AI Essentials for Governance Professionals

• Explain what artificial intelligence is and the distinction between AI and conventional software. Understand the difference between narrow AI, general-purpose AI and agentic AI, at the level of understanding required to evaluate information presented to the board.
• Describe how AI systems are built and how they produce outputs, including the role of training data, how models learn patterns, why outputs cannot always be explained by their developers, and why the character of training data directly affects the reliability and fairness of outputs.
• Identify the five key risks AI introduces - hallucination, bias, overconfidence, data privacy and the regulatory gap - explain the theoretical basis for each, and recognise the signals that indicate one or more of these risks may be present in an AI system operating within the organisation.
• Apply understanding of how AI works and what can go wrong to the practical situations directors encounter - recognising how AI is used across sectors and inside organisations, identifying when organisational use of AI is creating risk exposure, and engaging critically with primary material on AI-related failures to identify what better AI literacy at board level would have changed.

Section A – Governance Principles

• What AI is and what it’s not – practical starting point, definitions, misconceptions, examples, narrow versus general purpose AI defined and explained through the governance lens.
• How AI works – training data and its role, models, the black box problem and the governance consequences.
• Risks – risk concepts explained with examples; hallucination, model drift, and bias.
• Types of AI: from narrow models to agentic systems – Large Language Models (LLMs), Vision Language Models (VLMs), Small Language Models (SLMs), Masked Language Models (MLMs), Mixture of Experts Models (MoE), Agentic AI.
• Practical scenarios – explore interactive governance scenarios using AI conversation tools in the Learning Management System.

Section B – Professional Practices

• How AI is used inside organisations – three broad categories of AI use cases explained with examples, shadow AI and formal governance structures.
• Hallucination, model drift and overconfidence – what they look like in practice, organisational implications.
• Bias and organisational risk – how to manage these risks, the dimensions these risks operate across, the questions governance professionals should ask management, collaborating and influencing decision-making.
• Practical scenarios – explore interactive governance scenarios using AI conversation tools in the Learning Management System.

Section C – Personal Thinking

• Examines a concrete example of how organisations should govern the use of an emerging and rapidly developing technology when regulatory frameworks have not kept pace with the rate of change. In considering the specific facts of the case, to the governance principles it demonstrates, build understanding of how those principles apply to your own organisational context.

        Case study: Hallucination in a government contract – the Deloitte/DEWR matter.

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A short course is assessed as follows:

• A final quiz (multiple choice, achieve 75% to pass, unlimited attempts).
• A short case study written response (suggested answers are revealed for reflection).
• Knowledge checks are included as you progress through the course material (these are not assessed).
• See the Assessment Rules for further information.

We provide specialised in-house training to organisations who want to train large groups of staff.

What are the benefits on in-house training?

• Train large numbers of staff.
• Participate in customised training that caters to the needs of your staff and your organisation’s business objectives.
• Invite and engage with your key clients, suppliers and contractors to ensure you’re working together with the same knowledge.

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