I help technology professionals and teams turn AI into measurable outcomes.

This site is about putting AI to work in real technology jobs, in a way that produces results you can show rather than experiments you cannot explain. It brings together six free learning areas, one-to-one mentoring for people who want help with their own role and career, and a free audit, team workshops and advisory for organisations that want the same thinking applied inside their teams.

All of it draws on years of delivering technology inside banks and other organisations where mistakes are expensive, which is why the advice here is practical, specific and honest about what works.

It is written for people who work in and around technology: analysts, engineers, architects, delivery leads, decisioning specialists and the leaders they report to. If technology is part of your working day and you want AI to earn its place in it, you will find the site was built with you in mind.

Some areas go deeper than others, and whenever something is technical, it is explained properly rather than assumed.

There is no account to create. Every explainer, walkthrough and opinion in the learning areas is open to everyone, and the email list asks for an address and nothing else.

The only things that cost money are the ones where I work with you directly: mentoring, team workshops and advisory. The one exception is the Pega CDH interview guide, which is a paid product written for people preparing for that particular interview.

I publish one substantial piece every week or two, on YouTube first, and the deeper guides and frameworks go out by email when there is something worth reading. I would rather be consistent than prolific, and I would much rather explain a subject properly and take a clear position on it than add to the commentary on whatever happened in AI this week.

A way of using it that holds up in real work. Most people get a useful first draft out of an AI tool and then spend the rest of the afternoon fixing it, which is why so few can point to anything it actually changed. The difference is method: choosing the right piece of work to apply it to, giving the tool the context it needs, checking what comes back against a reliable source, and keeping the whole thing inside the rules your organisation already has.

When you work that way, everyday AI use turns into results you can point to, and results you can point to are what move a technology career forward.

It begins with a free discovery call, where we work out what you are trying to change and whether I am the right person to help you change it. From there it runs one-to-one, in whichever format fits: a single focused session on one decision, a five-session sprint, or a ten-session programme for a larger change.

Every paid format ends with a written summary and a clear next step, and there is no obligation at any point along the way.

You need to work in or around technology, but you do not need to be an engineer. Most of the material is pitched at architects, delivery leads, analysts and technology leaders, and whenever something is technical it is explained rather than assumed.

No. The technology careers area is as free as the other five, and the email list costs nothing beyond an address. Mentoring is the paid option for people who want to work through their own situation with me directly, and even that begins with a free call.

Yes, it is. The audit is a structured look at one real workflow from your organisation, and it ends with a written summary that is yours to keep whether or not anything else follows.

It is scoped as a review rather than a build. A full implementation or an architecture engagement is a separate piece of work, agreed and priced on its own terms.

Sometimes nothing, and that is a perfectly good outcome. Where it makes sense, the usual next step is either a workshop that brings your whole team to a shared understanding of what AI can do for them, or scoped advisory on one specific decision you need to get right.

Either way the decision is yours to make, and you make it with the written summary already in your hands.

Any organisation where technology is central to how the business runs and where getting AI wrong would cost real money. In practice that means banks and financial services, regulated industries, large transformation programmes, and technology businesses of every size.

The work itself is deliberately focused on AI adoption and governance, customer decisioning, transformation and migration. If your problem sits outside that, I will tell you early rather than let you pay to find out later.

Yes, a small number of each. Fractional roles as a Chief AI Officer, Chief Technology Officer, Head of Customer Decisioning or Chief Transformation Officer run for part of each week, with the remit, the days and the term agreed in writing before anything starts.

Keynotes, panels and internal leadership sessions cover enterprise AI, customer decisioning, transformation and technology careers. Each one is shaped to the audience in the room, and none of them is built around a product pitch.