Practical AI Training

What are Large Language Models, and how do they actually work? We explain the technology underpinning today’s AI tools in plain English, helping you understand what AI is good at, where it struggles, and why the quality of your output depends so heavily on the quality of your input.

We also look at how AI is changing now: moving beyond the chat window and into the tools you already use, including documents, spreadsheets, slides, browsers and shared files. The aim is to give you a practical understanding of when to use AI, what to hand over, and what still needs human judgement.

AI is no longer just a box you type into. We walk through the main types of tools now available: chat assistants, desktop agents, spreadsheet and slide add-ins, browser automation, connectors, projects, memory and reusable Skills.

The focus is on what each tool is actually for. When should you use chat? When should you use a tool that can work on files? When does it make sense to connect AI to email, Drive, Slack or Salesforce? By the end of this section, your team will understand the AI toolkit in practical terms, not as a list of features.

How can you make the most out of AI in day-to-day business? We explore the strongest current use cases across research, document drafting, spreadsheet analysis, slide creation, meeting preparation, client briefings, internal reporting and repetitive admin.

We look at simple examples as well as more joined-up workflows: gathering source material, analysing it, turning it into a report, building slides, and refining the final output. The goal is to help the team spot which tasks are worth improving first, rather than trying to use AI everywhere at once.

Once you know what AI can do, the next step is learning how to work with it properly. We cover the practical techniques that improve results straight away: giving clear context, defining the role and audience, scoping one task at a time, using structure, showing examples, setting constraints, and checking the output.

We also cover the habits that make AI use faster and cleaner: editing prompts rather than piling on follow-ups, batching related questions, starting fresh chats when topics change, matching the model to the task, and knowing when to ask AI to plan before it acts.

An interactive session where the team builds working AI assets together. We start with a writing-style template that captures tone, vocabulary, structure and formatting preferences, so AI can produce emails, memos, updates and documents in a more consistent house style.

We'll move onto more complicated agentic workflows that are tailored to your particular business cases, whether bulding bespoke presentations, generating reports from internal resources or integrating AI with spreadsheets for more complex analysis.  The aim is for each participant to leave with something they can test on real work the next day.

Individual experimentation is useful, but it does not create consistent results across a team. We cover how to move from ad hoc AI use to a shared setup: approved tools, shared instructions, reusable Skills, connected systems, clear data boundaries and ownership of the workflow library.

We also discuss common pitfalls, including tools being switched on without practical use cases, Skills being too generic, no one owning updates, and teams using different approaches for the same work. The goal is to make AI adoption useful, safe and repeatable.

Real-time Q&A, guided discussion and workflow mapping to identify where AI can make the biggest immediate difference in your business. We look at the tasks you repeat, the documents you produce, the systems you rely on, and the workflows most worth improving first.

After the session, continued support helps turn training into habit. The most useful AI workflows usually emerge through use, so we remain available as questions come up, tools change, and your team begins applying AI to live work.