AI Leadership and Governance Training Course

Participants can assess and prioritise AI opportunities using value, feasibility and risk evidence.

• Frame AI use cases through problem statements, users and decision outcomes
• Build benefits cases using revenue, cost, risk and service measures
• Score opportunities with value-feasibility-risk prioritisation matrices
• Separate automation candidates from decision-support and generative AI applications
• Apply stage gates from discovery through scaled operational deployment
• Balance quick wins against strategic capabilities, dependencies and control maturity

Participants can specify data governance requirements that support reliable and lawful AI use.

• Trace training, validation and operational data through lineage records
• Assess data quality using completeness, accuracy, timeliness and representativeness measures
• Assign data owners, stewards and custodians using DAMA role definitions
• Document lawful basis, retention and purpose limitations for personal data
• Detect proxy variables that may create unfair or inappropriate outcomes
• Establish data access controls for development, testing and production environments

Participants can define governance controls across the AI system lifecycle.

• Specify model requirements, acceptance criteria and prohibited-use conditions
• Separate development, validation and approval responsibilities through three lines
• Document model assumptions, limitations, dependencies and intended user groups
• Test performance using holdout data, stress tests and error analysis
• Approve releases through change control, versioning and rollback procedures
• Retire models using archive, access removal and records retention controls

Participants can identify, assess and treat AI risks using structured control methods.

• Apply NIST AI RMF Map, Measure and Manage functions
• Construct AI risk registers with causes, controls, owners and indicators
• Analyse threat scenarios including prompt injection, data poisoning and model theft
• Set risk appetite thresholds for accuracy, autonomy, privacy and customer impact
• Design incident triage for harmful outputs, failures and unauthorised access
• Monitor model drift, data drift and control exceptions through key indicators

Participants can establish responsible AI requirements and oversight mechanisms for high-impact uses.

• Translate OECD AI Principles into organisational policy requirements
• Assess fairness through subgroup testing, disparate impact and error-rate comparisons
• Define meaningful human oversight before, during and after automated decisions
• Prepare transparency notices explaining purpose, data use and decision limitations
• Apply EU AI Act risk categories to proposed systems
• Escalate unacceptable uses through ethics review and executive approval routes

Participants can design an operating model that assigns AI responsibilities and supports adoption.

• Compare centralised, federated and hub-and-spoke AI operating models
• Define product owner, model owner and accountable executive responsibilities
• Establish AI governance forums, charters, agendas and decision records
• Plan workforce impacts using task analysis, role redesign and skills matrices
• Set acceptable-use guidance for employee generative AI tools
• Build reporting routes for concerns, override decisions and incident learning

Participants can evaluate AI suppliers and set contractual assurance requirements.

• Conduct AI vendor due diligence across data, security and governance
• Request model documentation, evaluation evidence and known limitation disclosures
• Assess supplier claims through test protocols and independent assurance evidence
• Specify contract clauses for data use, audit rights and incidents
• Control subcontractors, model updates and cross-border data processing arrangements
• Plan exit arrangements for portability, records access and service continuity

Participants can measure AI performance and communicate governance evidence to senior decision-makers.

• Define outcome metrics linking AI outputs to business performance
• Combine accuracy, fairness, safety and user-adoption measures in dashboards
• Create key risk indicators for incidents, overrides and control failures
• Schedule independent validation, internal audit and management review activities
• Report material AI risks using board-ready thresholds and trend analysis
• Challenge benefit realisation assumptions through post-implementation review methods

Participants can prepare a practical AI governance action plan for their organisational priorities.

• Diagnose current governance maturity using ISO/IEC 42001 control domains
• Prioritise control improvements by risk exposure, effort and business dependency
• Sequence policy, process, technology and workforce actions over ninety days
• Assign named owners, milestones, evidence requirements and escalation points
• Prepare stakeholder communications for executives, delivery teams and affected users
• Review implementation barriers through assumptions, dependencies and decision logs