Training Module
AI Systems & Architectures
Practical AI literacy for understanding AI types, deployment models, agentic patterns, current trends and where AI is heading
Overview
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.
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 reality, language and misleading labels
AI types and system patterns
Data, models, training and inference
Generative AI, prompts, retrieval and tools
Deployment models and organisational use cases
Copilots, agents and autonomy
Current trends and near-future direction
Show detailed agenda...
Learning outcomes
Key outcomes
Distinguish common AI types and explain what each one does in plain language
Explain core AI concepts such as data, models, training, inference, prompts, retrieval, tools and agents at a useful non-technical level
Recognise how AI appears in organisations through internal tools, SaaS features, APIs, existing applications and workflow automation
Additional capabilities
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
Learning materials
Slide deck
Participant workbook
Templates & tools
Practical, reusable artefacts to apply the module directly to your organisation.
AI plain-language glossary guide
AI type and capability map
Data, model, training and inference primer guide
Generative AI and retrieval explainer guide
AI deployment pattern map
Copilot, agent and autonomy scale guide
AI literacy question cards guide
Confirmation
Certificate of completion
Overview
Dates
Bespoke
Module ID
HAM-AI-DF-01
Discipline
Domains
Delivery
Live virtual delivery
This module is delivered live online and combines conceptual framing, discussion, case work and direct interaction with the instructor.
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.
Upcoming course runs
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.
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
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.
