Generative AI for Executives: Opportunities & Risks
Overview
Generative AI can draft, summarise, analyse and create at remarkable speed, but it also introduces new risks to accuracy, reputation and data. This course gives executives a clear, practical view of what generative AI can do for their organisation today, where it can go wrong, and how to move from experimentation to measurable value.
Who should attend
Executives, general managers, and senior leaders responsible for innovation, operations or digital strategy.
Training methodology
Facilitated discussion, practical frameworks, case studies from different sectors, group exercises, short demonstrations of AI tools, and an action plan for each participant's organisation.
Objectives
By the end of this course, participants will be able to:
- Explain how generative AI works in business terms
- Identify high-value use cases for their organisation
- Assess accuracy, security and reputational risks
- Set policies for staff use of generative AI
- Plan a phased adoption roadmap
Course outline
Day 1Generative AI Explained
- How large language models work
- Text, image, voice and code generation
- Current capabilities and limits
- Live demonstrations
- Arabic language capabilities
- Myths and realities
Day 2Opportunities Across the Business
- Customer service and marketing
- Finance, HR and legal
- Operations and knowledge management
- Prioritising use cases
- Executive productivity use cases
- Opportunity scoring exercise
Day 3Risks and Safeguards
- Hallucinations and accuracy
- Confidential data and security
- Copyright and reputational exposure
- Designing safeguards and review steps
- Shadow AI and unapproved tools
- Incident examples and lessons
Day 4Policy and Organisation
- Acceptable use policies
- Choosing tools and vendors
- Skills and training for staff
- Case study: enterprise rollout
- Open versus enterprise tools
- Roles and responsibilities
Day 5From Pilot to Value
- Designing pilots with clear success measures
- Scaling what works
- Measuring productivity and quality gains
- Executive adoption roadmap
- Budgeting for generative AI
- Leading by example
