Training module
AI Systems & Architectures
Practical AI literacy for understanding AI types, deployment models, agentic patterns, current trends and where AI is heading
Make sense of AI as it changes
AI now appears in products, workflows, SaaS tools, APIs, copilots and agentic proposals. This module builds a clear, practical mental model of what AI is, which types exist, how modern AI systems work and what trends are likely to matter next.
Overview
What this module is about
AI is no longer a single specialist topic. It appears in search, office tools, customer service, forecasting, software products, workflow automation, embedded SaaS features and agentic proposals. The same word can describe simple automation, predictive models, generative AI, retrieval-enhanced assistants or systems that call tools and trigger actions.
This live module builds practical AI literacy for executives, managers, professionals and interested participants. Using Northstar Integrated Services as a running example, you learn to distinguish AI types, understand core concepts such as data, models, training, inference, prompts, retrieval, deployment models and agents, and interpret current AI trends without becoming dependent on hype or vendor language.
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…
want a clear, practical understanding of what AI is and what it is not.
need to distinguish automation, analytics, machine learning, generative AI, copilots and agents.
are an executive, manager or professional who needs to follow AI discussions without becoming an engineer.
want to understand how AI appears through SaaS products, APIs, internal tools and existing applications.
need a grounded view of current AI trends and likely near-future developments.
It may not be the best fit if you…
are looking for hands-on coding, model building or data-science exercises.
expect vendor- or platform-specific AI tooling training.
need a detailed AI risk assessment, audit, legal compliance or control-design module.
already have deep technical AI expertise and want advanced architecture or model-engineering methods.
What to bring
No prior AI background is required. The module is designed for executives, managers, professionals and interested participants who want practical AI literacy.
Helpful background includes general familiarity with digital tools, software products or organisational workflows, but the course does not assume coding, data-science or AI engineering knowledge.
Agenda
What is taught
7 parts01AI reality, language and misleading labels
Separate real capability from ordinary automation, analytics and marketing language
Use plain-language terms for common AI concepts without relying on hype
02AI types and system patterns
Compare rules, automation, analytics and predictive machine learning
Distinguish generative AI, retrieval-enhanced systems, copilots and agentic proposals
03Data, models, training and inference
Trace data, model, training, inference, input and output concepts in simple examples
Recognise where human use shapes the practical behaviour of an AI-enabled feature
04Generative AI, prompts, retrieval and tools
Connect prompts, context, retrieved sources and generated content
Recognise where tool use and user checking change what a generative AI system can do
05Deployment models and organisational use cases
Compare in-house builds, SaaS features, APIs, standalone tools and existing application integrations
Link deployment models to visibility, dependency, configuration options and change exposure
06Copilots, agents and autonomy
Place support, copilot assistance, tool use and delegated action on an autonomy scale
Spot when an agentic label describes real workflow action rather than marketing language
07Current trends and near-future direction
Interpret trends in multimodal AI, reasoning models, agents and smaller models
Discuss likely effects of on-device AI, SaaS AI features and synthetic media over the next few years
Outcomes
Learning outcomes
01
Distinguish common AI types and explain what each one does in plain language
02
Explain core AI concepts such as data, models, training, inference, prompts, retrieval, tools and agents at a useful non-technical level
03
Recognise how AI appears in organisations through internal tools, SaaS features, APIs, existing applications and workflow automation
Compare predictive AI, generative AI, retrieval-enhanced assistants, copilots and agentic patterns
Describe how deployment models affect visibility, configuration options, control and dependency
Interpret current AI trends and near-future direction without relying on hype or vendor language
Ask practical questions about AI capability, autonomy, data, provider dependency and change
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 Systems & Architectures 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-DF-01
- Discipline
- Artificial Intelligence
- Domains
- Transformation
- Part of tracks
- Management System Auditor · 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.