
AI for EXECUTIVES, directors & the board
AI Safety and Risk Management Course for Executives & Boards
Gain insight into AI safety, risk management, governance frameworks, ethics, biases and change management.
Effective AI risk management involves a systematic approach to identifying, evaluating, and mitigating AI risks within an organisation. It is essential for protecting sensitive data, ensuring compliance, and maintaining seamless AI operations. It also fosters innovation, provides a competitive advantage, and bolsters supply chains.
What you'll learn
Executive AI Strategy and Impact: High-level sessions focused on understanding AI’s impact on business strategy and competitive positioning.
AI Integration and Risk Management for Leaders: Training on safely implementing AI within business processes, including risk mitigation and ethical considerations.
Change Management for AI Adoption: Training on guiding teams through the AI adoption journey, fostering a culture of innovation, and addressing organisational challenges.

Course Overview
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Overview of AI's impact on competitive positioning
Case studies on successful AI-driven business transformations
Identifying strategic opportunities for AI in the organisation
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Frameworks for AI adoption and integration
Alignment of AI initiatives with existing business processes and goals
Steps to ensure effective implementation across departments
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Identifying and assessing risks specific to AI adoption
Risk mitigation strategies for AI-related threats
Building and maintaining governance frameworks for AI
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Ethical considerations in AI applications
Guidelines and best practices for ethical AI deployment
Understanding biases and fairness in AI systems
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Setting up governance models for AI projects
Navigating regulatory requirements and compliance standards
Establishing roles and responsibilities for AI oversight
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Leading organisational change to foster AI adoption
Overcoming resistance and promoting a culture of innovation
Best practices for engaging and training employees in AI use
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Defining and tracking key performance indicators (KPIs) for AI initiatives
Assessing ROI and other metrics to evaluate success
Continuous improvement strategies for long-term AI value
