Plain-English writing on AI in business.

Why we write about AI this way

There's no shortage of AI writing online - much of it aimed at enterprises with their own data teams, or at developers building the underlying models. We write for the people sat in the middle: the founder, the operations director, the head of finance, the partner running a thirty-person practice. The people who have to decide what to actually do this quarter, with the team they actually have, on the budget they actually have.

That means plain English, no breathless hype, and a strong preference for advice that survives contact with a real Tuesday morning. We try to write the kind of articles we wish we'd had when we were figuring this out the first time.

What you'll find here

Strategy pieces on how to think about AI in an SMB context. Playbooks for the most common starting points - pilots, policies, vendor selection, data hygiene. Honest write-ups of patterns we've seen work, and patterns we've seen quietly fail. The occasional rant when something is being mis-sold to businesses on a scale that deserves one.

We update roughly twice a month. Articles aim to be useful for at least a year, so we tend to focus on patterns rather than the tool-of-the-week. The tools will change. The shape of getting them to work in a small business will not.

Where to start

If you're new to all this, the readiness vs strategy piece is probably the most useful one to read first - it'll save you several wasted months. After that, the 30-day pilot template is the playbook most leaders find themselves coming back to. The rest is best read as needed, when a specific problem turns up.