Training Module
AI Limitations & Failure Modes
Build practical AI failure-mode literacy across predictive, generative and workflow systems
Overview
AI systems do not produce verified facts by default. Their outputs are shaped by data, labels, prompts, retrieved sources, model or service behaviour, integration choices, workflow conditions and human use. When those limits are not understood, organisations either over-trust AI or block useful AI for the wrong reasons.
This live module builds practical AI uncertainty and failure-mode literacy. Using Northstar Integrated Services as a running case, participants learn how failures arise across predictive AI, generative AI, data pipelines and socio-technical workflows, and how to challenge evidence, vendor claims and AI-polished wording with realistic judgement.
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 outputs as fallible signals
Where uncertainty enters AI systems
Predictive AI failure modes
Generative AI and retrieval failure modes
Data and pipeline failure modes
System and socio-technical failure modes
Evidence limits and downstream questions
Show detailed agenda...
Learning outcomes
Key outcomes
Explain why AI outputs are uncertain signals rather than verified facts
Trace where uncertainty enters across data, prompts, retrieval, model or service behaviour, integration, workflow and human use
Recognise common predictive, generative, data, pipeline and socio-technical AI failure modes in realistic artefacts
Additional capabilities
Challenge vendor claims, pilot results and AI-polished wording against the available evidence
Conduct a bounded failure-mode walkthrough without turning it into risk assessment or control design
Route failure-mode questions to AI inventory, risk management, operational control, monitoring, supplier assurance, audit or technical review
Materials
Learning materials
Slide deck
Participant workbook
Templates & tools
Practical, reusable artefacts to apply the module directly to your organisation.
AI uncertainty source map
Failure-mode pattern card set
Data-to-output failure walkthrough canvas
Evidence claim challenge checklist
Output variability and prompt sensitivity demo worksheet
Human-use and socio-technical failure lens guide
Downstream handoff question card set
Confirmation
Certificate of completion
Overview
Dates
Bespoke
Module ID
HAM-AI-DF-02
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 coding, data-science or model-engineering background is required. The module is designed for professionals who can work with high-level descriptions of digital services, data flows, SaaS tools, APIs and business workflows.
Helpful preparation includes basic AI system literacy: data, training versus inference, prompts, retrieval, models or AI services and common deployment patterns.
Preparatory modules
Supporting modules (optional)
Helpful if you want to deepen related skills, but not required to participate effectively.
