Artificial intelligence now influences decisions about credit, hiring, healthcare, security and public services. When these systems are biased, opaque or poorly secured, the harm falls on individuals first and on the deploying organisation soon after, through complaints, regulatory action, lost contracts and damaged trust. Responsible AI is therefore a management problem as much as a technical one, and it needs policies, roles, controls and evidence in the same way that information security does.

The programme is designed in two linked halves. The first five days establish shared understanding: how AI systems are built and fail, what fairness, explainability, accountability and human oversight mean in practice, and how major frameworks and laws shape obligations. The second five days focus on delivery. Participants learn how an AI management system under ISO/IEC 42001 is structured, how to define scope and context, assign responsibilities, assess risks and impacts, select controls, manage third-party and generative AI use, monitor performance, handle incidents and prepare for internal and external audit.

Work is hands-on throughout. Participants build an AI inventory for a sample organisation, complete risk and impact assessments, draft policies and a statement of applicability, and finish with an implementation plan for their own institution that includes resourcing, timelines and board reporting.

By the end of the course, participants will be able to:

• Explain how AI systems are developed, deployed and monitored, and where governance fits in the lifecycle.
• Apply principles of fairness, transparency, accountability and human oversight to real use cases.
• Compare the NIST AI Risk Management Framework, the EU AI Act and ISO/IEC 42001 and decide how each applies.
• Build and maintain an inventory of AI systems with owners, purposes and risk tiers.
• Carry out AI risk and impact assessments covering bias, privacy, security and misuse.
• Draft AI policies, roles and a responsibilities matrix for the organisation.
• Design controls for suppliers, generative AI tools and model changes.
• Plan the implementation, internal audit and certification readiness of an AI management system.

The course is meant for leaders and specialists who must put AI under proper control, including:

• Compliance, risk and governance managers
• Chief data, information and technology officers
• AI, data science and product owners
• Legal counsel and data protection officers
• Internal auditors and information security managers
• Digital transformation leads in government agencies
• Bank, insurer, telecom and health sector innovation heads
• Consultants advising on AI policy and certification
• Board and audit committee advisers

Participants leave the course with:

• An AI system inventory and risk tiering template.
• A completed sample AI impact and risk assessment.
• A draft AI policy set with roles and escalation paths.
• A gap analysis against ISO/IEC 42001 requirements.
• A supplier and generative AI use checklist.
• An internal audit programme and evidence list.
• A phased implementation roadmap and board briefing outline.

The ten days alternate between concept sessions and implementation labs so that learning turns into deliverables. The approach includes:

• Expert briefings on responsible AI, regulation and the standard
• A running case organisation used for inventory, risk and control exercises
• Template-driven labs for policies, assessments and audit evidence
• Simulations of AI incidents and certification audit questioning
• A closing project presented to a mock board and refined through peer feedback

Participants who attend the full programme and complete the labs and final project are awarded a CPD-accredited Certificate of Completion by Vision Reach Global Consultancy. The certificate evidences training and is separate from any formal certification of an organisation's management system.