CHIEF AI OFFICER

Get oriented fast, meet your instructor, understand the course flow, and know exactly what you're building by the end.

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

Understand what a modern CAIO does and how to turn AI ambition into focused, business-driven execution.

• Chief AI Officer role in 2026
• Job descriptions walkthrough
• Aligning AI with business transformation goals
• Bridging the AI ambition readiness-gap
• Case study: How EY navigated its AI-driven transformation

Learn to set a clear AI vision, tie it to business outcomes, and assess whether your organisation is ready to deliver on it.

• Leadership principles: how decisions are made
• Mapping AI to long-term KPIs & OKRs
• Enterprise AI readiness audit
• Frameworks for setting vision across business units
• Leading through change

Assignment #1: SWOT & KPIs
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.

Cut through the hype, compare AI technologies, make smart build vs. buy decisions, and plan for real-world limitations.

• Comparing LLMs, GenAI, Agentic AI, and multimodal/reasoning models
• Cost vs. scalability trade-offs in 2026
• Vendor vs. build: updated decision-making matrix
• Case study: AI transformation for consumer brands: Madison Reed

Move from ideas to action: identify high-impact AI use cases and turn them into scalable, ROI-driven roadmaps.

• Use case discovery frameworks
• Aligning business goals to pilots
• Prioritising PoCs by ROI, risk, feasibility
• Creating scalable deployment blueprints
• Structured ideation, canvases, hackathons

Assignment #2: AI Opportunity Matrix
Using structured ideation and canvases, build a 2x2 portfolio matrix for AI ideas, placing high-value and high-feasibility initiatives in the top right. Consider data availability, tech maturity, and organisational change readiness.

Translate AI into business language: learn how to pitch value, prove ROI, and get leadership buy-in.

• Translating AI into commercial outcomes & ROI narratives
• Positioning AI as a revenue growth & efficiency driver
• Workshop: Executive AI pitch simulation

Build and lead AI teams that actually ship: align talent, workflows, and communication across functions.

• Culture of experimentation & continuous upskilling
• AI product squads vs. centre of excellence models
• Executive communication tailored for non-technical leaders
• Demo: Vibe coding and building an agent

Assignment #3: AI Data Augmentor
Given a spreadsheet of company names, build an agent that augments the spreadsheet with rich data (location, phone number, website).

Take AI beyond pilots: learn how to enable teams across the business to adopt and use AI effectively.

• Democratising AI tools enterprise-wide
• Designing training programmes & onboarding toolkits
• Enabling non-technical functions such as HR, Marketing, Ops
• Case study: PepsiCo's AI programme

Stay compliant and in control: design governance frameworks that balance innovation with risk.

• Mapping AI use to GDPR, HIPAA, global AI laws & regulations
• Principles of Responsible AI
• Evaluating frameworks: AWS, NIST, OECD
• Policy-as-code & audit trails
• Integrating ESG into AI governance

Assignment #4: AI Governance Framework
Develop a high-level AI governance policy outline for a fictional organisation, including risk classification, responsible AI checklist, and alignment with EU AI Act obligations for general-purpose models.

Build AI people trust: learn how to embed ethics, transparency, and safety into every stage of development.

• Embedding fairness & explainability in AI product roadmaps
• Go-to-market planning with trust & compliance in mind
• Checklists & playbooks for responsible scaling of AI systems
• Case study: Spring Health: designing AI tools

Set up the backbone: ensure your systems, data, and architecture can support AI at scale.

• Cloud-native AI architecture & hyperscaler options
• Integrating AI with ERP, CRM, cybersecurity, legacy systems
• Building robust data pipelines & model observability

Assignment #6: Course Project: AI Adoption Plan
Develop a comprehensive AI adoption plan that ensures long-term success and integration of AI solutions within the organisation's operations.

Learn how to operationalise AI: manage models end-to-end, from deployment to monitoring and continuous improvement.

• End-to-end lifecycle automation for ML & GenAI
• CI/CD pipelines for models & agents with Git
• Model Context Protocol, monitoring, rollback, versioning
• Observability in production: drift detection, performance tracking

Understand how to choose the right partners: structure deals that scale, save costs, and reduce risk.

• Buy vs. build vs. partner: vendor evaluation strategy
• Managing partnerships, outsourcing, talent augmentation
• AI contracts, SLAs, billing alerts, adoption frameworks
• Case study: Unilever's GenAI procurement model

Assignment #5: AI-Enhanced Migration Request for Proposal
Produce a sample RFP to migrate from a legacy database to a modern cloud-based database. Evaluate respondents on their effective use of AI to reduce cost, time, and risk.

Balance performance with responsibility: make AI decisions that align with sustainability and stakeholder expectations.

• Mapping AI impact to ESG frameworks
• Green AI & sustainability
• Ethics, diversity, AI in social governance
• Workshop: Green AI decision sprint

Position yourself for the role: understand what it takes to become and succeed as a CAIO in today's market.

• Differences in CAIO scope: startups vs. enterprises vs. public sector
• What boards & CEOs expect from a CAIO in 2026
• Future of the role: will CAIOs become COOs of AI-driven businesses?
• Professional associations, certifications, networking, mentorship