Pillars of an Effective AI Strategy

1. Pillars of an Effective AI Strategy

Great, so we have learned how the three strategies relate. This lesson will deepen our understanding of the strategy pyramid by extending it to AI strategy.

2. Time for AI strategy

Andrew Ng, a global AI leader, emphasized the importance of an early focus on AI strategy, drawing a parallel to the regret expressed by S&P 500 CEOs for not strategizing their internet presence sooner.

3. Time for AI strategy

But, much like business strategy, there is no universal playbook for AI strategy. Instead, it's a unique journey shaped by choices and decisions that drive business ambitions.

4. Building an effective AI strategy

Let us learn how an effective strategy begins from the vision and covers the focused action plan in six steps. The first is to formulate a vision,

5. Building an effective AI strategy

then assess the current state,

6. Building an effective AI strategy

based on which identify goals and objectives,

7. Building an effective AI strategy

and start putting up an action plan

8. Building an effective AI strategy

starting with small-scale implementation

9. Building an effective AI strategy

and lastly, review and adjust.

10. Formulate a vision

Let's start with an organization's vision which harnesses current and emerging trends to adapt, steer, and reflect the long-term growth trajectory towards an envisaged future. Consider how Microsoft evolved from its initial vision of having "a computer on every desk and in every home" to now democratizing AI — "enabling every company to transform by bringing AI to every application, every business process, and every employee."

11. Assess the current state

While it is good to have an ambitious vision, it must be achievable. This requires a thorough evaluation of existing technology and processes, data availability, skills, and, importantly, the presence of an AI-promoting culture.

12. Goals and objectives

Once the gaps are identified, the strategy team develops an action plan. For instance, if the organization's goal is to improve customer service, but it lacks automated customer support, a high-value initiative could be implementing AI chatbots.

13. Putting up an action plan

With business goals set, it's time to segment them into manageable sub-goals related to data preparation, model development, integration, and more, detailing the steps to achieve them. This strategy focuses on specifics including three R’s - resources, responsibilities, and reporting, and considerations such as hiring vs. training, build vs. buy, and external partnerships.

14. Resources

Different types of resources include infrastructure such as servers,

15. Resources

GPUs

16. Resources

AI platforms and tools

17. Resources

data storage and processing,

18. Resources

funding to sponsor the initiative, and time investment. As Brian Tracy puts it - “There are no unrealistic goals, only unrealistic deadlines”.

19. Responsibilities

Next is team responsibility, which includes AI strategist,

20. Responsibilities

business analyst,

21. Responsibilities

data engineer,

22. Responsibilities

data analyst,

23. Responsibilities

model developer

24. Responsibilities

and MLOps engineer.

25. Reporting

Great, so we have the right resources and team. It is time to track progress to ensure that the strategy yields desired results. Reporting is done using Key Performance Indicators, or KPIs, that focus on specific metrics, such as reduced customer response time or Objectives and Key Results, or OKRs, to reduce response time by 50% using an AI chatbot. Besides project updates, periodic meetings manage unforeseen risks and challenges to adjust the plan. Keeping important stakeholders informed is crucial and can be done through dashboards or project management tools.

26. Implementation

After aligning AI initiatives with business goals and establishing KPIs, it's action time. Start with a proof of concept, or PoC, to gather learnings and tweak the plan before a broader scale implementation.

27. Review and adjust

AI projects, by nature, involve unanticipated hurdles, such as subpar data quality, ineffective models, or technical constraints. Regular progress reviews are vital, allowing for necessary adjustments to the plan while keeping stakeholders informed.

28. End-user adoption

Following a successful PoC, the full-scale AI initiative kicks off. But true success lies not just in deployment but in achieving end-user adoption. This involves fostering an AI-aware culture rather than AI-first - not all problems need AI solutions, but we must know which ones do.

29. Let's practice!

We learned how a well-aligned AI strategy can accelerate the digital transformation journey. Let's build a strategy for a healthcare company.

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