Learning Centre
AI readiness
Plain-English guides for using AI at work with better judgement, safer data habits, stronger prompting, and responsible workflow design.
AI Foundations
Know what AI can do, where it can fail, and when people must check it.
What generative AI can and cannot do
Understand why AI can produce useful work quickly while still being wrong, incomplete, or overconfident.
Search, retrieval, and generation
Learn the difference between finding existing information and generating a new answer.
Low-risk AI use cases
Start with tasks where AI can help without exposing sensitive data or making final decisions.
Human accountability with AI
AI can support work, but people and normal accountability chains remain responsible for decisions.
Task Fit & Everyday Use
Decide when AI is useful, when it needs review, and when it should not be used.
Prompting & Grounding
Give AI clear context, source material, and success criteria for useful drafts.
Prompting with context
Write prompts that give AI the audience, purpose, constraints, tone, and output format it needs.
Grounding AI in approved sources
Use source material carefully so AI answers stay inside the evidence you are allowed to rely on.
Prompt iteration and reusable templates
Improve weak AI outputs by refining the prompt and turning repeatable work into safer templates.
Evaluation & Human Judgement
Check facts, gaps, uncertainty, and risk before relying on AI output.
Checking AI output reliability
Decide whether AI-generated work is reliable enough to use in a workplace deliverable.
Trusted sources and unsupported claims
Know what to do when AI makes a strong claim without evidence or when outputs disagree.
Handling uncertainty and high-stakes AI
Ask for stronger review when AI affects customers, employees, money, compliance, or reputation.
Data, Security & Governance
Use safer habits for sensitive data, access, plug-ins, phishing, and reporting.
Sensitive data and approved tools
Know what information must not be entered into unapproved AI tools and why approval matters.
Public, internal, and organisation-run AI tools
Know why different AI tools need different data, access, and approval checks.
AI permissions, plug-ins, and integrations
Review what an AI tool can access, store, share, or change before enabling it.
AI phishing and social engineering
Understand phishing, social engineering, and why AI can make deceptive messages more convincing.
Incident response for AI data disclosure
Know what to do if sensitive information is entered into the wrong AI tool.
Responsible Adoption & Workflow Design
Design AI workflows with clear limits, review, ownership, and scale decisions.
Responsible AI workflow design
Design AI-assisted workflows with clear use cases, boundaries, review points, and ownership.
Human oversight and transparency
Keep human accountability visible and help people understand where AI is used.
Measuring AI workflow success
Judge AI workflows using quality, risk, user value, and efficiency together.
Keeping AI capability current
Refresh AI literacy because tools, risks, policies, and workflows change quickly.