Learn. Apply. Bring your project.

My approach to AI starts with the business: what you sell, to whom, at what margin and through which process. A tool can help with part of that work. To choose where to apply it, we need to understand the task, the expected result and who will be responsible for it.

If enquiries arrive and nobody answers them, producing more advertising may simply increase the backlog. If every sales proposal requires searching through five different places, organising that information might be the right starting point. These operational and commercial decisions should guide the technical choice that follows.

That is why I discuss online business and AI together. I have worked online since 2012, built my own projects and supported businesses. That experience shapes the questions I ask about execution, priorities and everyday use. I do not assume that an interesting demonstration will work in exactly the same way inside a team, with customers and deadlines to meet.

To get started, choose a task you know well and can observe before and after making a change. It might involve preparing a response, organising information or drafting a piece of content. A small scope lets you test carefully, understand the mistakes and decide what to do next with better evidence.

Write down how you do that task today. What information comes in? What decisions do you make along the way? Where do you usually need to correct something? This record helps you explain the request to AI and uncover steps that previously existed only in your head. It also gives your team a shared reference for discussing the process.

Then compare the result with an example you understand. Check the facts, look for omissions and try a less typical case. If everything still needs correcting manually, there is more preparation to do. Keep a record of what you learn and change one thing at a time, so you can understand the effect of each adjustment.

In sales, I want to understand how information supports a decision and helps prepare the next customer contact. AI can help organise an enquiry or prepare a proposal. The customer’s context, qualification criteria and commitments being made still need to remain clear to the team responsible for carrying the conversation forward.

Consider a service business receiving enquiries with very different levels of detail. A useful first exercise is to define which questions need answering before suggesting a solution. Only then does it make sense to explore a way of organising those answers or proposing a next step. Automating a poorly defined decision can multiply the confusion.

The quality of the response matters too. A well-written message that ignores what the customer requested still needs revision. I want you to use your knowledge of the business to assess the tool’s proposal, add context and decide what should actually be sent. Taking responsibility for that choice is part of the commercial work.

When an application or automation starts handling real information, we need to define access, limits and ways to recover from an error. Generating a solution quickly does not remove the need for review. I want you to distinguish a test from real use and recognise the points where you need specialist help.

Before connecting tools, identify which data will move between them and who can see it. Start testing with fictional information. Avoid putting credentials in prompts, public files or code uploaded to a repository. If an action can send messages, change data or create a commitment, decide where a person should validate it.

It is also worth preparing for failure. Who receives the alert? How can the process be stopped? Is there a way to restore the previous state? These questions make experimentation more responsible. No training programme removes every risk; recognising the limits of what you have built is part of learning to work with AI.

The Lab combines learning with support, and my mentoring sessions take place twice a month. To make good use of that time, leave room between sessions to experiment. A project becomes clearer when you return with an attempt already made, a more specific question or a result that needs to be reviewed.

I would not measure progress only by the number of classes watched. I would ask what you can now explain more clearly, which decision you made and which task you can approach with better judgement. Some weeks, progress means discovering that an idea was poorly defined and rewriting the brief before going further.

If you have a team, choose someone to take responsibility for the first test and agree how you will assess the outcome. If you work alone, keep the exercise within the time available to you. Consistency comes from adapting the work to your circumstances, rather than trying to keep up with every new tool.

This programme makes sense if you want to apply AI and automation at work or in a business and are willing to experiment. You may bring experience in a particular field while knowing little about AI. Your understanding of customers, tasks and everyday problems provides a useful starting point for learning how to apply it.

You do not need to arrive with a large project to have a worthwhile question. You can start with one small part of your work, as long as you can explain what you want to improve. The important commitment is to help evaluate the result: test it, provide context and recognise when something is not ready for real use.

If you want a team to implement a solution for you, explore SpartAds services. The Lab is about developing your ability to execute and make decisions, supported by training and mentoring. Before joining, check the current programme and conditions on the LAB website to confirm that they match what you need at this stage.