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Flagship · 5 Days

Certified Chief AI Officer Programme

A five-day executive immersion for leaders responsible for moving AI from experimentation to enterprise-wide value.

Date6–10 December 2027DeliveryJohannesburg or Live OnlineDesigned forChief AI officers, executives and transformation leaders
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Programme outcomes

What you will be able to do

  • Explain the enterprise AI landscape
  • Design an AI strategy and operating model
  • Establish governance, risk and responsible-AI controls
  • Lead business transformation and adoption
  • Create an implementation roadmap and executive scorecard

Daily agenda

What the programme covers

The agenda below shows the learning journey across each programme day. A detailed session timetable is issued with the joining instructions.

Day 1

Understanding AI and the enterprise opportunity

  • AI foundations and enterprise capabilities
  • Technology landscape, limitations and readiness
  • Enterprise value opportunities and use cases

Morning

AI foundations for senior leaders

Distinguish artificial intelligence, machine learning, generative AI, intelligent automation and AI agents. Explore how modern models learn, generate outputs and support business decisions.

Morning

Capabilities, limitations and technology choices

Assess what AI can and cannot do, including hallucinations, bias, privacy and security concerns. Compare build, buy and partner options without getting lost in technical jargon.

Afternoon

Enterprise use cases and value pools

Identify opportunities across customer experience, operations, finance, people, risk and new business models. Separate high-value transformation from low-value experimentation.

Afternoon

AI readiness diagnostic

Evaluate strategic alignment, data, technology, skills, leadership, governance and change readiness using a practical executive assessment.

Practical workshopMap the organisation's most material AI opportunities and complete an enterprise readiness diagnostic.

Daily outputEnterprise AI opportunity map and baseline readiness assessment.

Day 2

AI strategy, value portfolio and operating model

  • Strategic ambition and portfolio choices
  • Use-case prioritisation and business cases
  • Data, technology and talent foundations
  • AI operating-model design

Morning

Setting the AI ambition

Translate corporate strategy into an AI ambition, strategic themes and measurable outcomes. Examine how AI may improve the current business model or create new sources of value.

Morning

Prioritising the use-case portfolio

Score use cases against business value, feasibility, risk, time to impact and organisational readiness. Balance quick wins, enabling foundations and transformational bets.

Afternoon

Business cases, ROI and investment decisions

Build realistic value cases covering total cost of ownership, productivity, revenue, risk reduction, adoption assumptions and benefits realisation.

Afternoon

Designing the AI operating model

Compare centralised, federated and hybrid models. Define executive sponsorship, decision rights, delivery teams, data and technology foundations, talent needs and partner roles.

Practical workshopDevelop a prioritisation matrix and debate investment choices in an executive portfolio review.

Daily outputAI strategy-on-a-page and prioritised use-case portfolio.

Day 3

AI governance, risk and responsible AI

  • Responsible AI and ethics
  • Risk, controls and executive accountability
  • ISO/IEC 42001 management-system concepts
  • Board reporting and assurance

Morning

Accountability, ethics and material AI risk

Clarify the roles of the board, executive committee and Chief AI Officer. Assess bias, fairness, explainability, privacy, security, intellectual property and workforce impacts.

Morning

ISO/IEC 42001 and governance architecture

Understand the core concepts of an AI management system and how policies, objectives, accountability, impact assessment and continual improvement fit together.

Afternoon

Controls across the AI lifecycle

Design an AI inventory, risk classification, approval gates, human oversight, testing, documentation, monitoring and incident-management process.

Afternoon

Third-party risk, assurance and board reporting

Strengthen vendor due diligence, model and data risk management, audit evidence, escalation thresholds, key risk indicators and decision-ready board reporting.

Practical workshopClassify a high-impact AI use case and design proportionate controls from procurement through monitoring.

Daily outputAI governance charter, risk-control framework and board oversight dashboard.

Day 4

Business transformation, adoption and scale

  • Process and operating-model transformation
  • Workforce, skills and change leadership
  • Scaling pilots and benefits realisation
  • Partner and vendor management

Morning

Redesigning work and operating processes

Move beyond task automation to rethink end-to-end processes, decision flows, customer journeys and the balance between human judgement and machine support.

Morning

Workforce strategy and responsible adoption

Assess role and skills impacts, prepare leaders and teams, establish responsible-use practices and build trust through communication, participation and capability development.

Afternoon

From pilot to production and enterprise scale

Address data quality, integration, architecture, security, AI operations and ownership. Establish criteria for stopping, improving or scaling pilots.

Afternoon

Ecosystem, change and benefits realisation

Manage technology partners and vendors, mobilise stakeholders, track adoption and value, and sustain transformation through clear accountabilities and feedback loops.

Practical workshopRedesign a priority business process and prepare a stakeholder adoption plan for enterprise rollout.

Daily outputAI transformation blueprint, adoption plan and benefits-realisation approach.

Day 5

Implementation roadmap and Chief AI Officer leadership

  • CAIO mandate and executive influence
  • Transformation sequencing and funding
  • Metrics and executive scorecards
  • First 90 days and capstone presentation

Morning

The Chief AI Officer mandate

Define the role, mandate, decision rights and relationships required to align the board, executive team, business units, technology, data, risk and human resources.

Morning

Sequencing and resourcing the roadmap

Convert the strategy into phases covering governance, foundations, pilots, scale and transformation. Assign ownership, dependencies, funding, skills and partner requirements.

Afternoon

Executive scorecard and first 90 days

Select value, adoption, delivery and risk measures; establish reporting rhythms; and develop a focused 30-60-90 day leadership plan.

Afternoon

Capstone: board-ready AI roadmap

Present and defend an organisation-specific implementation roadmap. Receive structured peer and facilitator feedback on value, feasibility, governance and execution.

Practical workshopIntegrate the week's work into a concise executive narrative and implementation plan for board or executive approval.

Daily outputBoard-ready AI implementation roadmap, executive scorecard and 30-60-90 day action plan.