A reference library on enterprise AI: how AI tools get built, and how the people around them come to use them. Written from practice, for my clients.
Each dot is an example question, the kind a leadership team asks at each step of putting AI to work.
Whatever I hand over at the end of an engagement, a report, a roadmap, a deck, has to be short enough to read and long enough to be honest. It never quite manages both.
It doesn’t go deep enough for the person defending the roadmap, or the person defending the budget, or the person who has to keep it running once everyone else has moved on. And it’s only true for as long as the situation that produced it holds. So I built what the deliverable could point to: a reference library written from practice, kept current, and honest about where the methods break. The report gets shorter. The links stay true. And what settled into place only after an engagement had ended, too late for the deliverable, has somewhere to live.
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I help organisations put AI to work, and get their people working with it.
Most AI effort is spent on the technology, though most of the value lies in the people and the process. The work here starts from that end: one process where AI clearly pays, AI tools built for it, and as much care for how the work changes around it as for the build.
The people who do the work already know where the time goes, which steps take judgement and which are habit. AI tools built without them get worked around. Built with them, they get used, because they fit how the work is actually done.
I run my own practice this way. The library you are reading is researched, drafted and checked with AI tools, and edited by me. The same machinery reads what an organisation's people say about their work and shows where AI would help and where it would not.
The reading is automated. The conversations are not.
An organisation is not a machine. It pushes back, it surprises, and the only way to find out what works here is to try something here. So the change moves in small steps, designed by the people who have to live with it, and measured against how the work ran before.
Twenty-five years of helping people work in new ways, most of it inside large organisations in the middle of real change.
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Financial services, pharma, manufacturing, technology, professional services.
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I built this for my clients.
The articles are written for people already doing this work. What they answer is the harder question: why this way, why now, and what it changes for your team once you do it. They’re written to be read in pieces, out of order, by different people who each need a different part of the same answer.
This is meant to be the evidence. Read one and see if it holds up.
Every article is researched before it is written, and every citation is checked against its source.
The shelves lead with enterprise AI: where it fits, whether it pays, the first build, the people using it, and running it safely at scale. Behind it sits scaled agile, SAFe as it runs inside large organisations, where most of what I know about the people side was learned. Beside them sits the research: each study is one finding from a real engagement, anonymised: what the scores said, what people wrote beside them, and what connected the two.
Shelf
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Two kinds of people tend to write to me.
Morné Wiggins