Course Code: aistbusprof
Duration: 21 hours

Prerequisites:

Prerequisites

● Basic business and management experience
● Familiarity with digital tools and business processes
● No technical background required

Target Audience

● Business Leaders and Executives seeking to understand AI's strategic implications
● Department Managers looking to implement AI in their functional areas
● Project Managers responsible for AI initiative delivery
● Business Analysts exploring AI-driven insights and process improvements
● Consultants and Advisors helping organisations with digital transformation
● Entrepreneurs and Innovation Managers developing AI-enabled business models

Overview:

Learning Outcomes

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

  1. Strategic Planning: Develop comprehensive AI strategies aligned with business objectives
  2. Prompt Engineering Mastery: Create effective prompts for various business applications
    and train teams on best practices
  3. Business Case Development: Create compelling business cases for AI investments with
    clear ROI projections
  4. Risk Management: Implement governance frameworks to manage AI-related risks and
    ensure compliance
  5. Change Leadership: Lead organisational transformation initiatives for AI adoption
  6. AI Tool Optimisation: Effectively utilise ChatGPT, Gemini, and Copilot for maximum
    business value
  7. Performance Measurement: Design KPIs and measurement frameworks for AI project
    success
  8. Future Planning: Create long-term strategic roadmaps for AI evolution within their
    organisations

Course Format

  • Interactive Workshops: Hands-on exercises with real business scenarios using ChatGPT,
    Gemini, and Copilot
  • Prompt Engineering Labs: Practical sessions developing and refining prompts for specific
    business use cases
  • Case Study Analysis: Industry-specific examples and lessons learned
  • Group Discussions: Peer learning and experience sharing
  • Practical Exercises: Business case development and strategic planning activities
  • Action Planning: Personalised roadmaps for post-course implementation

Course Outline:

Day 1: Foundation and Strategy

Morning Session (9:30-12:30)

Module 1: Introduction to AI and Business Transformation

Learning Objectives:
● Identify key AI definitions and terminology relevant to business contexts
● Differentiate between narrow and general AI applications in business
● Explain AI's impact on industry competitiveness and market positioning
● Assess sustainability and environmental impact considerations in AI implementation

Key Topics:
● AI fundamentals for business leaders
● Industry disruption patterns and competitive advantages
● Market trends and adoption rates across sectors
● Sustainability measures and environmental impact considerations

Afternoon Session (1:30-4:30)

Module 2: Ethical and Legal Framework for Business AI

Learning Objectives:
● Describe ethical concerns, including bias, privacy, and fairness in business AI
● Develop governance frameworks for ethical AI implementation
● Explain regulatory compliance requirements across different jurisdictions
● Create risk management strategies for AI projects

Key Topics:
● Business ethics in AI decision-making
● Regulatory landscape (GDPR, EU AI Act, ISO 42001 and sector-specific regulations)
● Corporate governance and accountability frameworks
● Stakeholder management and public trust

Day 2: Technical Understanding and Implementation

Morning Session (9:30-12:30)

Module 3: AI Technologies and Business Applications

Learning Objectives:
● List common AI applications across different business functions
● Describe the role of automation and intelligent systems in business processes
● Master prompt engineering fundamentals for effective AI interaction
● Identify machine learning opportunities in business operations
● Evaluate emerging technologies and their business potential

Key Topics:
● Customer service automation (chatbots, virtual assistants)
● Marketing and sales optimisation
● Supply chain and operations intelligence
● Financial analysis and risk management
● HR and talent management applications
● Prompt Engineering Fundamentals:
o Understanding how AI language models work
o Crafting effective prompts for business use cases
o Prompt optimisation techniques and best practices
o Role-based prompting for different business functions
o Iterative prompt refinement strategies
o Common prompt engineering pitfalls and how to avoid them

Afternoon Session (1:30-4:30)

Module 4: Data Strategy and Business Intelligenc

Learning Objectives:
● Describe key data concepts and their business value
● Explain data quality requirements for successful AI implementation
● Apply prompt engineering techniques for data analysis and insights
● Identify data governance and security considerations
● Design data visualisation strategies for business insights

Key Topics:
● Data as a business asset and competitive advantage
● Data quality frameworks and measurement
● Privacy, security, and compliance in data handling
● Business intelligence and analytics platforms
● ROI measurement and KPI development
● Advanced Prompt Engineering Applications:
o Data analysis and interpretation prompts
o Report generation and summarisation techniques
o Query optimisation for business intelligence
o Prompt chains for complex analytical workflows

Day 3: Implementation and Future Planning

Morning Session (9:30-12:30)

Module 5: Implementing AI in Your Organisation

Learning Objectives:
● Identify high-impact AI opportunities within your organisation
● Develop comprehensive business cases for AI investments
● Create stakeholder engagement and change management strategies
● Design project management approaches for AI initiatives
● Implement prompt engineering standards and best practices across teams

Key Topics:
● AI opportunity assessment and prioritisation
● Business case development and ROI calculation
● Stakeholder mapping and engagement strategies
● Change management and organisational readiness
● Vendor selection and partnership strategies
● Budget planning and resource allocation
● Organisational Prompt Engineering:
o Creating prompt libraries and templates for business functions
o Training teams on effective AI interaction
o Establishing prompt engineering governance and standards
o Measuring and optimising AI tool effectiveness

Afternoon Session (1:30-4:30)

Module 6: Future Planning and Organisational Transformation

Learning Objectives:
● Describe emerging career opportunities and skill requirements in the AI era
● Identify real-world AI use cases across industries
● Explain AI's long-term impact on business models and society
● Develop strategic roadmaps for AI adoption
● Create sustainable prompt engineering practices for continuous improvement

Key Topics:
● Workforce transformation and reskilling strategies
● Industry-specific AI applications and case studies
● Future business models and competitive landscapes
● Building AI-ready organizational culture
● Long-term strategic planning and continuous adaptation
● Future of Human-AI Collaboration:
o Evolving prompt engineering techniques and capabilities
o Integration of AI tools into business workflows
o Continuous learning and adaptation strategies