About This Course
Artificial intelligence is no longer a future consideration — it is a present competitive imperative. Across industries, organisations that successfully integrate AI into their core operations are outpacing peers in efficiency, innovation, and customer value delivery. Yet the greatest barrier to AI transformation is rarely technological; it is leadership. Executives who lack a structured understanding of AI strategy, governance, and change management struggle to make sound investment decisions, align stakeholders, and build the institutional capabilities required for sustainable AI adoption.
Leading AI Transformation: The Executive Playbook Training is a five-day immersive programme designed specifically for senior leaders, directors, and C-suite executives who must guide their organisations through the complexities of AI-driven change. The course takes a deliberately non-technical stance — participants do not need a background in data science or engineering. Instead, the focus is on the leadership dimensions of AI transformation: how to set strategy, govern responsibly, manage risk, develop talent, and measure impact.
Drawing on real-world case studies from Africa, the Middle East, and global markets, the programme equips participants with a practical executive playbook they can immediately apply in their own organisations. Through facilitated discussions, peer learning, and hands-on planning workshops, leaders leave with a clear AI transformation roadmap, a governance framework, and the confidence to champion AI adoption at the board level and across their teams.
TrainingComplex has designed this course to reflect the realities facing executive leaders in emerging and high-growth markets, where the pace of digital disruption is accelerating and the need for informed, ethical, and decisive AI leadership has never been greater.
Who Should Attend
- Chief Executive Officers and Managing Directors
- Chief Operating Officers and Chief Strategy Officers
- Chief Information Officers and Chief Digital Officers
- Senior Directors and Vice Presidents overseeing digital or innovation portfolios
- Government Commissioners, Permanent Secretaries, and Agency Directors driving public-sector digitalisation
- Board members and non-executive directors seeking AI governance literacy
- Senior HR and Talent Leaders responsible for workforce transformation
- Head of Department and divisional leaders in AI-adjacent functions such as finance, supply chain, and customer experience
Learning Objectives
- Articulate a clear organisational AI vision and align it with corporate strategy and stakeholder expectations
- Conduct a structured AI readiness assessment across people, processes, data, and technology dimensions
- Design and implement an AI governance framework that addresses ethics, accountability, and regulatory compliance
- Evaluate and prioritise AI use cases using value-impact matrices and ROI frameworks relevant to executive decision-making
- Lead cross-functional AI transformation programmes using proven change management and stakeholder engagement methodologies
- Build and sustain a data-driven organisational culture that enables continuous AI learning and innovation
- Manage AI-related risks including bias, security vulnerabilities, reputational exposure, and third-party dependency
- Develop workforce AI capability strategies, including reskilling, hiring, and partnering approaches
- Establish performance metrics and governance dashboards to monitor AI initiative progress and business impact
- Communicate AI strategy compellingly to boards, investors, regulators, and frontline employees
Course Modules
Module 1: The AI Imperative: Why Executive Leadership Is the Deciding Factor
- The global AI landscape: where value is being created and destroyed
- Why AI transformation fails — the leadership gap vs. the technology gap
- Distinguishing AI hype from genuine strategic opportunity
- Key AI concepts every executive must understand (without writing code)
- The executive's role in setting the AI agenda vs. delegating it
- Self-assessment: your current AI leadership posture and readiness
Module 2: Defining Your Organisational AI Vision and Strategy
- Linking AI strategy to overall corporate mission, vision, and competitive positioning
- Frameworks for articulating a compelling and credible AI vision
- Identifying strategic AI domains: where AI will create the most value for your organisation
- Setting short-, medium-, and long-term AI transformation horizons
- Aligning AI ambition with organisational capacity and risk appetite
- Workshop: drafting your organisation's AI vision statement
Module 3: AI Readiness Assessment: People, Data, Process, and Technology
- The four dimensions of AI readiness and how to diagnose each
- Data readiness: quality, availability, governance, and infrastructure
- Process readiness: which workflows are AI-automatable or AI-augmentable
- People readiness: current skills inventory and capability gaps
- Technology readiness: infrastructure, cloud maturity, and digital systems
- Workshop: completing an AI readiness scorecard for your organisation
Module 4: Identifying and Prioritising AI Use Cases
- The use case discovery process: from brainstorming to business case
- Value-impact matrix: scoring use cases on feasibility, ROI, and strategic fit
- Quick wins vs. transformational bets: balancing the AI portfolio
- Avoiding common use case selection mistakes that drain resources
- Building the business case: financial and non-financial value articulation
- Workshop: selecting and prioritising three AI use cases for your organisation
Module 5: AI Governance: Ethics, Accountability, and Responsible AI
- Why governance is the foundation of sustainable AI transformation
- Core principles of responsible AI: fairness, transparency, accountability, and safety
- Establishing an AI ethics policy and code of conduct
- Structuring AI governance bodies: roles of the board, executive committee, and AI council
- Navigating the emerging regulatory landscape for AI across key jurisdictions
- Building accountability mechanisms: model oversight, audit trails, and incident response
Module 6: Managing AI Risk: From Bias to Security to Reputational Exposure
- Taxonomy of AI risks executives must understand and manage
- Algorithmic bias: causes, consequences, and mitigation strategies
- Data privacy and cybersecurity risks in AI-powered systems
- Third-party AI vendor risk: evaluation, contracting, and monitoring
- Reputational and trust risk when AI decisions affect customers or citizens
- Building a risk register and escalation protocol for AI initiatives
Module 7: Leading Change: Driving Organisation-Wide AI Adoption
- Why AI transformation is fundamentally a change management challenge
- Applying structured change frameworks (ADKAR, Kotter) to AI adoption
- Identifying and engaging AI champions, sceptics, and neutral stakeholders
- Overcoming cultural resistance: fear of job displacement and AI mistrust
- Communicating the AI transformation narrative to diverse internal audiences
- Designing a change roadmap with milestones, owners, and accountability
Module 8: Building an AI-Ready Workforce and Data-Driven Culture
- The workforce impact of AI: augmentation, displacement, and new role creation
- Designing an AI reskilling and upskilling strategy for different employee segments
- Attracting and retaining AI talent: hiring strategies and partnerships
- Leader behaviours that model and reinforce a data-driven decision culture
- Creating psychological safety for experimentation, learning from failure, and iteration
- Partnering with universities, accelerators, and ecosystem players to build capability
Module 9: Executing AI Transformation: From Roadmap to Results
- Programme management principles for large-scale AI transformation initiatives
- Structuring the AI transformation office: roles, governance, and ways of working
- Agile and iterative delivery approaches applied to AI deployment
- Managing cross-functional dependencies in AI projects
- Budgeting and resource allocation strategies for AI portfolios
- Tracking execution progress: stage gates, reviews, and course corrections
Module 10: Measuring AI Impact and Communicating Value to the Board
- Defining success metrics for AI initiatives: financial, operational, and strategic KPIs
- Building an executive AI performance dashboard
- ROI measurement frameworks for AI investments over different time horizons
- Translating technical AI outcomes into board-level business language
- Reporting AI transformation progress to investors, regulators, and the public
- Finalising your personal AI Executive Playbook and 90-day action plan
Requirements & Prerequisites
Participants should hold a senior leadership, management, or director-level position within their organisation. No prior technical knowledge of artificial intelligence, data science, or software engineering is required. A general familiarity with strategic planning and organisational management will help participants engage fully with the programme content.
Training Methodology
- Executive case study analysis drawing on real AI transformation journeys from leading African, Middle Eastern, and global organisations across public and private sectors
- Facilitated peer roundtables where participants debate AI governance dilemmas, investment trade-offs, and transformation challenges drawn from their own industries
- AI Readiness Assessment Workshop in which each participant scores their own organisation across people, data, process, and technology dimensions to identify priority gaps
- Use Case Prioritisation Lab using a structured value-impact matrix that participants apply to real AI opportunities within their organisations
- Scenario planning exercises simulating AI crisis situations — such as a biased algorithm decision or a data breach — requiring executive-level response and communication
- Board-level communication simulation where participants pitch an AI transformation strategy to a panel, receiving structured feedback on clarity, credibility, and strategic alignment
- Personal AI Leadership Reflection Journal maintained throughout the five days to capture insights, commitments, and leadership growth areas
- Guest perspectives and benchmark data from AI transformation leaders shared through curated video interviews and practitioner briefings
Organisational Benefits
- Organisations gain executive leaders equipped to make informed, strategic AI investment decisions that deliver measurable returns
- Improved AI governance structures reduce the risk of regulatory non-compliance, reputational harm, and ethical failures
- Accelerated AI adoption timelines as leaders overcome internal resistance and align stakeholders more effectively
- Stronger cross-functional collaboration on digital initiatives, reducing siloed experimentation and wasted investment
- Enhanced competitive positioning through AI strategies that are grounded in business value rather than technology hype
- A more AI-literate senior leadership team capable of critically evaluating vendor proposals, partnerships, and technology roadmaps
- Clearer AI talent strategies that reduce skills gaps and support sustainable organisational capability building
- Organisations develop practical AI transformation roadmaps they can begin executing immediately upon participants' return
Where Is Leading AI Transformation: The Executive Playbook Training Offered?
This course runs in-person across: Johannesburg, Accra, Naivasha, Cape Town, Riyadh, Lagos, Dar es Salaam, Kampala, Mombasa, Kigali, Abuja, Nairobi, Kisumu, Dubai, Pretoria, Addis Ababa, Arusha, Zanzibar, Nakuru, plus Live Virtual (online) cohorts. Browse live dates for your city below.
Upcoming Schedules
| Location | Format | Dates | Price |
|---|
| Live Virtual |
Online |
12 Oct 2026 – 16 Oct 2026 |
KES 80,000 |
| Johannesburg, South Africa |
In-Person |
26 Oct 2026 – 30 Oct 2026 |
USD 3,500 |
| Accra, Ghana |
In-Person |
26 Oct 2026 – 30 Oct 2026 |
USD 3,800 |
| Naivasha, Kenya |
In-Person |
26 Oct 2026 – 30 Oct 2026 |
KES 120,000 |
| Cape Town, South Africa |
In-Person |
26 Oct 2026 – 30 Oct 2026 |
USD 3,800 |
| Riyadh, Saudi Arabia |
In-Person |
26 Oct 2026 – 30 Oct 2026 |
USD 4,500 |
| Lagos, Nigeria |
In-Person |
26 Oct 2026 – 30 Oct 2026 |
USD 2,500 |
| Dar es Salaam, Tanzania |
In-Person |
26 Oct 2026 – 30 Oct 2026 |
USD 2,300 |
| Kampala, Uganda |
In-Person |
26 Oct 2026 – 30 Oct 2026 |
USD 1,900 |
| Mombasa, Kenya |
In-Person |
26 Oct 2026 – 30 Oct 2026 |
KES 120,000 |
| Kigali, Rwanda |
In-Person |
26 Oct 2026 – 30 Oct 2026 |
USD 1,900 |
| Naivasha, Kenya |
In-Person |
2 Nov 2026 – 6 Nov 2026 |
KES 120,000 |
| Abuja, Nigeria |
In-Person |
2 Nov 2026 – 6 Nov 2026 |
USD 2,800 |
| Dar es Salaam, Tanzania |
In-Person |
2 Nov 2026 – 6 Nov 2026 |
USD 2,300 |
| Kampala, Uganda |
In-Person |
2 Nov 2026 – 6 Nov 2026 |
USD 1,900 |
+227 more dates — view live schedule →
Frequently Asked Questions
Do I need a technical background in AI or data science to benefit from this course?
No technical background is required. This programme is designed specifically for executives and senior leaders who need to understand AI strategically — not build or code it. The content is deliberately non-technical, focusing on strategy, governance, culture, and leadership.
What will I leave with at the end of the five days?
Participants leave with a personalised AI Executive Playbook that includes an AI readiness assessment for their organisation, a prioritised use case portfolio, a governance framework, a workforce strategy outline, and a concrete 90-day action plan ready for implementation.
Is this course relevant to public-sector leaders, or is it primarily designed for the private sector?
The course is designed for both sectors. Case studies and frameworks draw from public-sector AI transformation examples — including e-government, smart cities, and digital health — as well as private-sector applications, ensuring relevance for government officials, agency heads, and corporate executives alike.
Will I receive a certificate upon completing the course?
Yes. Participants who complete the full five-day programme receive a Certificate of Completion from TrainingComplex, which can be used for professional development records, board profiles, or continuing education portfolios.
How large are the cohorts, and is there peer networking time built into the schedule?
Cohorts are intentionally kept small — typically 15 to 25 participants — to maximise peer interaction and the quality of facilitated discussions. Structured networking sessions, peer roundtables, and collaborative workshops are built into each day of the programme.
