Training Module
Training Module
AI Fundamentals 1
Learn core AI concepts, AI system types, and the technical building blocks that underpin modern AI-enabled products and services
Understand
Implement
Manage
Audit
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 full-day domain fundamentals 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.
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 full-day domain fundamentals 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.
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)
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)
Agenda
AI in organisations: what “AI” does and does not mean
Automation, analytics, machine learning, and generative AI: clean distinctions
Where AI typically sits in products and decision processes
Core concepts: data, models, and inference
Training vs. inference; features, embeddings, prompts (as applicable)
Model families at a practical level (classification, regression, ranking, generation)
AI system types and typical architectures
Rule-based, ML-based, generative, and hybrid systems
Components and interfaces: pipelines, services, human touchpoints
Data building blocks that matter for AI systems
Dataset types, labels, provenance, and data splits (conceptual overview)
Data transformations and dependencies across the AI stack
Deployment patterns and operational context (technical view)
API services, embedded/edge use, workflow integration, and human-in-the-loop patterns
Monitoring signals and system dependencies (without prescribing governance methods)
Supporting technical controls around AI components
Access management, encryption, key handling, secrets, and configuration basics
Logging, traceability signals, and change points across pipelines and services
Workshop: map an AI-enabled service from a case
Identify system type, main components, and key data/model interfaces
Derive the minimal set of “what to ask for” artefacts (technical, not risk or inventory)
AI in organisations: what “AI” does and does not mean
Automation, analytics, machine learning, and generative AI: clean distinctions
Where AI typically sits in products and decision processes
Core concepts: data, models, and inference
Training vs. inference; features, embeddings, prompts (as applicable)
Model families at a practical level (classification, regression, ranking, generation)
AI system types and typical architectures
Rule-based, ML-based, generative, and hybrid systems
Components and interfaces: pipelines, services, human touchpoints
Data building blocks that matter for AI systems
Dataset types, labels, provenance, and data splits (conceptual overview)
Data transformations and dependencies across the AI stack
Deployment patterns and operational context (technical view)
API services, embedded/edge use, workflow integration, and human-in-the-loop patterns
Monitoring signals and system dependencies (without prescribing governance methods)
Supporting technical controls around AI components
Access management, encryption, key handling, secrets, and configuration basics
Logging, traceability signals, and change points across pipelines and services
Workshop: map an AI-enabled service from a case
Identify system type, main components, and key data/model interfaces
Derive the minimal set of “what to ask for” artefacts (technical, not risk or inventory)
Course ID:
HAM-AIF-1
Audience:
Auditor
Manager
Executive
Domain:
Artificial Intelligence
Available in:
English
Duration:
7 h
List price:
CHF 550
Excl. VAT. VAT may apply depending on customer location and status.
What you get
Learning outcomes
Distinguish automation, analytics, machine learning, and generative AI in practical terms
Classify common AI system types and recognise typical architecture patterns
Explain (at an implementation-relevant level) how data, training, and inference relate
Identify the main technical components and interfaces in an AI-enabled service
Describe common deployment patterns and where AI integrates into workflows and decisions
Explain how access management, encryption, and logging typically apply across AI components
Communicate with technical teams using shared terminology and precise questions
Distinguish automation, analytics, machine learning, and generative AI in practical terms
Classify common AI system types and recognise typical architecture patterns
Explain (at an implementation-relevant level) how data, training, and inference relate
Identify the main technical components and interfaces in an AI-enabled service
Describe common deployment patterns and where AI integrates into workflows and decisions
Explain how access management, encryption, and logging typically apply across AI components
Communicate with technical teams using shared terminology and precise questions
Learning materials
Slide deck
Participant workbook
Certificate of completion
Slide deck
Participant workbook
Certificate of completion
Templates & tools
AI terminology and concept glossary (participant reference)
AI system types quick-reference sheet
AI system component map (conceptual architecture canvas)
Data / model artefact primer (what it is, where it appears, why teams use it)
Question prompts for technical walkthroughs (for implementers and auditors)
AI terminology and concept glossary (participant reference)
AI system types quick-reference sheet
AI system component map (conceptual architecture canvas)
Data / model artefact primer (what it is, where it appears, why teams use it)
Question prompts for technical walkthroughs (for implementers and auditors)
Prerequisites
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
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
Continuous learning
Follow-up modules
Follow-up modules
After completion of this module, the following modules are ideal to further deepen the participant's competence.
After completion of this module, the following modules are ideal to further deepen the participant's competence.
Ready to achieve mastery?
Bring ISO requirements into everyday practice to reduce avoidable issues and strengthen the trust of your customers and stakeholders.
Ready to achieve mastery?
Bring ISO requirements into everyday practice to reduce avoidable issues and strengthen the trust of your customers and stakeholders.
Ready to achieve mastery?
Bring ISO requirements into everyday practice to reduce avoidable issues and strengthen the trust of your customers and stakeholders.