Practical AI for Everyday Work
A clear, jargon-free introduction that gets a whole team productive with AI — without the risks no one warned them about.
Most teams are handed AI tools with no guidance and left to work it out alone, which wastes time and creates avoidable risk. This jargon-free session gets a whole team genuinely productive with AI on real tasks like writing, research and analysis, and shows them the common mistakes that cause trouble. Everyone leaves on the same page.
Any team that wants to use AI well and safely day to day.
Work confidently with AI on real tasks — writing, research, analysis — and avoid the common, costly mistakes.
AI tools are now built into the everyday software your team already uses, so people are experimenting whether or not anyone has shown them how. Without basic ground rules, it’s easy to trust a wrong answer or accidentally share company information with a tool that keeps it. A short shared grounding now prevents the messy, costly mistakes later.
- Understand what today's AI tools are actually good at, without the jargon
- Write better prompts so you get useful results instead of vague ones
- Work through real tasks: drafting, summarising, researching and analysing
- Recognise the common mistakes that get people into trouble and steer clear of them
- Keep company information safe when using AI tools
- Leave with a team cheat-sheet and a set of safe-use ground rules
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What today's AI tools are actually good at
- A simple, jargon-free picture of what an AI assistant does
- Why the same question can give different answers each time
- What the tool does and doesn't know, and why it can be out of date
- The kinds of tasks it shines at versus the ones it struggles with
- Setting realistic expectations before you start
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Writing better prompts to get useful results
- The parts of a good prompt: role, task, context, constraints, format
- Giving examples so the tool matches the style you want
- Asking for output as a list, table, summary or draft
- Letting the tool ask you clarifying questions
- Turning a vague request into a specific, answerable one
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Real tasks: drafting, summarising, researching, analysing
- Drafting and rewriting emails, reports and notes for the right tone
- Summarising long documents, threads and meetings
- Using AI to explain and research, with checking built in
- Pulling out, sorting and analysing information from text
- Brainstorming and working through problems with the tool
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Iterating to get better answers
- Treating prompting as a loop: draft, look, refine
- Adding constraints or examples when the answer misses
- Building up a long piece step by step instead of all at once
- Combining a few simple techniques for stronger results
- Knowing when to stop refining and start over
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The mistakes that get people in trouble — and how to avoid them
- Why a confident, well-written answer can still be wrong
- What hallucinations are and how to catch them
- Cross-checking facts against trusted sources before relying on them
- Spotting biased framing, missing evidence and stale information
- When to trust your own judgement over the tool
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Keeping company information safe
- What should never be pasted into a public AI tool
- Public tools versus approved company tools, and the difference
- How to tell whether a tool may keep or learn from what you type
- Simple habits that prevent accidental data leaks
- A quick 'is this OK to share?' check before you paste
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A cheat-sheet and ground rules your team keeps
- A one-page cheat-sheet of prompts and techniques that work
- A short set of safe-use ground rules everyone agrees on
- A starter set of reusable prompts for common tasks
- The 'what to check' reminder for any AI output
- A reference the whole team keeps and adds to
Bring "Practical AI for Everyday Work" to your team.
A short conversation about your team, your risk, and the session that would move them. No pitch deck — just the right scope and dates.
Enquire