CHIEF AI OFFICER

Get the lay of the land, meet your instructor, and see what’s ahead. This session sets you up with everything you need to hit the ground running, from assignments to your final project.

• Instructor intro 
• Course structure
• Assignments & final project overview

See how the Chief AI Officer has reshaped business in 2025. Learn what it takes to align AI with big-picture goals, close the readiness gap, and turn bold tech ambitions into real transformation, backed by a real-world case from MIT.

• Role of the Chief AI Officer in 2025
• Aligning AI with business transformation goals
• Bridging the AI ambition 
• Case Study: MIT NANDA: The GenAI Divide, state of AI in business 2025

Learn how to turn AI from a buzzword into a business driver. You’ll map where AI fits into long-term goals, gauge your organisation’s readiness, and build a clear vision that moves the needle.

• Drivers of AI value
• Mapping AI to long-term KPIs and OKRs
• Enterprise AI readiness: Maturity roadmap
• Frameworks for setting vision across business units

Assignment #1: Measuring AI Success
For the provided case study, consider the stated organisational strategy and undertake a SWOT, mapping the organisation’s readiness to AI readiness and defining candidate KPIs to measure AI’s success.

See what’s really driving the AI boom and what’s just hype. You’ll break down LLMs, GenAI, and Agentic AI, weigh costs against scalability, and learn how smart companies (like Microsoft with Copilot) make build-or-buy calls that move business goals forward.

• Comparing LLMs, GenAI, Agentic AI
• Cost vs. scalability trade-offs
• Buy, configure, build: Decision-making matrix
• Case Study: Microsoft Copilot Integration (2024–2025)

Class 04 is all about turning ideas into action. You’ll learn how to spot the right use cases, weigh risks and ROI, and map out scalable AI solutions, then put it into practise with a hands-on group exercise that turns theory into a ready-to-roll roadmap.

• Use case discovery frameworks
• Use cases vs. core capabilities & patterns 
• Prioritising PoCs by ROI, risk, and repeatability
• Creating scalable deployment blueprints
• Use Case Evaluation Group Exercise: Exploring the provided use cases by their capabilities

Assignment #2: Use Case Plan Implementation
Building on the group exercise, pick one of the discussed use cases and plan for implementation. Consider: is it Buy or Build or Hybrid? Outline risks, develop & operational considerations, and anticipated ROI.

Learn to speak the language of the C-suite. Turn AI insights into clear business value, craft compelling ROI stories, and pitch confidently even to sceptical execs who think AI is just hype.

• Translating AI into commercial outcomes
• The emotional labour of “selling AI” 
• AI as a revenue growth narrative
• Workshop: Executive AI pitch simulation

Lead AI without losing your mind. Learn how to guide cross-functional teams, foster a culture of experimentation, and communicate complex AI ideas to execs, all while keeping your squad motivated and aligned.

• Culture of experimentation & upskilling
• AI product squads vs. center of excellence
• Executive communication for non-tech leaders

Drive AI adoption that sticks. Learn how to roll out tools across teams, empower HR, marketing, and ops, and see it in action through PepsiCo’s 2025 GenAI case study.

• Democratising AI tools inside companies
• Training programmes & onboarding toolkits
• Enabling HR, Marketing, Ops with low-code AI
• Case Study: PepsiCo’s AI Programme (2025)

Learn how to navigate AI responsibly without tripping over the rules. Map AI workflows to GDPR, HIPAA, and global regulations, build feedback and audit systems, and ensure your AI projects stay ethical, compliant, and accountable.

• Mapping AI use to GDPR, HIPAA, and global AI laws
• Principles of Responsible AI
• Observability, feedback, audit trails

Assignment #3: From Pilot to Production
Develop your plan to roll out your chosen AI solution into production. Ensure your plan covers people, process, and technology, considering governance & compliance, measurement & operational considerations, people & change management.

Learn how to build AI products people can trust. Explore fairness, explainability, and governance in real-world roadmaps, and see how AstraZeneca turned responsible AI principles into practical action.

• Model evaluation, safety, security
• Embedding fairness & explainability in AI product roadmaps
• Go-to-market planning with trust & compliance in mind
• AI Governance for responsible scaling of AI systems
• Case Study: AstraZeneca’s Responsible AI Playbook

Connect the dots between your data and AI. Learn how to integrate AI into core business systems, monitor performance, secure pipelines, and keep everything running smoothly, while seeing real-world applications in action with insights from an industry guest.

• Core Business (ERP, CRM) & LOB Application integration
• Monitoring, performance, availability, debugging
• Charge-back, auditing, securing AI systems
• Data & model pipelines 

Assignment 4: Course Project: AI Adoption Plan
Build a comprehensive plan to scale AI adoption across the organisation, including: choice of AI organising principle; key foundational capabilities; key data sources and integration points; critical risks and mitigations; governance, ethics, and security considerations; plan of resources and/or investments.

Turn AI experiments into reliable, repeatable systems. Learn how to scale, deploy, and manage ML and generative AI models in real-world workflows, so your AI projects work when it matters.

• GenAIOps: Enterprise GenAI Lifecycle
• CI/CD for ML & GenAI systems
• Scaling, iterating, model lifecycle management

Navigate the AI vendor jungle. Learn when to buy, configure, or build, manage partnerships and outsourcing, and handle contracts and SLAs with confidence.

• Buy vs. Configure vs. Build: Vendor strategy
• Partnerships, outsourcing, talent augmentation
• Managing AI contracts & SLAs

Learn to align AI decisions with ESG priorities and real-world impact. Assess sustainability, ethics, and business trade-offs through hands-on model evaluation, and practise justifying choices that balance performance, cost, and social responsibility.

• Mapping AI impact to ESG frameworks
• Efficient AI for cost & sustainability
• Content safety 
• Ethics, diversity, AI in social governance
• Workshop: Green AI: Model evaluation decision sprint

Explore what it really takes to thrive as a Chief AI Officer, what boards and CEOs expect, and how to position yourself for the future of AI-driven leadership. Wrap it up by showcasing your project and stepping confidently into the next stage of your career.

• Differences in CAIO scope: Startups vs. enterprises
• What boards & CEOs expect from a CAIO
• Future of the role: Will CAIOs become COOs of AI-driven businesses?
• Project presentations