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 plan8. Building an effective AI strategy
starting with small-scale implementation9. 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
GPUs16. Resources
AI platforms and tools17. 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 developer24. 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.Create Your Free Account
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