Training module
AI Limitations & Failure Modes
Build practical AI failure-mode literacy across predictive, generative and workflow systems
Rely on AI only when the evidence holds up
Build practical judgement about AI uncertainty, output fallibility and common failure modes across predictive, generative and AI-enabled workflow systems.
Overview
What this module is about
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.
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 to understand why AI outputs should be treated as fallible signals rather than verified facts.
need to recognise common AI failure modes in practical business, product, risk, compliance or audit conversations.
work with predictive AI, generative AI, vendor AI features or AI-enabled workflows and need better questions.
want to challenge vendor claims, pilot evidence and AI-polished wording without becoming an AI engineer.
are preparing for AI inventory, AI risk management, operational control, supplier assurance or audit work.
It may not be the best fit if you…
need a full AI risk assessment, harm assessment or control-design course.
expect coding, prompt engineering craft, MLOps configuration or model tuning.
want statistical model validation, red teaming or advanced AI safety methods.
need legal classification or formal compliance advice for specific AI Act obligations.
What to bring
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.
Agenda
What is taught
7 parts01AI outputs as fallible signals
Separate AI output from verified fact, source evidence and unsupported claim
Use Northstar examples to calibrate useful trust without blind reliance or blanket rejection
02Where uncertainty enters AI systems
Trace uncertainty through data, labels, prompts, retrieval, model or service behaviour and integration
Connect workflow conditions and human use to practical reliability limits
03Predictive AI failure modes
Recognise generalisation limits, spurious correlations and threshold trade-offs
Challenge pilot performance, false positives, false negatives and context-shift assumptions
04Generative AI and retrieval failure modes
Diagnose hallucination, unsupported claims, prompt sensitivity and overconfident wording
Check whether retrieved sources are current, relevant and sufficient for the answer
05Data and pipeline failure modes
Identify data leakage, training-serving skew, missingness and weak provenance
Read drift-like monitoring signals as questions rather than automatic conclusions
06System and socio-technical failure modes
Examine automation bias, misuse, gaming, feedback loops and brittle interfaces
Spot local configuration changes, fallback failure and agentic creep before they become invisible assumptions
07Evidence limits and downstream questions
Challenge vendor claims, pilot results and AI-polished assurance wording against the available evidence
Route failure-mode questions to inventory, risk, control, monitoring, supplier, audit or technical review
Outcomes
Learning outcomes
01
Explain why AI outputs are uncertain signals rather than verified facts
02
Trace where uncertainty enters across data, prompts, retrieval, model or service behaviour, integration, workflow and human use
03
Recognise common predictive, generative, data, pipeline and socio-technical AI failure modes in realistic artefacts
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
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 Limitations & Failure Modes 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-02
- Discipline
- Artificial Intelligence
- Domains
- Governance · Risk
- Part of tracks
- Management System Auditor · Executive · 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.