AI Essentials Course

Topics Covered:

• What is AI – from rule-based systems to machine learning: how AI differs from traditional software, how machine learning systems learn from data, and the distinction between narrow AI and general AI
• Types of AI and their business applications: Natural Language Processing, Computer Vision, Recommendation Systems, Predictive Analytics, and Generative AI – what each does and where each appears in workplace tools and industries
• How AI already works in the workplace: mapping the AI layer inside everyday tools including email, calendar, CRM, and customer service platforms, with a hands-on exercise identifying AI types in tools learners use at work

Topics Covered:

• AI task suitability and evaluation framework: four characteristics of AI-appropriate tasks versus four characteristics of human-essential tasks, and the four-question structured framework for assessing any task or workflow – applied to real workplace scenarios with trainer modelling and a paired exercise
• Industry and function mapping: AI applications and human-essential limits across Marketing, Operations, HR, Finance, Customer Service, and Administration, with a hands-on exercise where learners identify one AI-appropriate task and one human-essential task from their own role

Topics Covered:

• Getting started with GenAI tools and prompt fundamentals: navigating ChatGPT, Claude, and Gemini as workplace tools, understanding what each is best suited for, and prompt structure fundamentals for common workplace tasks such as drafting emails, summarising documents, and generating ideas
• Hands-on GenAI practice session: learners complete three structured workplace tasks using a GenAI tool of their choice, apply a basic prompt-writing checklist, iterate on outputs to improve quality, and evaluate each result for accuracy and suitability before professional use

Learning Outcome:

Topics Covered:

• What AI outputs are and how they fail: how generative AI produces text through probabilistic generation rather than fact retrieval, and three AI output failure modes demonstrated with real examples – hallucinations, dataset bias, and outdated information – with one detection technique and the five-point output review checklist introduced for each
• Output evaluation in practice: hands-on drill applying the five-point checklist to three AI-generated workplace documents each containing deliberate errors, paired review on issues and recommended actions, and class debrief on what structured evaluation catches that casual reading misses

Topics Covered:

• Ethics, fairness, data privacy, and responsible AI use: three ethical dimensions of professional AI use, three data privacy rules for workplace AI, and scenario walkthrough distinguishing appropriate from inappropriate AI data use
• Communicating and applying AI at work: how to explain AI capabilities and limitations to colleagues, managers, and stakeholders, reflection exercise drafting a key message, and programme preview connecting today’s foundations to Modules 2, 3, and 4

• Case Study Written Assessment
• Individual Project Presentation