Training Module
AI Fundamentals I
Core AI concepts, AI system types, and the technical building blocks behind modern AI-enabled products and services
Training module overview
Organisations often adopt “AI” as an umbrella label that hides critical differences: rules vs. machine learning, predictive vs. generative behaviour, model-in-a-box vs. AI as part of a wider socio-technical system. Without a clear mental model, governance discussions drift, requirements get misapplied, and assurance work focuses on surface artefacts rather than how the system actually operates.
This training module establishes practical AI literacy: key terms, AI system types, and the technical building blocks that recur across most implementations (data pipelines, models, interfaces, and supporting IT controls such as access management, encryption, and logging). It intentionally does not cover AI risk and harm assessment, lifecycle scoping and inventory methods, operational control design, or detailed failure-mode analysis—those are handled in the dedicated follow-up modules.
Applicable environments
This module applies to organisations for which artificial intelligence is relevant. It supports professionals who need a solid understanding of AI-related concepts, terminology, and context.
Target audience
AI management system managers and implementers working with technical teams
Governance, risk, and compliance professionals who need AI domain fluency
Product owners and process owners responsible for AI-enabled services
Auditors who need a shared baseline understanding of AI systems (not audit craft)
Anyone who wants to get a basic understanding of AI fundamentals
Decision support
Is this module for you?
It is a good fit if you…
want a clear, shared mental model of how AI systems actually work.
need to distinguish rules, ML, and generative AI beyond buzzwords.
work with AI systems and must ask better questions of technical teams.
need concept-level literacy to interpret AI risks, controls, or evidence.
want to reason about AI systems without relying on specific tools or vendors.
If most of the points above apply, this module is likely a good fit.
It may not be the best fit if you…
are looking for hands-on model building or coding exercises.
expect vendor- or platform-specific AI tooling training.
want ISO/IEC 42001 requirements, roles, or processes explained in detail.
already have deep technical AI expertise and seek advanced methods.
Agenda
AI in organisations: what “AI” does and does not mean
Core concepts: data, models, and inference
AI system types and typical architectures
Data building blocks that matter for AI systems
Deployment patterns and operational context (technical view)
Supporting technical controls around AI components
Case-based workshop
Show detailed agenda...
Learning outcomes
Key outcomes
Distinguish automation, analytics, machine learning, and generative AI in organisational contexts
Explain how AI systems are built from data, models, and inference
Identify core components, interfaces, and supporting technical controls in AI-enabled services
Additional capabilities
Recognise common AI system types and architectural patterns
Explain how datasets, labels, splits, and provenance shape behaviour
Identify typical deployment and integration patterns
Formulate structured technical questions about AI components
Additional benefits
Learning materials
Slide deck
Participant workbook
Templates & tools
Practical, reusable artefacts to apply the module directly to your organisation.
AI terminology and concept glossary
AI system types quick-reference sheet
AI system component map
Data / model artefact primer
Question prompts for technical walkthroughs
Confirmation
Certificate of completion
Module ID
HAM-AI-DF-01
Discipline
Domain
Audience
Auditor
Manager
Executive
Language
English
Delivery
Live virtual
Duration
7 h
List price
CHF 550
Excl. VAT. VAT may apply depending on customer location and status.
Delivery & learning format
Virtual live teaching
This module is delivered live, with a strong focus on discussion, practical application, and direct interaction with the instructor.
Sessions work through realistic examples, clarify concepts in context, and apply methods directly to participants’ organisational realities.
Custom delivery options
For organisations with specific constraints or learning objectives, the module can be adapted in format or scope, including in-house delivery and contextualised case material.
For an optimal learning experience
Preparation guidance
This module is designed as part of a modular training approach. Topics are deliberately distributed across modules and are not repeated in full, in order to avoid unnecessary redundancy. Each module is self-contained and can be taken on its own. Where prior knowledge or experience is helpful, this is indicated below so you can decide whether any preparation is useful for you.
Assumed background
This module assumes general professional familiarity with how organisations run systems and services. No prior AI background is required.
Helpful background includes:
Basic understanding of digital services (applications, APIs, data stores)
Familiarity with roles such as product, IT, security, and operations
