1. Programme Overview

This programme provides senior leaders with practical tools, frameworks, and decision architectures to improve the governance of AI-enabled systems across their organisations. The focus is on systemic risk, human oversight, and operational governance, rather than model development or technical optimisation.

2. Target Audience

• Senior executives responsible for AI-enabled operations

• Risk, Compliance, Technology, and Transformation leaders

• Heads of Governance, Controls, and Enterprise Risk

• Leaders accountable for AI adoption outcomes

3. Total Learning Time

6 CPD Hours. Delivered as an intensive workshop or modular programme

4. Learning Objectives

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

1. Map AI-enabled systems as socio-technical processes, identifying where risk, accountability, and decision failure can arise.

1. Apply practical governance tools to assess and control AI-enabled system risks beyond model performance (data, process, people, controls, outcomes).

1. Design or refine accountability, decision rights, and oversight mechanisms for AI-enabled systems, including human-in-the-loop requirements.

1. Integrate AI-enabled system risks into existing enterprise risk, controls, and assurance frameworks (risk taxonomy, controls, monitoring, auditability).

1. Establish thresholds and escalation routes for AI-related incidents, drift, harm, or non-compliance.

1. Develop an implementation roadmap to strengthen AI governance capability appropriate to organisational maturity and risk appetite.

5. Programme Structure (High-Level Agenda)

• Module 1: AI-Enabled Systems as Socio-Technical Risk

• Module 2: Governance Tools for AI Risk Identification and Oversight

• Module 3: Human-in-the-Loop, Accountability, and Decision Rights

• Module 4: Risk Taxonomies, Controls, and Assurance for AI Systems

• Module 5: Leadership Decision Architecture and Escalation Design

• Module 6: Practical Application to Organisational Use Cases

6. Learning Methods

• Framework-based teaching

• Applied governance tool walkthroughs

• Facilitated group analysis

• Practical exercises using participant-relevant AI use cases

• Guided reflection on leadership and accountability implications

7. Assessment / Engagement

Participant engagement is demonstrated through:

• Completion of applied governance and risk exercises

• Participation in facilitated discussions and peer learning

• Reflection on how tools apply to their organisational context

No formal testing; emphasis is on applied understanding and leadership judgement.

8. Quality Assurance

• Programmes designed and delivered by subject-matter experts in AI risk and governance

• Content updated to reflect regulatory, technological, and organisational developments

• Continuous improvement informed by participant and client feedback