About This Course
Artificial intelligence is no longer a future concern for boards—it is a present-day governance imperative. From algorithmic decision-making and data privacy to workforce disruption and reputational risk, AI is reshaping the landscape in which every organisation operates. Yet many boards lack the foundational literacy to scrutinise AI strategies, challenge executive proposals, or set meaningful guardrails. This gap between the boardroom and the algorithm is one of the most pressing governance challenges of our era.
Board-Level AI Literacy: Governing the AI-Powered Enterprise is a five-day intensive programme designed specifically for directors, trustees, commissioners, and C-suite executives who must govern organisations deploying or considering AI. The course does not aim to turn leaders into data scientists. Instead, it builds a structured understanding of what AI is, what it can and cannot do, how it creates value and introduces risk, and what robust governance looks like in practice.
Participants will explore real-world AI governance failures and successes, examine emerging regulatory frameworks across jurisdictions, and work through board-level tools including AI risk registers, ethics checklists, and board committee structures tailored for AI oversight. By the end of the programme, delegates will be equipped to engage productively with technical teams, hold management accountable for responsible AI use, and champion governance practices that protect stakeholders and sustain organisational trust.
TrainingComplex has designed this programme to be immediately practical. Case studies are drawn from sectors including financial services, healthcare, public administration, retail, and infrastructure, ensuring relevance across industries. The course is deliberately non-technical in tone while remaining intellectually rigorous—giving leaders the confidence to govern AI without needing to write a line of code.
Who Should Attend
- Board directors and non-executive directors seeking AI governance competency
- Chief Executive Officers and Managing Directors overseeing AI adoption
- Chief Risk Officers and Chief Compliance Officers responsible for AI-related risk
- Chief Financial Officers evaluating AI investments and financial exposures
- Company secretaries and board secretaries supporting AI governance processes
- Trustees of public bodies, NGOs, and development organisations using AI tools
- Senior civil servants and policy advisors engaged in AI regulation or procurement
- Audit and risk committee chairs requiring AI literacy for effective oversight
- General counsel and legal advisors supporting boards on AI liability and compliance
- C-suite executives preparing board-level AI policies or strategies
Learning Objectives
- Explain core AI concepts—including machine learning, generative AI, and automation—in plain language relevant to board-level decision-making
- Distinguish between AI hype and proven capability to evaluate management proposals critically and accurately
- Assess the strategic opportunities AI presents across industries and understand how to govern innovation responsibly
- Identify the principal risks AI introduces—including bias, opacity, cybersecurity, and reputational harm—and apply structured board-level risk frameworks
- Evaluate ethical dimensions of AI deployment, including fairness, accountability, transparency, and human rights implications
- Interpret and apply emerging AI regulatory requirements across key jurisdictions, including the EU AI Act and equivalent frameworks
- Design or strengthen board structures and committee mandates for effective AI oversight
- Challenge and engage productively with technical teams, asking the right questions during AI investment and deployment decisions
- Apply board-level AI governance tools including AI risk registers, AI ethics checklists, and AI-specific board reporting templates
- Develop an organisational AI governance policy and present a board-level AI oversight roadmap
Course Modules
Module 1: Understanding AI: What Every Board Member Must Know
- Demystifying artificial intelligence—key terms and concepts without the jargon
- Types of AI: narrow AI, generative AI, machine learning, and large language models
- How AI systems learn, make decisions, and produce outputs
- The difference between AI, automation, and data analytics
- A brief history of AI development and why governance urgency has accelerated
- Common misconceptions boards hold about AI capabilities and limitations
Module 2: The AI Opportunity Landscape: Strategic Value Across Sectors
- How AI is creating competitive advantage in financial services, healthcare, retail, and public services
- Revenue growth, cost efficiency, and service improvement: mapping AI's value drivers
- Evaluating management's AI business case—questions every director should ask
- AI and organisational transformation: workforce, culture, and operating model implications
- Distinguishing proven use cases from speculative AI claims in board proposals
- Strategic frameworks for assessing where AI investment creates sustainable value
Module 3: AI Risk Typology: Identifying What Can Go Wrong
- Algorithmic bias and discriminatory outcomes: causes, consequences, and board responsibilities
- Opacity and explainability: governing AI systems that cannot fully account for their decisions
- Data quality, data poisoning, and the risks embedded in AI training datasets
- Cybersecurity threats unique to AI systems—adversarial attacks, model theft, and manipulation
- Reputational and brand risk from high-profile AI failures or public controversy
- Operational and concentration risk from AI vendor dependency and system fragility
Module 4: AI Ethics and Responsible Innovation
- Core principles of ethical AI: fairness, accountability, transparency, and human dignity
- Human rights implications of AI deployment in hiring, credit, healthcare, and law enforcement
- The board's ethical duty of care toward employees, customers, and communities
- Ethics by design: how governance decisions shape the values embedded in AI systems
- Stakeholder expectations and the reputational stakes of ethical AI commitments
- Building an organisational AI ethics framework that the board can own and enforce
Module 5: The Global AI Regulatory Environment
- Overview of the EU AI Act: prohibited applications, high-risk categories, and compliance obligations
- National AI strategies and regulatory frameworks across Africa, the Middle East, and Asia
- Sectoral regulation of AI in financial services, healthcare, and critical infrastructure
- Data protection law and AI: GDPR, national data laws, and cross-border data flows
- Liability and accountability under emerging AI legislation—who is responsible when AI causes harm
- Anticipating future regulation: how boards should monitor and prepare for regulatory evolution
Module 6: Board Structures and Mandates for AI Oversight
- Where AI governance sits: full board, audit committee, risk committee, or dedicated AI committee
- Designing a board-level AI oversight mandate: scope, authority, and reporting lines
- The role of the board secretary in managing AI governance documentation and agenda-setting
- Engaging external AI advisors and independent technical experts at board level
- Board skills matrix review: identifying AI literacy gaps and filling them through appointment or training
- Case examples of effective board AI governance structures from leading global organisations
Module 7: Governing AI Risk: Frameworks, Tools, and Board Accountability
- Applying enterprise risk management frameworks to AI-specific risks
- Introduction to the AI risk register: structure, ownership, and board review cadence
- Three lines of defence in AI governance: first-line deployment, second-line risk, and third-line audit
- Board-level key risk indicators for AI: what metrics boards should monitor and challenge
- Incident response governance: how boards should respond when AI systems fail or cause harm
- Connecting AI risk appetite to organisational strategy and stakeholder expectations
Module 8: AI in the Audit and Assurance Landscape
- How external and internal auditors are approaching AI systems and AI-generated outputs
- AI in financial reporting: risks, disclosures, and audit committee responsibilities
- Assessing the adequacy of management's AI controls through board-level questioning
- ISO 42001 and other emerging AI management system standards boards should be aware of
- Board responsibilities for AI-related disclosures to regulators, investors, and the public
- Commissioning an AI governance audit: what to ask for and how to interpret the findings
Module 9: Engaging Technical Teams: Asking the Right Questions
- Bridging the communication gap between the board and AI or data science teams
- A director's questioning toolkit: ten essential questions for any AI proposal or update
- Evaluating AI performance dashboards and technical reports presented to the board
- Challenge without obstruction: how to govern AI innovation without stifling it
- Recognising when technical assurances are insufficient and escalating appropriately
- Building productive working relationships between board committees and the Chief AI Officer
Module 10: Developing and Implementing a Board-Level AI Governance Policy
- Components of a comprehensive organisational AI governance policy
- Aligning AI governance policy with organisational values, strategy, and risk appetite
- Stakeholder consultation and communication in AI policy development
- Embedding AI governance into procurement, vendor management, and partnership due diligence
- Monitoring and reviewing AI governance policy effectiveness over time
- Workshop: participants draft a tailored AI governance policy outline for their own organisation
Requirements & Prerequisites
Participants should be serving or aspiring board members, senior executives, or governance professionals with responsibility for organisational strategy or risk oversight. No technical background in data science or artificial intelligence is required—this course is designed for non-technical leaders. A basic familiarity with corporate governance principles and strategic decision-making is assumed.
Training Methodology
- Boardroom simulation exercises in which participants roleplay governance scenarios—including approving an AI investment proposal, responding to an AI-related incident, and briefing external stakeholders
- Case study analysis of high-profile AI governance successes and failures drawn from financial services, healthcare, government, and consumer industries
- Structured board paper review sessions where participants critique real-style AI strategy documents using a governance assessment rubric
- Guided use of board-level AI governance tools—including a customisable AI risk register, an AI ethics checklist, and an AI oversight reporting template
- Small-group discussion panels that mirror audit or risk committee deliberations, developing participants' ability to question AI proposals constructively
- Regulatory mapping workshops in which participants assess how current and upcoming AI regulations (EU AI Act, national frameworks) apply to their own organisations
- Peer learning exchanges where participants from different sectors share governance challenges and emerging practices
- Individual reflection assignments requiring each participant to draft a one-page AI governance position paper for their own board
- Expert-facilitated Q&A sessions modelled on board briefings from a Chief AI Officer or external AI advisor
Organisational Benefits
- Boards are better equipped to challenge AI strategies and hold management accountable, reducing blind spots at the top of the organisation
- Strengthened board-level AI risk oversight reduces the likelihood of costly AI failures, regulatory breaches, or reputational damage
- Organisations gain a governance advantage as regulators, investors, and stakeholders increasingly demand demonstrable AI accountability
- Clear board mandates and AI committee structures improve the speed and quality of AI-related decisions at the executive level
- Ethical AI governance frameworks protect vulnerable stakeholders and strengthen the organisation's social licence to operate
- Directors become more confident sponsors of responsible AI innovation, accelerating value creation without sacrificing compliance
- Improved dialogue between the board and technical teams reduces miscommunication and aligns AI initiatives with strategic priorities
- AI governance policies developed during training can be directly adapted and implemented within participants' organisations
- Organisations are better positioned to attract investment and partnerships in an environment where AI governance is increasingly scrutinised
- Participants return as internal champions for responsible AI, elevating the governance culture across leadership tiers
Where Is Board-Level AI Literacy: Governing the AI-Powered Enterprise Training Offered?
This course runs in-person across: Kampala, Pretoria, Dar es Salaam, Addis Ababa, Accra, Johannesburg, Arusha, Naivasha, Kisumu, Abuja, Nairobi, Dubai, Nakuru, Cape Town, Zanzibar, Kigali, Lagos, Mombasa, Riyadh, plus Live Virtual (online) cohorts. Browse live dates for your city below.
Upcoming Schedules
| Location | Format | Dates | Price |
|---|
| Live Virtual |
Online |
19 Oct 2026 – 23 Oct 2026 |
KES 80,000 |
| Kampala, Uganda |
In-Person |
26 Oct 2026 – 30 Oct 2026 |
USD 1,900 |
| Pretoria, South Africa |
In-Person |
26 Oct 2026 – 30 Oct 2026 |
USD 3,300 |
| Dar es Salaam, Tanzania |
In-Person |
26 Oct 2026 – 30 Oct 2026 |
USD 2,300 |
| Addis Ababa, Ethiopia |
In-Person |
26 Oct 2026 – 30 Oct 2026 |
USD 2,400 |
| Accra, Ghana |
In-Person |
26 Oct 2026 – 30 Oct 2026 |
USD 3,800 |
| Johannesburg, South Africa |
In-Person |
26 Oct 2026 – 30 Oct 2026 |
USD 3,500 |
| Arusha, Tanzania |
In-Person |
26 Oct 2026 – 30 Oct 2026 |
USD 2,100 |
| Naivasha, Kenya |
In-Person |
26 Oct 2026 – 30 Oct 2026 |
KES 120,000 |
| Kisumu, Kenya |
In-Person |
26 Oct 2026 – 30 Oct 2026 |
KES 120,000 |
| Abuja, Nigeria |
In-Person |
2 Nov 2026 – 6 Nov 2026 |
USD 2,800 |
| Pretoria, South Africa |
In-Person |
2 Nov 2026 – 6 Nov 2026 |
USD 3,300 |
| Live Virtual |
Online |
2 Nov 2026 – 6 Nov 2026 |
KES 80,000 |
| Nairobi, Kenya |
In-Person |
2 Nov 2026 – 6 Nov 2026 |
KES 105,000 |
| Addis Ababa, Ethiopia |
In-Person |
2 Nov 2026 – 6 Nov 2026 |
USD 2,400 |
+226 more dates — view live schedule →
Frequently Asked Questions
Do I need a technology or data science background to attend this course?
No technical background is required. This programme is designed specifically for non-technical leaders—the focus is entirely on governance, strategy, and risk rather than coding or data science. All AI concepts are explained in plain, accessible language throughout the course.
Will I receive a certificate upon completing the course?
Yes. Participants who complete the full five-day programme will receive a TrainingComplex Certificate of Completion in Board-Level AI Literacy: Governing the AI-Powered Enterprise. This certificate can be used for continuing professional development records and board governance portfolios.
How is this course different from general AI awareness training?
This course is purpose-built for board members and senior executives, not general staff. The content, case studies, tools, and exercises are all framed around board-level responsibilities—governance structures, risk oversight, regulatory accountability, and strategic decision-making—rather than operational or technical AI adoption.
Can the training be delivered in-house for an entire board or executive team?
Yes. TrainingComplex offers tailored in-house delivery of this programme for boards, executive committees, or senior leadership teams who wish to participate together. Bespoke delivery can incorporate your organisation's specific AI context, sector, and governance challenges. Please contact TrainingComplex directly to discuss an in-house arrangement.
Which sectors is this course relevant to?
The course is relevant across all sectors in which AI is being deployed or considered, including financial services, healthcare, government, development organisations, retail, telecommunications, energy, and education. Case studies and examples are drawn from multiple industries to ensure broad applicability.
