Introduction
AI and data science are reshaping how organizations operate, compete, and innovate. AI and Data Science for Business Transformation is designed to help leaders understand how these capabilities can be applied to drive strategic change, improve efficiency, and create new value. The course focuses on the business implications of AI and data science, enabling participants to assess opportunities, manage risks, and lead transformation initiatives with greater confidence and clarity.
Course Objectives
- Understand the strategic role of AI and data science
- Identify transformation opportunities across business functions
- Evaluate the business value of AI-driven initiatives
- Recognize organizational, ethical, and operational risks
- Strengthen leadership readiness for digital transformation
- Build a roadmap for responsible AI and data adoption
Target Audience
- Executives leading transformation and innovation agendas
- Senior managers responsible for strategy and modernization
- Business leaders evaluating AI and analytics opportunities
- Department heads overseeing process and service improvement
- Entrepreneurs exploring new digital business models
- Decision-makers sponsoring technology-enabled change
Course Outline
- 5 Sections
- 0 Lessons
- 5 Days
Expand all sectionsCollapse all sections
- Day 1: AI and Data Science in Modern Business• What AI and data science mean for transformation
• How organizations create value from intelligent systems
• Key business trends in analytics and automation
• The relationship between technology and organizational change
• Examples of transformation driven by data and AI0 - Day 2: Identifying Strategic Opportunities• Where AI and data science add the most value
• Improving efficiency, service, growth, and decision-making
• Prioritizing high-impact use cases
• Evaluating feasibility and readiness
• Workshop: Mapping transformation opportunities0 - Day 3: Implementation and Organizational Change• The capabilities needed for successful adoption
• Working across business, technical, and operational teams
• Managing change, resistance, and expectations
• Building momentum through early wins
• Practical activity: Assessing transformation readiness0 - Day 4: Risks, Governance, and Responsible Use• Ethics, bias, and fairness in AI adoption
• Data governance and privacy considerations
• Managing implementation and reputational risk
• Leadership responsibilities in oversight and control
• Case study: Responsible AI transformation in practice0 - Day 5: Leading Sustainable Transformation• Building a strategic roadmap for AI and data science
• Aligning transformation with business goals
• Creating capability, culture, and accountability
• Measuring progress and business impact
• Final group project: Presenting an AI-enabled transformation strategy0
Instructor
Lessons:
