• Explain the key principles, terminology and professional applications of AI for Productivity: Five Traits for Optimal Results.
• Identify operational needs and common challenges related to artificial intelligence.
• Apply practical techniques for models, tools and practical workflows in relevant work scenarios.
• Develop informed decisions and actionable solutions involving output evaluation, ethics and governance.
• Evaluate outcomes using implementation and performance monitoring and propose improvements.

• Introduction to AI for Productivity: Five Traits for Optimal Results: scope, terminology and practical relevance.
• Core foundations: Ai concepts, use cases and data requirements.
• Methods and tools: Models, tools and practical workflows.
• Professional practice: Output evaluation, ethics and governance.
• Applied focus: Key concepts, terminology and professional considerations in artificial intelligence.
• Workshop: Supervised activities applying models, tools and practical workflows; Case studies, performance review and workplace action planning.
• Assessment and action planning: Implementation and performance monitoring.