ISO IEC 42001 AI Management System Implementation Training Course

Course overview

Organisations are moving AI systems from experiments into customer service, recruitment, fraud detection, forecasting and decision support, often without a consistent way to assign accountability, assess impacts, control suppliers or evidence responsible operation. ISO/IEC 42001 provides a management-system framework for governing these activities, but implementation teams must translate its clauses and controls into operating processes, records and measurable objectives. This course helps professionals build an AI management system (AIMS) that can withstand internal scrutiny, customer due diligence and formal audit preparation.

Participants work through ISO/IEC 42001:2023 clause by clause, from organisational context and AIMS scope through leadership, risk-based planning, operational controls, performance evaluation and continual improvement. They learn to define AI roles and policy commitments; build an AI system inventory; conduct AI system impact assessments; identify and treat AI-specific risks; select applicable Annex A controls; establish supplier, data, transparency and human-oversight arrangements; and prepare audit-ready documented information. Related guidance from ISO/IEC 42005 and ISO/IEC 23894 is used to make impact assessment and risk treatment practical.

Instructor-led briefings are combined with a running implementation case, document reviews and group workshops. Participants complete an AIMS implementation pack containing a scope statement, interested-party register, risk and impact assessment, Statement of Applicability, control implementation plan, KPI set and internal-audit roadmap. The result is a usable starting point for a workplace implementation rather than notes on the standard alone.

The course suits professionals responsible for AI governance, risk, compliance, information security, data governance or delivery of AI-enabled products. It is equally useful for managers commissioning an AIMS programme who need to define the work, resources, evidence and decisions required before pursuing certification.

Course objectives

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

• Interpret ISO/IEC 42001:2023 clauses and Annex A controls for an AI management system implementation
• Define an AIMS scope, organisational context and interested-party requirements register
• Draft an AI policy with accountable roles, governance responsibilities and measurable objectives
• Build an AI system inventory and classify systems by purpose, lifecycle stage and risk exposure
• Conduct an AI system impact assessment using ISO/IEC 42005 assessment criteria
• Create an AI risk register and treatment plan using ISO/IEC 23894 risk-management principles
• Produce a Statement of Applicability linking Annex A controls to implementation evidence
• Plan internal audits, management reviews and corrective actions for continual AIMS improvement

Benefits of attending

Target competencies

Who should attend

• AI Governance Managers — who must establish accountable controls and evidence across an AI portfolio
• Risk and Compliance Managers — who need to translate AI obligations into auditable management-system processes
• Data Protection Officers — who must align privacy, impact assessment and AI governance activities
• Information Security Managers — who need to integrate AI controls with existing ISO/IEC 27001 practices
• Data and AI Product Managers — who must embed governance requirements into AI system delivery and operation
• Internal Auditors and Management System Leads — who need to assess AIMS readiness and plan assurance activity

Requirements and prerequisites

Participants should understand how their organisation develops, procures or uses AI-enabled systems and be able to discuss one real or representative use case. Familiarity with basic risk concepts, process ownership, policies, controls and management-system terminology is helpful, particularly for teams already using ISO 9001 or ISO/IEC 27001. No prior ISO/IEC 42001 knowledge is required, and participants do not need to be data scientists, programmers or AI model developers. Complete beginners should expect to work with practical governance documents and risk scenarios rather than learn machine-learning mathematics or code.

Training methodology

The five days combine instructor-led interpretation of ISO/IEC 42001 requirements with a single AI-service case that develops throughout the programme. Participants examine sample policies, inventories, risk registers, impact assessments, supplier clauses and audit evidence, then build and critique equivalent artefacts in small groups. Facilitated discussions focus on implementation decisions that differ by AI use case, such as human oversight, data provenance and transparency. The final session converts course outputs into a prioritised workplace action plan with owners, dependencies and first 90-day milestones.

Course outline

Day 1: ISO/IEC 42001 foundations and AIMS scope

• ISO/IEC 42001:2023 purpose, structure and certification context
• AI management system lifecycle and Plan-Do-Check-Act model
• ISO/IEC 22989 AI concepts and stakeholder terminology
• Organisational context, strategic drivers and AI governance boundaries
• Interested parties, legal obligations and customer assurance expectations
• Defining the AIMS scope across products, business units and suppliers
• Leadership commitments, AI policy and accountable governance roles

Workshop: Participants define an AIMS scope and interested-party register for a case organisation, including justified inclusions, exclusions and governance owners.

Day 2: AI inventory, impact assessment and risk planning

• AI system inventory fields and lifecycle ownership model
• AI system classification by intended use, autonomy and affected parties
• ISO/IEC 42005 AI system impact assessment process
• Impact criteria for individuals, groups, society and organisations
• ISO/IEC 23894 AI risk-management principles and risk sources
• Risk register construction, scoring scales and treatment decisions
• AIMS objectives, implementation plans and resource allocation

Workshop: Participants create an AI system inventory entry, impact assessment and prioritised risk treatment plan for a high-impact case system.

Day 3: Operational controls and documented information

• Annex A control structure and Statement of Applicability method
• AI system lifecycle controls from design through retirement
• Data governance, data provenance and data quality evidence
• Human oversight, transparency and information for affected parties
• AI system testing, validation and change-management controls
• Third-party AI supplier due diligence and contractual control requirements
• Document control, retention and traceability of AIMS evidence

Workshop: Participants build a Statement of Applicability excerpt and evidence map for selected Annex A controls in the case organisation.

Day 4: Measurement, assurance and improvement

• Operational performance measures and AI governance KPIs
• Monitoring AI system performance, drift and control effectiveness
• Incident reporting, escalation and AI-related corrective action
• Internal audit programme design and auditor evidence sampling
• Management review inputs, decisions and action tracking
• Nonconformity analysis and root-cause methods for AIMS issues
• Integrating ISO/IEC 42001 with ISO/IEC 27001 and ISO 9001 systems

Workshop: Participants conduct a mini internal audit against a case evidence pack and write findings, corrective actions and management-review inputs.

Day 5: Implementation roadmap and audit readiness

• AIMS gap assessment against clauses 4 to 10 and Annex A
• Prioritising implementation work by risk, dependency and business value
• Defining workstream owners, RACI responsibilities and governance forums
• Evidence collection plan for stage-one and stage-two audit readiness
• Common implementation failures and control design weaknesses
• Communicating the AIMS business case to executives and delivery teams
• Ninety-day implementation planning and continual-improvement backlog

Workshop: Participants consolidate their implementation pack into a 90-day AIMS roadmap with milestones, named owners, required evidence and executive decisions.

Tools & standards covered

ISO/IEC 42001:2023 Artificial intelligence management system, ISO/IEC 42005 AI system impact assessment guidance, ISO/IEC 23894 Artificial intelligence risk management guidance, ISO/IEC 27001 Information security management systems

A typical training day

Live online deliveries follow the same structure in the East Africa Time zone, with shorter screen blocks and longer breaks.

What the fee includes

How you can take this course

Certification

Participants who complete the full five days receive the Skillset Development Certificate of Completion, stating the course title, course code, dates and delivery format — suitable for professional-development records and employer reimbursement.

Frequently asked questions