Artificial Intelligence and Machine Learning Fundamentals for Business

A business-level grounding in how AI and machine learning work, for managers in any function

Platform:

Online

In-class

Revised and Updated: 23 October 2026

Date	Venue	Duration
16 - 20 November 2026	Sandton, Gauteng	5 Days
08 - 12 February 2027	Sandton, Gauteng	5 Days
03 - 07 May 2027	Sandton, Gauteng	5 Days
02 - 06 August 2027	Sandton, Gauteng	5 Days
08 - 12 November 2027	Sandton, Gauteng	5 Days

Course Introduction

AI and machine learning are reshaping how organisations operate, compete, and make decisions. Yet most business professionals have never had a clear, non-technical explanation of what these technologies are, how they learn, and where they go wrong. Without it, it is hard to judge a vendor’s claims, a project’s chances, or an AI-generated answer.

This five-day Artificial Intelligence and Machine Learning Fundamentals for Business course builds that understanding for managers and professionals in any function. Facilitator-led sessions combine plain-language explanation with real business case studies and hands-on exercises. Participants work through how AI systems learn and make decisions, the different types of AI in business use, including generative AI and AI agents, how to use generative AI well, and the data and organisational foundations that make AI initiatives succeed or fail. Responsible use is built in, including the basics of POPIA and AI regulation.

Participants leave able to identify realistic use cases, work with technical teams in a shared vocabulary, and evaluate AI proposals and vendors with informed, critical judgement. No coding or technical background is required.

Course Objectives

By the end of this Artificial Intelligence and Machine Learning Fundamentals for Business course, participants will be able to:

Explain core AI and machine learning concepts in plain business language
Understand at a conceptual level how AI systems learn from data and make decisions, and how their performance is judged
Distinguish between different types of AI systems, including predictive, generative, and agentic AI, and their appropriate business applications
Use generative AI tools effectively and check their output critically
Identify and prioritise realistic use cases for AI and machine learning within their own organisation or function
Assess the data quality and infrastructure foundations that make AI initiatives succeed or fail
Evaluate organisational readiness, including skills and culture, for AI adoption
Recognise the main ethical, privacy, and regulatory issues in business AI use
Evaluate AI-related proposals, vendors, and initiatives with informed, critical judgement, and work effectively with IT, data, and AI specialist teams

Course Benefits

Business-Level Technical Fluency: Go beyond everyday AI tool tips to a real grounding in how AI and machine learning work.
Cut Through Vendor Hype: Evaluate AI proposals and vendors with informed, critical judgement.
Match Capability to Problem: Identify realistic AI use cases in your own function, instead of chasing trends.
Know What Makes AI Initiatives Succeed: Understand the data, infrastructure, and readiness foundations that separate successful AI projects from failed ones.
A Bridge to Specialist Teams: Build the shared vocabulary to work with IT, data, and AI specialists.

Who should attend?

Managers and Professionals in Any Department Who Want a Grounding in AI and Machine Learning
Project Sponsors and Business Analysts Evaluating AI-Related Initiatives
Professionals Who Have Completed Function-Specific AI Courses and Want the Broader Picture
Anyone Preparing to Move into a Role with AI or Digital Transformation Responsibilities

Training Methodology

Our diverse instructional approaches ensure effective learning:

– Lectures & Presentations: Engage with expert-driven, stimulating content.
– Course Material: Access well-crafted supporting resources.
– Group Work: Collaborate on discussions and case studies for practical insights.
– Workshops & Role-Play: Participate in immersive, scenario-based activities.
– Practical Application: Focus on applying theoretical knowledge in real situations.
– Post-Training Support: Receive extensive support after training for skill implementation.

Training Outline

Day 1 — Foundations: What AI Is and How It Learns

Module 1: Foundations of AI and Machine Learning

Core AI and machine learning concepts and terminology, in plain business language
The relationship between AI, machine learning, deep learning, and generative AI
Why these technologies matter to business decisions today, not just IT
Common misconceptions that lead to poor AI decisions
Practical Exercise: Explain three core AI and machine learning terms in your own plain-language summary

Module 2: How AI Systems Actually Learn and Make Decisions

How machine learning models learn patterns from data, at a conceptual level, and the main learning approaches: supervised, unsupervised, and reinforcement
Why AI outputs are probabilistic, not guaranteed facts
How model performance is judged: accuracy, false positives and negatives, and why a model that works in testing can fail in practice
Understanding model limitations, bias, and failure modes in business terms
What 'training data' means and why its quality matters so much
Practical Exercise: Identify potential bias or limitation risks in a sample AI use case

Day 2 — Types of AI and Generative AI in Practice

Module 3: Types of AI in Business Use

Predictive analytics and its common business applications
Generative AI and large language models: where they add genuine business value
AI agents: systems that take actions, and what that means for control
Computer vision and its practical use cases
Automation technologies and how they relate to AI
Practical Exercise: Match a set of sample business problems to the most appropriate type of AI

Module 4: Using Generative AI Well

How generative AI tools work, and why they sometimes state falsehoods confidently
Writing clear prompts and iterating on the output
Checking AI output: verification habits and when a person must review
What not to put into public AI tools: confidential and personal information
Practical Exercise: Complete a short business task with a generative AI tool, and verify and correct the result

Day 3 — Use Cases and Data Foundations

Module 5: Identifying Business Use Cases

Matching AI capabilities to real organisational problems
Prioritising use cases by feasibility and business impact
Avoiding the trap of adopting AI for its own sake
Building a simple use-case business rationale, including expected benefits and costs
Practical Exercise: Identify and prioritise three realistic AI use cases for your own function

Module 6: Data Quality and Infrastructure Foundations

Why data quality determines AI initiative success more than the technology itself
Core data and infrastructure concepts every business sponsor should understand: data sources, storage, cloud, and integration
Common data readiness gaps that derail AI projects
Questions to ask your data and IT teams before committing to an AI initiative
Practical Exercise: Assess a sample organisation's data readiness against a simple readiness checklist

Day 4 — Readiness and Responsible AI

Module 7: Organisational Readiness and the Skills Foundation

Assessing skills, culture, and change readiness for AI adoption
Common organisational barriers that stall AI initiatives
Building internal AI literacy as a foundation for broader adoption
Sequencing AI adoption to match organisational maturity
Practical Exercise: Score your organisation's readiness across skills, culture, and change management factors

Module 8: Responsible AI, Privacy and Regulation Essentials

Core responsible AI principles: fairness, transparency, accountability, and human oversight
POPIA basics for AI: personal information in prompts and training data, and limits on solely automated decisions
The regulatory picture: the EU AI Act where it applies, and the status of South Africa's AI policy (the 2026 draft was withdrawn and is not law)
Basic safeguards: acceptable-use rules, human review, and incident reporting
Practical Exercise: Review a sample AI use case and list the minimum safeguards it needs
The regulatory content is an overview, not legal advice.

Day 5 — Evaluating Proposals and Putting It Together

Module 9: Evaluating AI Proposals and Vendors

Questions that separate a strong AI proposal from a weak one
Common pitfalls in AI vendor evaluation and procurement
Assessing vendor claims critically, including proof-of-concept results
Structuring a decision framework for evaluating competing AI proposals
Practical Exercise: Build an evaluation checklist and apply it to a sample AI vendor proposal

Module 10: Capstone — Your AI Use-Case Pitch

Combining your use case, data readiness check, risks, and safeguards into a short proposal
Presenting for feedback from facilitators and peers
Building a personal action plan for the next 90 days
Practical Exercise: Present a one-page AI use-case proposal for your own function

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FAQs – Artificial Intelligence and Machine Learning Fundamentals for Business

Explore AI and machine learning fundamentals for business, covering core concepts, business applications, automation, data-driven decision-making, AI tools, and emerging technologies.

What is covered in the Artificial Intelligence and Machine Learning Fundamentals for Business course?

The course introduces artificial intelligence and machine learning concepts, business applications, generative AI, data and AI-driven decision-making, automation, AI opportunities and responsible AI adoption.

Who should attend the Artificial Intelligence and Machine Learning Fundamentals for Business training?

The training is suitable for business managers, executives, professionals, analysts, technology teams and decision-makers who want to understand AI and machine learning and their practical business applications.

Do I need programming or technical experience to attend this AI and machine learning course?

No. The course is designed to explain AI and machine learning in a business-focused way, making it suitable for participants who do not have an advanced programming or technical background.

How can artificial intelligence and machine learning be applied in business?

Participants explore practical business applications of AI and machine learning, including automation, data analysis, prediction, decision support, customer applications and opportunities to improve organisational processes.

Does the course cover generative AI and responsible AI use?

Yes. The training introduces generative AI and addresses responsible use, including understanding AI limitations, managing risks and considering ethical and governance requirements when applying AI in business.

Are practical activities included in the AI and Machine Learning Fundamentals course?

Yes. The course uses practical business-focused activities to help participants connect AI and machine learning concepts with real organisational challenges and identify opportunities for applying these technologies.

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Artificial Intelligence and Machine Learning Fundamentals for Business

A business-level grounding in how AI and machine learning work, for managers in any function

Course Objectives

Who should attend?

Course Outline

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Course Introduction

Course Objectives

Course Benefits

Who should attend?

Training Methodology

Training Outline

Course Categories

Course Registration Guide

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