Training module
AI System Lifecycle & Inventory
Define AI system scope, set lifecycle boundaries, and maintain an AI system inventory aligned with ISO/IEC 42001
Create the reliable object of AI governance
AI governance depends on knowing which AI systems exist, where their boundaries sit, who owns them, what they depend on and when their records must change. This module shows how to build and maintain that inventory as practical ISO/IEC 42001 implementation evidence.
Overview
What this module is about
AI inventories often start as lists of tools, pilots, vendor features and use cases. They look complete until a real governance question appears: what exactly is the AI system, where does its boundary sit, who owns the record, and which change should trigger review?
This module teaches AI system lifecycle and inventory work as a practical management-system capability. Participants work through the evolving Northstar case to identify candidate AI systems, separate systems from components and ordinary automation, define boundaries and context of use, record lifecycle state, assign ownership, surface supplier and data dependencies, and maintain traceability to risk, controls, monitoring and assurance. The focus is implementation judgement, not generic AI awareness, legal classification, model development or MLOps.
Audience
Who it is for
Management system implementers and coordinators
Executives and department heads accountable for management system performance
Those responsible for processes, policies, assets, risks and controls
Auditors seeking insight into management-side practice, not audit technique
Consultants working on management system design, governance or improvement
Decision supportIs this module for you?
It is a good fit if you…
need to decide what counts as an AI system in your organisation.
work with AI pilots, embedded vendor features, GenAI use cases or regional tool variation.
need a maintained AI inventory that risk, controls, monitoring and assurance can rely on.
support ISO/IEC 42001 implementation, readiness, governance, audit preparation or customer assurance.
want practical templates for boundary, lifecycle, ownership and update-trigger decisions.
It may not be the best fit if you…
are primarily looking for general AI awareness or AI architecture fundamentals.
need a full AI risk, impact or harm assessment method.
want detailed operational control design, model validation, prompt engineering or MLOps practice.
need legal advice or a dedicated AI Act classification course.
already maintain a current, traceable AI inventory with clear boundaries, owners and review triggers.
What to bring
No formal prerequisites. Participants should be ready to work with practical management-system concepts such as scope, ownership, registers, documented information and change control. The module introduces the AI system framing and inventory terminology needed for the exercises.
Helpful background includes:
Familiarity with how organisations operate digital services, suppliers and internal tools
Basic awareness that AI capabilities may be built, bought, embedded in vendor tools or used through GenAI services
Comfort discussing roles, responsibilities, evidence and review triggers without needing technical AI depth
Agenda
What is taught
6 parts01AI system discovery and candidate triage
Separate AI systems from components, ordinary automation and informal tool use
Triage pilots, embedded vendor features and GenAI use cases
Clean duplicate records and mark unresolved candidates for follow-up
02AI system boundaries and context of use
Distinguish AIMS scope from object-level AI system scope
Map purpose, workflow, data, model or service, user role and human decision point
Record supplier interfaces and regional variation without turning the module into architecture training
03Lifecycle checkpoints and inventory evidence
Place systems in proposal, pilot, controlled deployment, change, monitoring, suspension or retirement states
Connect lifecycle state to evidence, approval and next decision needs
Recognise when pilot language hides real operational use
04Ownership, dependencies and supplier interfaces
Assign ownership for business purpose, system record, data, supplier interface and monitoring
Surface vendor AI features, model/service changes, configuration and retained responsibility
Define who must report changes that affect the inventory
05Inventory metadata that supports decisions
Design a lean inventory record that supports risk, controls, monitoring and assurance
Repair weak inventory entries with source links and quality rules
Use AI support to draft, compare and challenge records with human verification
06Change triggers, traceability and assurance limits
Route vendor, data, model/service, configuration and business-use changes to the right review action
Check traceability from inventory to risk assessment, controls, monitoring and management review
Prepare customer-safe assurance wording without overclaiming inventory completeness
Outcomes
Learning outcomes
01
Identify AI systems and unresolved candidates across pilots, embedded vendor features, automation and GenAI use
02
Define AI system boundaries, context of use and lifecycle state with enough evidence for downstream governance work
03
Build and maintain an AI inventory record with ownership, dependencies, metadata and update triggers
Distinguish between AIMS scope, AI system scope, AI components and AI-enabled work practices
Use lifecycle checkpoints to route proposal, pilot, deployment, change, suspension and retirement decisions
Check traceability from inventory to AI risk assessment, controls, monitoring, management review and assurance claims
Materials
The content and the assessment
Written module
The full content in writing, complete in itself. Videos are recorded for parts of it as an alternative way through, and the written module always carries everything.
Exercises
Graded work on the case organisation's own registers: structured answers checked against the encoded case, written answers scored against a rubric traced to it, with a trainer holding the final word on every assessed item.
On completion
The assessed exercises must be passed before the certificate is issued.
Scheduling
No public run of AI System Lifecycle & Inventory is scheduled at the moment. Tell us you are interested and we will let you know when the next one opens, or discuss running it in-house.
Module facts
- Module ID
- HAM-AI-S-01
- Discipline
- Artificial Intelligence
- Domains
- Governance
- Part of tracks
- Management System Manager
Case organisation
All modules share one company
Each module enters the company at a particular point in its history and finds it as that history has left it.
Northstar Integrated Services AG is a group headquartered in Zurich, operating in six European countries, with about 1,350 employees at the end of 2025. It provides digital operations platforms and managed operational services to regulated organisations in healthcare, the public sector and industrial services. Founded in 2008 as a field-operations firm serving business customers, it had about twenty-five employees when its first regulated customer, a hospital group, required documented decisions, named accountability and evidence on request. It moved into the platform business in 2016 and in 2024 acquired an operating business in Poland and the Czech Republic, whose legal entity continues inside the group.
Linked registers
Organisation and people, roles, risks, objectives, policies, documents and records, communications, findings, audits and reviews, interested parties, processes
Records, not summaries
The evidence sits in the registers and documents where a real organisation would keep it.
A history
The company has a timeline from 2008, so a structure can be examined before and after it changed.
More than one legal entity
A Swiss parent company and an acquired subsidiary in Poland and the Czech Republic, with customers in several jurisdictions
Why it matters
Judgement develops on material that is incomplete and partly contradictory, as it is in practice. Northstar is built that way on purpose. Because modules enter its history at different points, a governance structure can be seen while it is built, when it is outgrown and when it is rebuilt.
Delivery & dates
How this module reaches you
Delivered live online, combining conceptual framing, discussion, case work and direct interaction with the trainer. In-house and contextualised delivery is available on request.
No public run is scheduled at the moment. Most modules run on request as well as on the public calendar, so tell us the timing you need.
Want this module scheduled?
We will tell you when the next run is scheduled, or run this module in-house with your own case material.
Not sure it is the right module?
Describe your context in a short message and we will tell you honestly.