d. Example: “Reducing Scope 3 emissions by optimizing supplier data

From Briefing to Breakthrough: AI for Decision Makers

Introduction: The Urgent Need for AI Strategy

Welcome to the course

PART ONE

Unveiling the power of AI

1. AI today: Its evolution and integration, and the dawn of a new digital age

A New Era of AI Capabilities: Understand how advancements in machine learning, generative AI, and automation are creating exponential change.
The Economic Impact of AI: Explore how AI is projected to add trillions to the global economy and reshape job roles and productivity.
But How Did We Get Here?: Trace the historical breakthroughs that led to today’s AI revolution.
Are We Close to Artificial General Intelligence?: Examine the debate around AGI and whether human-like reasoning is near.
Further Reading
Key Takeaways

2. The positive and negative impacts of AI: A balanced perspective

a. Using AI for good
b. But there are risks…
c. Will AI help or hinder the fight against climate change?
d. Impact on jobs
e. Tackling (or exacerbating?) inequality
f. The polarization problem
g. Combatting bias
h. Cyberattacks and terrorism
i. Regulation
j. Further reading
k. Key takeaways
l. Notes

3. Why every business is now an AI business: Embracing the inevitable transformation

a. What do we mean by an ‘AI business’
b. Smart everything, AI everywhere
c. Business processes are also becoming smarter
d. Key takeaways
e. Notes

PART TWO

Crafting your AI blueprint

4. Business alignment: Rethinking strategies for the AI revolution

a. Why AI may impact your business strategy and model
b. When AI challenges existing, successful business models and strategies
c. Companies that have evolved their business model and reinvented themselves
d. Companies that have changed their business model
e. Key takeaways
f. Notes

5. Your use cases: From quick wins to strategic triumphs

a. What’s involved?
b. Pinpointing potential use cases (according to your business strategy)
c. Defining your use cases in more detail
d. Prioritizing your use cases
e. Cascading this approach through the business
f. Further reading
g. Key takeaways

6. The need for scalability: Building AI for tomorrow, today

a. What is AI scalability and why does it matter?
b. How to overcome the main scalability roadblocks
c. A final pep talk
d. Key takeaways
e. Notes

7. Data management: The keystone of AI success

a. Data is the lifeblood of AI
b. Structured vs unstructured data
c. Internal vs external data
d. Satellite data
e. Synthetic data
f. The shelf life of data
g. Further reading
h. Key takeaways
I. Notes

8. Technology stack: Architecting the future

a. What technology infrastructure do you need for AI?
b. Updating legacy systems
c. On-premises versus cloud versus hybrid
d. Should you go for open-source or closed-source solutions?
d. Should you go for open-source or closed-source solutions?
e. Key takeaways
f. Notes

9. Talent and skills development: Cultivating an AI-ready workforce

a. Why is addressing the skills gap so important?
b. The future skills every organisation needs
c. Creating a culture of continuous learning
d. Other ways to access talent
e. A final pep talk
f. Further reading
g. Key takeaways
h. Notes

10. Vendor and partner ecosystems: Leveraging collective expertise

a. The many benefits of partnerships
b. What’s the ‘right’ way to partner?
c. Evaluating potential partners
d. Managing partner relationships
e. Key takeaways
f. Notes

11. Ethical and responsible AI: Guiding principles for trustworthy technology

a. What are the biggest challenges around AI?
b. AI for good: The positive impact
c. Building trust through transparency and explainability
e. Key takeaways
d. Practical steps for organisations
f. Notes

12. Compliance and security: Fortifying your AI endeavours

a. The biggest security risks for AI
b. Regulation is increasing
c. AI security and compliance measures
d. Practical steps for business leaders
e. Key takeaways
f. Notes

13. Sustainability: AI for a greener tomorrow

a. Energy consumption and carbon footprint
b. AI and water usage
c. Resource depletion
d. The mounting problem of e-waste
e. Indirect environmental impacts
f. What should organisations do to mitigate these impacts?
g. Key takeaways
h. Notes

14. Risk management: Safeguarding your AI journey

a. A recap of the biggest AI risks
b. Managing AI risks
c. It’s all about de-risking AI
d. Key takeaways
e. Notes

15. Delivery and change management: Navigating the AI transformation

a. How the best-intended AI projects can fall short on delivery
b. Your change management plan: Critical elements for managing change
c. Key takeaways
d. Note

16. Measurement and evaluation: Defining success in the AI era

a. Establishing the right metrics, key performance indicators and benchmarks
b. Building a culture of regular reporting, ongoing assessment and iterative improvement
c. Real-world examples of measuring AI performance
d. Further reading
e. Key takeaways
f. Notes

17. Culture and mindset: Cultivating innovation and adaptability

a. Culture and mindset: Why you need both
b. What all this means for organisational structure
c. How to implement the right culture and mindset
d. Further reading
e. Key takeaways
f. Notes

PART THREE

AI strategy in action: Real-world examples from the front lines

18. Real-world AI use cases: Identifying overlaps and overarching themes

a. AI strategy at a national level
b. The National Health Service: Transforming healthcare through AI
c. NextGen Biotech: Using AI to drive innovation
d. Shell’s AI strategy: Transforming an energy giant
e. IKEA’s comprehensive AI strategy: Enhancing the customer experience
f. EduTech Innovations: An AI-driven learning transformation
g. Key takeaways
h. Notes

19. Real-world examples from business functions

Case Study One
Case Study Two
Case Study Three
Case Study Four
Case Study Five

19. Real-world examples from business functions

a. Marketing: Personalizing customer experiences at NextGen Biotech
b. Human resources: Optimizing talent management in a major US telecom company
c. Finance: Enhancing financial operations and risk management in a European insurance company
d. Information technology: Driving digital transformation in an Asian fintech start-up
e. Key takeaways

PART FOUR

Envisioning the future with AI

20. Top tips: Key enablers of AI success

a. Have a strategic roadmap
b. Foster a data-driven culture
c. Invest in talent development
d. Embrace ethical AI principles and governance
e. Foster a culture of innovation
f. Build strong partnerships
g. Prioritize user experience
h. Leverage cloud technologies
i. Lead by example and think big
j. Stay informed about AI advancements
k. Key takeaways

21. A look into the future: Beyond the horizon

a. Where is all this heading?
b. Major AI trends to watch
c. Where is AI likely to make the biggest difference?
d. What we really need to get a handle on
e. Will AI ultimately change what it means to be human?
f. The future is human + AI
g. Connect with me
h. Note

PART FIVE:

Capstone Project

22. Certified Capstone Project: Strategic AI Rollout Plan

a. Objective
b. Demonstrate your ability to design and lead a strategic AI initiative within your organization (or a hypothetical one), using the frameworks, tools, and insights from the course.
c. Project Deliverables

23. Problem Definition & Business Case (10%)

a. Identify a business problem or opportunity within your department or company.
b. Explain why AI is a fit for solving it.
c. Define success metrics (e.g., cost savings, efficiency, carbon reduction, risk mitigation).
d. Example: “Reducing Scope 3 emissions by optimizing supplier data and predicting ESG risks using AI.”

24. AI Use Case Design (15%)

a. Detail the AI solution (e.g., predictive analytics, NLP, GenAI for process automation).
b. Align the use case with strategic goals.
c. Describe potential impact on workflows, customers, and stakeholders.

25. Data & Infrastructure Assessment (15%)

a. Assess current data availability, quality, and gaps.
b. Identify required data sources (internal/external).
c. Explain what needs to change (e.g., data governance, integrations).
d. Use the “AI Fuel Audit” checklist provided during the course.

26. Stakeholder & Capability Map (10%)

a. Identify key roles (sponsors, users, data teams, risk/compliance).
b. Identify required data sources (internal/external).
c. Plan for stakeholder buy-in and education.

27. Risk & Ethics Analysis (15%)

a. Identify key ethical, regulatory, or reputational risks.
b. Propose mitigations (e.g., explainability, bias checks, audit trails).
c. Align with internal values (e.g., sustainability, DEI, transparency).
d. Implementation Roadmap (20%)
e. Outline a 6–12 month rollout plan.
f. Include milestones, resourcing, budget estimates, and success metrics.
g. Address change management and communication strategy.

28. Executive Summary & Presentation (15%)

a. Create a 1-page executive summary of your proposal.
b. Present your capstone as if to your board or executive team (video or slides with audio narration).
c. Focus on clarity, persuasion, and business impact.

Evaluation and Certification

Evaluation Criteria
Certification Requirements

d. Example: “Reducing Scope 3 emissions by optimizing supplier data and predicting ESG risks using AI.”

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