Your staff are already using ChatGPT and Copilot. The board wants an AI plan. Most companies don’t yet have the data, the skills or the rules to do much with either.
This one-day course gives managers a working understanding of the whole chain: what a data-driven organisation looks like, how analytics, machine learning and generative AI differ, and why data quality decides what AI can do. Participants practise prompting on their own work tasks, score AI use cases for value and feasibility, plan a project with CRISP-DM, and cover privacy, bias and Malaysia’s personal data protection rules. They leave with an AI Opportunity Canvas for a real use case and a 30-60-90 day action plan.
By the end, participants can
- Explain the difference between data, information, knowledge and insight
- Assess their organisation’s data maturity and explain why a data-driven culture matters
- Describe the evolution of AI, and distinguish analytics, machine learning, deep learning and generative AI
- Explain why data quality and quantity determine AI results
- Write effective prompts and use generative AI tools for everyday business tasks
- Identify and prioritise AI use cases using a value-versus-feasibility approach
- Apply the CRISP-DM methodology to plan an analytics or AI project
- Recognise privacy, bias, security and ethical risks, and promote responsible AI practices
Course outline
The day
01Becoming a Data-Driven Organisation
- Why data and AI matter now, and how to build a data-driven culture
- Data, information, knowledge and insight, with real-world examples
- Data foundations: collection, integration, quality and the big data ecosystem
- Analytics in action: choosing the right technique and designing clear dashboards
02AI Explained, from Machine Learning to Generative AI
- A short history of AI and how it reached today's breakthroughs
- AI, machine learning, deep learning and generative AI: how they differ
- How machines learn, and what neural networks and large language models do
- Why data quality and quantity determine AI results
- Strengths and limits of AI, including hallucinations and bias
03Generative AI at Work
- The AI assistant landscape and what each tool is good for
- A practical prompting framework, with before-and-after examples
- Use cases by function: finance, HR, marketing, operations, sales and customer service
- Hands-on lab: participants write and improve prompts for their own job tasks
04From Idea to Impact
- Choosing the right AI use cases: value versus feasibility
- Planning a project with CRISP-DM, estimating ROI and managing change
- Case studies: successful AI implementations across industries
- Workshop: teams begin an AI Opportunity Canvas for a real use case from their own organisation
05Responsible AI and Your Action Plan
- Data privacy and security, including personal data protection requirements in Malaysia
- Bias, transparency, human oversight and a staff AI usage policy
- Emerging trends: AI agents and automation
- Wrap-up: finish the AI Opportunity Canvas, share short team pitches, and create a personal 30-60-90 day action plan