By the end of today, AI will feel a lot less mysterious. We'll strip back the jargon, look at where it already shows up in your work, and practise using it safely and sensibly.
Understand AI, machine learning and generative AI in plain English.
Spot where AI already shows up in the tools you use every day.
Try a generative AI tool using safe examples and sensible judgement.
Explain artificial intelligence in clear, everyday language.
Tell the difference between traditional software, machine learning and generative AI.
Recognise common examples of AI in workplace tools.
Describe, at a high level, how AI uses data to identify patterns.
Use a generative AI tool for a simple, low-risk task — and know exactly what to check.
You will see short demonstrations, try tasks for yourself and apply the ideas to realistic scenarios.
You will learn from colleagues through paired discussion, group reflection and shared examples.
We'll introduce each idea briefly, then connect it straight to something you can use.
Open the Day 1 resource and complete the opening check-in.
This isn't about catching anyone out. It helps us see where you're already confident and where it's worth spending more time.
Pay attention to why you choose an answer. Your reasoning matters more than the score.
Personal data, client records, employee records, private messages, contracts or restricted files.
Use approved tools, follow organisational policy, question the source and decide whether human review is needed.
Fictional scenarios, public information, anonymous extracts and the practice text provided in the course.
AI is one single technology.
AI understands things in the same way people do.
AI always searches the internet before it answers.
If the wording sounds confident, the answer must be correct.
Share the first word AI brings to mind — then tell us what's behind it.
Notice where AI might already be showing up in your day.
Connect different types of AI tools to the work you actually do.
Try three safe prompts using an approved tool and non sensitive examples.
Build a list of opportunities and concerns you'll carry through the whole course.
Check the purpose, timing, group setup and data boundary.
Work individually, with a partner or in a small group.
Notice what changed, improved, failed or surprised you.
Connect the activity to your role, your judgement and your next sensible action.
AI is software that uses patterns in data to make predictions, recommendations or new content.
Keep this in mind whenever AI comes up. It makes the conversation much easier — and removes the mystery.
People write the rules. The system follows those rules.
The system studies many examples and learns patterns from them.
The system creates text, images, audio or code based on patterns it has learned.
Spam filtering, priority inboxes and suggested replies.
Ranking results, predicting search terms and recommending content.
Grammar suggestions, transcription and translation.
Scheduling, fraud detection and service routing.
Early AI focused on rules, expert systems and periods of slowed progress.
More data and stronger computing helped machine learning become part of everyday technology.
Large language models and generative tools made AI more visible to the public.
Workplaces are deciding where AI is useful, where it is risky and what good practice should look like.
Examples of content or behaviour that the system can learn from.
Relationships the system identifies across many examples during training.
A likely response based on patterns, not a guarantee that the answer is true.
Use an anonymous paragraph and ask for five clear key points.
Ask for a clearer version for someone who is not a specialist.
Create a fictional out of office message or a simple meeting note.
Work on your own or with your group. When we regroup, each person shares one thing that shifted their thinking.
Look for patterns in your output, your group's output and the wider discussion.
Connect what you're seeing to the concept and the safe practice boundaries.
Make the next attempt stronger by adding context, checks or clearer instructions.
Discuss: use, use with caution, or do not use?
Discuss: use, use with caution, or do not use?
Discuss: use, use with caution, or do not use?
Discuss: use, use with caution, or do not use?
What worked better than you expected?
What felt unclear, unreliable or risky?
What would you need to check before using the output?
Where does human judgement still matter?
What would your workplace policy need to make clear?
What would you do differently next time?
Where could AI genuinely save time or improve quality?
What felt risky, unclear or too confident?
What would need to be verified before workplace use?
What guidance, policy or practice would help people use AI well?
I would benefit from another example or more time to practise.
I understand the idea, but I would like support applying it.
I can try this independently and explain the checks I would make.
Your opportunities and concerns matrix, alongside a brief reflection on what changed in your thinking.
It should show what you understood, what you tried and what judgement you applied.
| Developing | Secure | Strong |
|---|---|---|
| Can repeat key terms, but still needs support to explain them clearly. | Explains the concepts plainly and completes the guided tasks. | Adapts the approach to context and identifies sensible checks. |
| Accepts or rejects AI without much explanation. | Reviews the output and can name relevant risks. | Balances usefulness, data limits, verification and accountability. |
Complete the Day 1 quiz and assessment resource.
Aim for 7 or above. If a question stumps you, that's exactly what we want to talk about.
Focus on the reasoning behind each answer, not just the final score.
What assumption, worry or expectation did you bring into the session?
What has changed in your understanding, confidence or judgement?
Write one honest sentence in your workbook — not what you think we want to hear.
| Step | Plain English explanation | What a person still checks |
|---|---|---|
| Input | A message arrives with sender, wording, links, attachments and past patterns. | Is the sender expected? Does anything feel unusual? |
| Prediction | The system compares the message with examples of spam, phishing and normal mail. | A prediction is helpful, but not proof. |
| Action | The email may be shown, hidden, flagged or moved to junk. | Legitimate messages can be flagged and risky messages can slip through. |
It shows AI as pattern-based support, not a thinking person or a perfect judge.
| Term | Simple meaning | Example | Useful caution |
|---|---|---|---|
| Artificial intelligence | A broad label for systems that perform tasks we associate with human judgement. | A tool recommends which support ticket should be handled first. | The label AI does not tell you whether the tool is good or safe. |
| Machine learning | A system learns patterns from examples rather than only following hand-written rules. | A fraud system notices unusual account behaviour. | Patterns can reflect messy or biased data. |
| Generative AI | A system creates new text, images, audio, code or plans from learned patterns. | A chatbot drafts an email from bullet points. | The result can sound polished and still be wrong. |
Ranking results, predicting search terms and suggesting related questions.
Grammar suggestions, transcription, translation and summarising.
Routing queries, spotting common issues and prioritising cases.
Spam filters, suspicious login alerts and fraud detection.
Which of these have you used without calling it AI? Which ones would you trust more or less, and why?
| Example in training data | Pattern the system may learn | Possible output |
|---|---|---|
| Many urgent tickets mention outage, payroll, deadline or many affected users. | Certain words and combinations often appear in urgent work. | A new payroll deadline ticket is ranked as higher priority. |
| Many clear emails have short paragraphs, direct subject lines and next steps. | Readers respond well to direct structure. | A draft email includes a clear subject and action. |
| Many meeting summaries include decisions, actions and owners. | These are common summary categories. | The tool creates headings for decisions, actions and owners. |
This is not the same as understanding the organisation, the people or the consequences.
The style can make the answer feel more certain than it is.
If context is missing, the tool may still produce a complete-looking response.
It can imitate common structures such as reports, policies and emails.
A good-looking answer is a starting point. It is not evidence.
| Task | Safe prompt | Why it is safe |
|---|---|---|
| Rewrite | Rewrite this fictional update so it is clearer for a busy colleague. Keep it under 120 words. | Fictional text, clear task, easy to check. |
| Summarise | Summarise this public article into five points for a non-specialist audience. | Public source, named audience, visible output. |
| Brainstorm | Suggest five agenda items for a team meeting about communication, without using any real team names. | No personal data and low stakes. |
| Task | Likely risk | Reason |
|---|---|---|
| Draft a fictional out of office message. | Low | No personal data, easy to check, low consequence. |
| Summarise a confidential complaint. | High | Personal data, sensitive context and tone matter. |
| Suggest team meeting ideas. | Low to medium | Useful starter, but still needs local judgement. |
| Rank job applicants. | High | Fairness, transparency and accountability issues. |
Is the answer based on text you supplied, public information or something unstated?
Look for dates, names, figures, assumptions and missing context.
Would a mistake inconvenience, embarrass or disadvantage someone?
If it is used at work, a person still owns the final output.
You've got a clearer, more practical way to think and talk about AI at work — and the habit of checking before you trust.
Before you close: pick one thing you're taking forward and write it down.