Training Module
AI Systems & Architectures
Core AI concepts, AI system types, AI agents, and the technical building blocks behind modern AI-enabled products and services
Overview
Organisations often adopt “AI” as an umbrella label that hides critical differences: rules vs. machine learning, predictive vs. generative behaviour, single-model services vs. agentic patterns, and 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, AI agents, and the technical building blocks that recur across most implementations (data pipelines, models, prompts, orchestration, 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?
Agenda
AI in organisations: what “AI” does and does not mean
Core concepts: data, models, inference, and prompts
AI system types, AI agents, and typical architectures
Data building blocks that matter for AI systems
Deployment patterns, orchestration, and operational context
Supporting technical controls around AI components
Case-based workshop
Show detailed agenda...
Learning outcomes
Key outcomes
Distinguish automation, analytics, machine learning, generative AI, and AI agents in organisational contexts
Explain how AI systems are built from data, models, inference, prompts, and orchestration
Identify core components, interfaces, and supporting technical controls in AI-enabled services and agentic workflows
Additional capabilities
Recognise common AI system types, agent patterns, and architectural structures
Explain how datasets, labels, splits, provenance, and prompts shape behaviour
Identify typical deployment, integration, and orchestration patterns
Formulate structured technical questions about AI components, tools, and agent flows
Materials
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 and agent patterns quick-reference sheet
AI system and agent component map
Data / model artefact primer
Question prompts for technical walkthroughs
Confirmation
Certificate of completion
Module ID
HAM-AI-DF-01
Discipline
Domains
Public delivery
Live virtual
Duration
7 h
List price
CHF 550
Excl. VAT. VAT may apply depending on customer location and status.
Delivery
Live virtual delivery
This module is delivered live online and combines conceptual framing, discussion, case work and direct interaction with the instructor.
A public cohort is currently not scheduled. If you register your interest, we will notify you when a new public cohort is scheduled or suitable delivery options become available.
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
Prerequisites & preparation
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
