategist is a trusted advisor to business teams to adopt AI amid global competitiveness. With trust comes the need for the ethical and responsible development of AI. It is crucial to consider the potential implications of risks that could arise from the selected use-case of AI to ensure the seamless execution of more innovative products and services.

11. Skills of an AI strategist - an overview

It certainly takes a lot to be an effective AI strategist! To summarize, they must possess excellent project management skills,

12. Skills of an AI strategist - an overview

expertise in business domain,

13. Skills of an AI strategist - an overview

understand AI and technology workflows,

14. Skills of an AI strategist - an overview

be a change-agent,

15. Skills of an AI strategist - an overview

a good communicator,

16. Skills of an AI strategist - an overview

and can foresee risks associated with the selected initiatives.

17. Let's practice!

Let's check our understanding of the skills an AI strategist possesses.

This exercise is part of the course

Artificial Intelligence (AI) Strategy

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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

Current Exercise

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!