ISO/IEC 42001:2023 Artificial Intelligence Management Systems Lead Implementer Training and Certification Program

Programme Summary

Practise leading a structured AI management implementation project against ISO/IEC 42001:2023. Working through an enterprise AI service deployment case, participants translate AI governance, lifecycle risks and impact assessment evidence into accountable controls, resources and measurable milestones. The programme develops an implementation portfolio, tests operational feasibility and prepares management review evidence to support responsible development, procurement and use of AI systems.

Training Objectives

1 Structure AI management implementation leadership; 2 Translate requirements into workable controls; 3 Coordinate ownership resources and project milestones; 4 Establish meaningful measures and review evidence; 5 Prepare implementation priorities for management approval.

Learning Outcomes

1 Build a AI management implementation roadmap; 2 Specify controls for unassessed AI impacts; 3 Design AI risk and impact assessments; 4 Assign accountable owners and measurable milestones; 5 Present a justified management review proposal.

Target Audience

AI Governance Managers; AI Product Managers; Technology Risk Managers; Implementation Project Managers; Management System Consultants.

Target Industries

Enterprise Software; Financial Services; Healthcare Technology; Government Digital Services; Industrial Automation.

Course Contents

• Module 1 Implementation Leadership and Project Mandate: Business case scope and intended benefits; Sponsor responsibilities and implementation team roles; Constraints resources and governance decisions.
• Module 2 Diagnostic Review and Implementation Priorities: Requirements mapping and baseline evidence; Risk based gaps and priority decisions; Implementation milestones and acceptance criteria.
• Module 3 AI Roles Context and System Scope: AI provider developer and user roles; System inventory and intended use context; Interested parties and organisational boundaries.
• Module 4 AI Leadership and Governance Planning: AI policy and accountable responsibilities; Objectives risks and management resources; Competence communication and documented information.
• Module 5 AI Risk Assessment and Treatment: Risk criteria and lifecycle risk scenarios; Risk analysis evaluation and treatment ownership; Control selection and applicability justification.
• Module 6 AI System Impact Assessment: Affected people groups and societal contexts; Intended use foreseeable misuse and consequences; Impact assessment evidence and review triggers.
• Module 7 AI Lifecycle Controls and Accountability: Design development and deployment responsibilities; Verification validation and operational acceptance; Human oversight and controlled lifecycle changes.
• Module 8 Data and Information Governance: Data provenance suitability and quality; Data preparation access and lifecycle controls; Documentation transparency and stakeholder information.
• Module 9 AI Suppliers Use and Monitoring: Supplier responsibilities and acquisition interfaces; Responsible use and deployment safeguards; Performance monitoring incident reporting and escalation.
• Module 10 AI Assurance and Improvement: Management system and control effectiveness measures; Internal audit and management review; Corrective action and changing AI risks.
• Module 11 Designing the Implementation Architecture: Process interactions and accountable control owners; Integration with existing management arrangements; Proportionate documentation and practical control design.
• Module 12 Operational Control Design Workshop: Preventing unassessed AI impacts; Addressing unclear human oversight responsibilities; Responding to unreliable AI data governance.
• Module 13 Building the Required Evidence Portfolio: AI risk and impact assessments; AI control applicability records; AI lifecycle and monitoring records.
• Module 14 Competence Communication and Organisational Adoption: Role based competence and learning needs; Operational communication and stakeholder engagement; Resistance escalation and management support.
• Module 15 Resources Suppliers and Implementation Scheduling: Resource estimates and implementation dependencies; External support and supplier accountability; Controlled milestones and progress reporting.
• Module 16 Piloting Controls and Managing Changes: Pilot objectives and realistic acceptance checks; Operational feedback and control adjustments; Approval communication and controlled change records.
• Module 17 Monitoring Performance and Demonstrating Effectiveness: Measurable indicators and reliable data sources; Control performance and exception reporting; Evidence based decisions and corrective action.
• Module 18 Internal Assurance and Management Review: Internal audit planning and independence; Review inputs decisions and resource commitments; Readiness gaps and continuing improvement priorities.
• Module 19 Maintaining the Management System: Ownership handover and ongoing monitoring; Changing risks obligations and operating conditions; Learning mechanisms and improvement programme.
• Module 20 Implementation Portfolio and Leadership Simulation: Present controls responsibilities and milestones; Defend priorities using supplied evidence; Respond to management challenge and feedback.

Assessment

• 100 single best answer questions, including applied scenarios; one mark per question.
• 150 minute invigilated digital examination with identity verification. Approved accessibility adjustments preserve the assessed competence.
• Clean standard text and supplied case documents permitted; internet, messaging and external AI tools prohibited.
• Individual applied assessment (90 minutes): Using an enterprise AI service deployment case, develop an AI management implementation roadmap and governance control portfolio, assign resources and milestones, demonstrate one control and defend implementation priorities in a brief panel review.
• Assessment criteria: Workable design of controls for unassessed AI impacts; Relevant evidence portfolio including AI risk and impact assessments; Feasible sequencing ownership and measurable implementation acceptance criteria.
• Pass each component separately: 75/100 written marks (75%) and at least 70% on the applied task.
• All mandatory criteria must pass: Do not fabricate evidence or knowingly present unsupported conformity conclusions.
• Individual results and feedback are provided. One reassessment may follow coaching under the published enrolment terms.

Certification

• Successful learners receive a TQCSI Arabia digital Certificate of Successful Completion.
• The certificate records the learner name, exact programme title, teaching hours, completion date and unique verification reference.
• Verified identity, full attendance and separate written and applied passes are required; all mandatory critical criteria must be met.
• Proposed issue target: within 30 calendar days of approved results and settlement of applicable fees.
• Records assessed implementation planning skills in AI management; it does not confer independent personnel certification or guarantee organisational certification.
• Attendance confirmation may record completed hours for unsuccessful learners; it does not certify successful completion.

Programme Profile

Number of daysNumber of daysNumber of days

555

Total teaching hoursTotal teaching hoursTotal teaching hours

35 hours; breaks, pre course work and formal final assessments excluded.35 hours; breaks, pre course work and formal final assessments excluded.35 hours; breaks, pre course work and formal final assessments excluded.

Delegates allowedDelegates allowedDelegates allowed

Maximum 20 delegates.Maximum 20 delegates.Maximum 20 delegates.

Training prerequisitesTraining prerequisitesTraining prerequisites

Working knowledge of ISO/IEC 42001:2023, relevant AI management experience and familiarity with project planning are required. Participants should understand process ownership and operational controls.Working knowledge of ISO/IEC 42001:2023, relevant AI management experience and familiarity with project planning are required. Participants should understand process ownership and operational controls.Working knowledge of ISO/IEC 42001:2023, relevant AI management experience and familiarity with project planning are required. Participants should understand process ownership and operational controls.

Pre course assignment and assessmentPre course assignment and assessmentPre course assignment and assessment

Complete a 45-minute gap diagnostic using supplied AI risk and impact assessments; identify priorities, process owners and implementation constraints.Complete a 45-minute gap diagnostic using supplied AI risk and impact assessments; identify priorities, process owners and implementation constraints.Complete a 45-minute gap diagnostic using supplied AI risk and impact assessments; identify priorities, process owners and implementation constraints.

End of course assessmentEnd of course assessmentEnd of course assessment

100 question written exam (150 minutes) and individual applied task (90 minutes).100 question written exam (150 minutes) and individual applied task (90 minutes).100 question written exam (150 minutes) and individual applied task (90 minutes).

Passing criteriaPassing criteriaPassing criteria

75% written and 70% applied, passed separately; full attendance. All mandatory criteria must pass.75% written and 70% applied, passed separately; full attendance. All mandatory criteria must pass.75% written and 70% applied, passed separately; full attendance. All mandatory criteria must pass.