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.
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
- Part of tracks
- Management System Auditor · Management System Manager
Case organisation
You work inside a company that already has the problem
Exercises run on one case organisation, carried across modules rather than restarted, so what you build here is what the next module finds.
Northstar Integrated Services AG is a group headquartered in Zurich, providing digital operations platforms and managed services to regulated organisations across Europe. It has not always been that. It began in 2008 as a field-operations firm of about twenty-five people, and what first forced documented decisions and named accountability on it was not growth but a single regulated customer. It now runs an acquired business in Poland and the Czech Republic through a subsidiary that kept its own legal identity, which is where the interesting failures live: group instruments rolled out operationally and never put in force by the governing bodies of the subsidiary itself.
Linked registers
Organisation and people, risks, objectives, policies and documents, findings and reviews, third parties, processes
Not a case study
Nothing is summarised for you; the evidence is where it would really be
It has a history
The organisation has a timeline, and modules enter it at different points, so a structure can be studied before it broke as well as after
It crosses borders
A Swiss parent, an acquired operating business in Poland and the Czech Republic under its own legal entity, and customers in several jurisdictions
Why it matters
Judgement is not trained on tidy examples, and it is not trained on a fresh one each week. Northstar is deliberately untidy, and modules enter it at different points of its history, so you see a governance structure being built, outgrown and rebuilt rather than a finished one. What you decide in one module is what the next one finds.
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.