Practical AI for Business

• Two days, instructor-led, built around business tasks rather than a single AI platform.
• Compares ChatGPT, Copilot, Gemini, Claude and NotebookLM on the work each does best.
• Moves from assistants to custom assistants to agents and intelligent automation.
• Ends with security, governance and a practical AI adoption plan for the participant's own team.

• No programming or data-science background is required.
• General business computer literacy is enough.
• Prior use of an AI assistant is helpful but not assumed.

By the end of this course, participants will be able to:

• explain what AI is and how it differs from traditional software
• distinguish automation, machine learning, generative AI and agentic AI
• explain why AI can confidently produce incorrect information
• compare leading AI assistants and select the right one for a given business task
• construct prompts from objectives, context, constraints and examples, and refine them iteratively
• build reusable prompt templates and AI-assisted workflows for recurring work
• configure a custom assistant with instructions, guardrails and knowledge files
• explain the agentic workflow of planning, execution and reflection, and where oversight belongs
• identify AI security threats including phishing, prompt injection, deepfakes and Shadow AI
• develop an AI adoption approach for their own role or department

• Module 1. Understanding Artificial Intelligence
  • How today's AI differs from traditional software, and how generative AI produces content.
  • Automation, machine learning, generative AI and agentic AI.
  • Common models, their usage and cost; the current landscape and major providers.
• Module 2. Working with Modern AI Assistants
  • ChatGPT, Copilot, Gemini, Claude and NotebookLM compared.
  • Multimodal capability: documents, analysis, text, summaries, reports and presentations.
  • Public versus enterprise platforms, and why human review stays in the loop.
• Module 3. Prompt Engineering and AI Workflows
  • Objectives, context, constraints and examples; role-based prompting.
  • Iterative refinement and reusable prompt templates.
  • Building efficient AI-assisted workflows for common business tasks.
• Module 4. Building Custom AI Assistants
  • Configuring an assistant from business knowledge, without programming.
  • Custom GPTs, Gemini Gems and comparable solutions.
  • Instructions, guardrails, knowledge files and safe sharing inside an organization.
• Module 5. AI Agents and Intelligent Automation
  • Assistants versus agents; planning, execution and reflection.
  • How agents interact with external tools and data.
  • Human-in-the-loop decision making, current limits and the risks of autonomy.
• Module 6. Secure and Responsible AI
  • AI security threats: phishing, prompt injection and deepfakes.
  • Risks of public AI services and the reality of Shadow AI.
  • Protecting confidential information; AI, privacy legislation and organizational policy.
• Module 7. Responsible AI and Business Transformation
  • Principles of responsible AI; bias, hallucinations and automation bias.
  • Transparency, accountability and regulatory developments.
  • Identifying the processes most suitable for AI adoption and building an adoption strategy.
• Module 8. AI-Powered Research and Data Analysis (time permitting)
  • Structured research with AI-assisted tools, and synthesis across multiple sources.
  • Analysing spreadsheets and structured business data in natural language.
  • Verifying sources and claims, and turning analysis into decision-support material.