THIS WEEK: A practical guide for managers who want to make better AI decisions

There's a version of AI adoption we see play out a lot. A leadership team decides it's time to "do something with AI." Tools get evaluated. Subscriptions get purchased. A few months later, not much has changed, and no one is quite sure why.

The reason is almost always the same - the problem was never defined.

We built AI in practice — understand, decide, structure to address exactly that. It's a workbook for managers and decision-makers, not technical specialists, who want a structured way to think through where AI actually makes sense for their team, and where it doesn't.

It starts with a simple formula for problem definition, one we use with clients before any tool discussion enters the room. It walks through how to assess the data you already have, how to run a quick decision test to determine whether AI is even necessary, and how to structure a full use case with our AI Canvas — a ten-section framework built around clarity, responsibility, and realistic expectations.

There's also a practical chapter on Shadow AI that tends to generate the most immediate reaction. The core idea is that unsanctioned AI use in organisations is a governance signal, not a personnel problem, and the response that actually works is a simple framework rather than a blanket ban. We've included a three-question risk filter and a 90-day action plan for getting things under control.

The workbook is free. It works as independent reading, as a team discussion tool, or as the foundation for a workshop session. We designed it to be used, not filed away.

One line that keeps coming up when we share this with clients: The starting point with AI is identifying what is the problem. A good problem definition is worth more than the best tool on the market, and the workbook opens there, with the questions worth asking before anything else.

If you'd like support working through this process with your team, our AI strategists can help managers go from problem definition to a scoped, ready-to-run use case. Get in touch to find out how we work.

— The WeAreBrain team