About This Course
Artificial intelligence is reshaping every sector — from finance and healthcare to agriculture and public administration. Yet many senior leaders find themselves making high-stakes decisions about AI investments, partnerships, and policies without a clear understanding of what AI actually is, what it can realistically do, and where its limits lie. This knowledge gap is not a minor inconvenience; it is a strategic vulnerability that can lead to costly missteps, missed opportunities, and organisational exposure to ethical and reputational risk.
Understanding AI: A Non-Technical Primer for Leaders is a five-day programme designed specifically for decision-makers who do not have — and do not need — a background in data science or software engineering. Instead of teaching algorithms, this course builds the conceptual fluency, strategic vocabulary, and governance instincts that allow leaders to ask the right questions, hold technical teams accountable, and make sound judgements about when and how to deploy AI in their organisations.
Participants will examine real-world AI applications across industries, explore the business value and organisational risks associated with AI adoption, and develop a personal leadership framework for guiding their teams through the complexities of an AI-enabled environment. The programme blends executive-level case studies, structured peer dialogue, and hands-on strategy exercises to ensure that learning translates directly into action back in the workplace.
TrainingComplex designed this course to be immediately applicable. Participants leave not with abstract theory but with concrete tools: an AI readiness checklist for their organisation, a framework for evaluating vendor AI claims, a governance lens for ethical AI deployment, and a communication strategy for bringing their boards and teams along on the AI journey.
Who Should Attend
- Chief Executive Officers, Managing Directors, and C-suite executives seeking strategic AI literacy
- Senior managers and department heads responsible for digital transformation decisions
- Board members and trustees who oversee technology strategy and risk
- Government officials and public sector leaders shaping AI-related policy or procurement
- HR and People leaders navigating the workforce implications of automation and AI
- Strategy, planning, and innovation officers evaluating AI-driven business models
- Project and programme managers leading or commissioning AI-enabled initiatives
- Non-technical founders and entrepreneurs considering AI integration in their products or services
Learning Objectives
- Explain the core concepts of artificial intelligence, machine learning, and generative AI in plain business language
- Distinguish between genuine AI capabilities and inflated vendor claims or popular myths
- Identify high-value AI use cases relevant to their own sector and organisational context
- Assess the strategic, financial, and operational risks associated with AI adoption
- Apply a structured framework for evaluating AI readiness within their organisation
- Ask the right questions when engaging technical teams, vendors, and consultants about AI solutions
- Recognise the ethical dimensions of AI deployment, including bias, fairness, transparency, and accountability
- Develop a basic AI governance approach aligned with organisational values and regulatory expectations
- Communicate a compelling AI vision and strategy to boards, staff, and external stakeholders
- Build a personal action plan for leading AI-related change within their team or organisation
Course Modules
Module 1: Demystifying Artificial Intelligence: What Leaders Actually Need to Know
- Defining AI, machine learning, deep learning, and generative AI in plain language
- A brief and honest history of AI: hype cycles, winters, and genuine breakthroughs
- How AI systems learn: patterns, data, and prediction without programming details
- The difference between narrow AI and the idea of general AI
- Common misconceptions leaders hold about AI and how to correct them
- Why non-technical leaders must be AI-literate in today's strategic environment
Module 2: The AI Landscape Today: Technologies, Tools, and Players
- An overview of major AI technology categories: computer vision, natural language processing, predictive analytics, and robotics
- Generative AI and large language models: what they are and what they can realistically do
- Key players in the global AI ecosystem: technology giants, startups, research labs, and open-source communities
- How AI is being adopted across Africa, the Middle East, and emerging markets specifically
- Cloud AI platforms and what procurement of AI capabilities looks like in practice
- Reading the AI news critically: separating signal from noise in media and vendor communications
Module 3: AI Value Creation: Where and How Organisations Benefit
- The four primary ways AI creates business value: automation, augmentation, acceleration, and insight generation
- Mapping AI use cases to organisational functions: HR, finance, operations, customer service, and strategy
- Sector-specific AI applications in government, healthcare, financial services, agriculture, and education
- How to identify high-potential AI opportunities within your own organisation
- Understanding AI's impact on revenue, cost, and customer experience
- Case study: how a regional bank used AI to transform credit decisioning and customer onboarding
Module 4: Reading the Room: Evaluating AI Vendor Claims and Proposals
- Why AI vendor claims often outpace delivery: incentives, terminology, and selective benchmarking
- A practical framework for evaluating AI product proposals as a non-technical leader
- Key questions to ask vendors about data requirements, model accuracy, explainability, and maintenance
- Understanding AI contracts: what to look for in terms of data ownership, liability, and performance guarantees
- Pilot design basics: how to structure a low-risk AI proof of concept before committing to full deployment
- Case study exercise: evaluating a simulated AI vendor pitch using the course assessment framework
Module 5: Organisational AI Readiness: Assessing Where You Stand
- The five dimensions of AI readiness: data, talent, infrastructure, culture, and leadership
- Conducting an honest data audit: quality, availability, and governance of organisational data
- Understanding the AI talent landscape and what roles organisations actually need to build or buy
- Assessing technology infrastructure requirements without getting lost in technical specifications
- Cultural readiness for AI: risk appetite, experimentation culture, and staff trust in technology
- Completing and debriefing the TrainingComplex Organisational AI Readiness Diagnostic
Module 6: AI Risk: Strategic, Operational, and Reputational Exposure
- Categories of AI risk: technical failure, data risk, regulatory risk, reputational risk, and strategic misalignment
- Understanding model error types and what it means when AI systems get things wrong
- AI and cybersecurity: how AI both creates and helps manage new threat vectors
- Reputational scenarios: what happens when AI decisions harm customers or communities
- Vendor and third-party AI risk: due diligence for AI in your supply chain or partnerships
- Building a risk register for an AI initiative using a practical leadership-level template
Module 7: Ethics, Fairness, and Responsible AI
- Why AI systems can be biased and how bias enters data, models, and deployment contexts
- The impact of biased AI on marginalised communities: case studies from hiring, lending, and law enforcement
- Core principles of responsible AI: fairness, transparency, accountability, and human oversight
- The concept of explainability: why leaders should insist on understanding how AI decisions are made
- AI and human dignity: navigating surveillance, profiling, and automated decision-making ethically
- Developing a responsible AI values statement for your organisation
Module 8: AI Governance: Policies, Oversight, and Accountability Structures
- What AI governance means in practice and why boards and executives own it
- Key international AI governance frameworks: the EU AI Act, NIST AI RMF, and African Union perspectives
- Building an AI governance committee or oversight function within your organisation
- Creating an AI policy: key elements including acceptable use, human-in-the-loop requirements, and escalation paths
- AI auditing and monitoring: how organisations verify that AI systems perform as intended over time
- Case study: how a public sector organisation built a governance framework after an AI procurement failure
Module 9: Leading People Through AI-Driven Change
- How AI changes jobs, roles, and workflows rather than simply eliminating them
- Communicating about AI to staff: addressing fear, building understanding, and creating psychological safety
- Workforce planning in an AI environment: reskilling, upskilling, and role redesign strategies
- Leading cross-functional teams that include both technical AI staff and domain experts
- Managing resistance to AI adoption: diagnosing sources and applying change leadership principles
- Case study: how a manufacturing company successfully reskilled its workforce during an AI-driven process transformation
Module 10: Building and Communicating Your AI Strategy
- Framing a coherent AI strategy aligned to organisational mission, values, and competitive context
- Prioritising AI investments: a portfolio approach to balancing quick wins with longer-term transformation
- Gaining board and stakeholder buy-in: how to present AI strategy compellingly to non-technical audiences
- Building an AI roadmap: sequencing initiatives, setting milestones, and defining success metrics
- Creating a 90-day personal AI leadership action plan tailored to your own organisational context
- Final reflection and peer sharing: commitments, challenges anticipated, and support networks going forward
Requirements & Prerequisites
No technical background in data science, programming, or mathematics is required or expected. Participants should have at least five years of professional or managerial experience and a general familiarity with strategic decision-making in an organisational context. An openness to exploring new technologies and their business implications is all that is needed to get the most from this programme.
Training Methodology
- Executive case study analysis drawn from AI deployments in banking, healthcare, agriculture, government, and retail across Africa and the Middle East
- Structured leadership dialogue sessions where participants debate real AI investment and governance dilemmas using a structured decision-making protocol
- Vendor pitch evaluation exercises in which participants apply a critical assessment framework to simulated AI product proposals
- Organisational AI readiness self-assessment using a guided diagnostic tool completed individually and debriefed in peer groups
- Scenario-planning workshops where leadership teams map AI risks and opportunities across a three-to-five-year strategic horizon
- Guest perspectives drawn from video interviews with AI practitioners, ethicists, and senior executives who have led AI transformations
- Reflective journaling prompts at the close of each day to consolidate personal insights and leadership commitments
- Co-created personal action planning sessions in which each participant designs a concrete 90-day AI leadership roadmap for their own organisation
Organisational Benefits
- Leaders gain the strategic confidence to champion AI initiatives without relying solely on technical advisors
- Organisations reduce the risk of costly AI investments driven by hype rather than sound business judgement
- Improved cross-functional dialogue between senior leadership and technical or data science teams
- Stronger governance frameworks that protect the organisation from ethical, legal, and reputational AI risks
- Faster, better-informed decision-making on AI procurement, partnerships, and deployment
- A shared leadership language around AI that aligns board, executive, and operational levels
- Enhanced organisational readiness to attract AI talent and technology partnerships
- Clearer workforce planning strategies in the face of automation and AI-driven role changes
Where Is Understanding AI: A Non-Technical Primer for Leaders Training Offered?
This course runs in-person across: Abuja, Mombasa, Nairobi, Riyadh, Nakuru, Kigali, Dubai, Zanzibar, Cape Town, Lagos, Accra, Kampala, Naivasha, Arusha, Pretoria, Kisumu, Johannesburg, Dar es Salaam, Addis Ababa, 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 |
| Live Virtual |
Online |
24 Oct 2026 – 15 Nov 2026 |
KES 80,000 |
| Abuja, Nigeria |
In-Person |
26 Oct 2026 – 30 Oct 2026 |
USD 2,800 |
| Mombasa, Kenya |
In-Person |
26 Oct 2026 – 30 Oct 2026 |
KES 120,000 |
| Nairobi, Kenya |
In-Person |
26 Oct 2026 – 30 Oct 2026 |
KES 115,000 |
| Riyadh, Saudi Arabia |
In-Person |
26 Oct 2026 – 30 Oct 2026 |
USD 4,500 |
| Nakuru, 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 |
| Dubai, United Arab Emirates |
In-Person |
26 Oct 2026 – 30 Oct 2026 |
USD 4,100 |
| Zanzibar, Tanzania |
In-Person |
26 Oct 2026 – 30 Oct 2026 |
USD 2,500 |
| Cape Town, South Africa |
In-Person |
26 Oct 2026 – 30 Oct 2026 |
USD 3,800 |
| Lagos, Nigeria |
In-Person |
26 Oct 2026 – 30 Oct 2026 |
USD 2,500 |
| Abuja, Nigeria |
In-Person |
2 Nov 2026 – 6 Nov 2026 |
USD 2,800 |
| Nairobi, Kenya |
In-Person |
2 Nov 2026 – 6 Nov 2026 |
KES 105,000 |
| Accra, Ghana |
In-Person |
9 Nov 2026 – 13 Nov 2026 |
USD 3,800 |
+228 more dates — view live schedule →
Frequently Asked Questions
Do I need any technical background to attend this course?
Absolutely not. This programme is specifically designed for leaders without a technical background. No coding, mathematics, or data science knowledge is required — the course is built entirely around strategic, organisational, and governance perspectives on AI.
Will I receive a certificate upon completion?
Yes. All participants who complete the five-day programme receive a Certificate of Completion from TrainingComplex. This certificate recognises your achievement in AI strategic literacy and can be shared on professional profiles or submitted to your organisation's HR records.
How is this course different from a data science or AI technical training?
This course does not teach you how to build AI systems — it teaches you how to lead organisations that use them. The focus is on strategic understanding, governance, risk management, and people leadership rather than algorithms, programming, or model development.
Can this course be delivered in-house for our leadership team?
Yes. TrainingComplex offers customised in-house delivery of this programme for executive leadership teams, boards, and senior management cohorts. In-house sessions can be tailored to your specific sector, organisational context, and strategic priorities. Contact TrainingComplex directly to discuss a customised proposal.
What will I be able to do differently after attending this course?
You will leave with the confidence to engage meaningfully in AI-related decisions, ask the right questions of technical teams and vendors, assess your organisation's AI readiness, apply a responsible AI governance lens, and communicate a credible AI strategy to your board and staff. You will also have a concrete 90-day personal action plan ready to implement.
