What this is
Where AI genuinely changes a commercial process, and where it does not. Plus the behavioural data foundation underneath it, because without one no model has anything useful to act on.
What usually brings people here
A leadership team told AI is urgent without being told what it is urgent for. Several pilots running with no owner and no number attached to any of them. Or an expensive data project that improved the reporting and changed nothing.
The questions that separate a plan from an enthusiasm
What specifically gets cheaper, faster or better, named as a process rather than a capability. Who stops doing what, because if nobody stops the saving is theoretical. Who owns the number in twelve months. What happens when it is wrong, and who notices. What data leaves the building and on what terms. And what you are choosing not to do in order to fund it.
What I do
I start from the commercial outcome, never from the technology. We keep the two or three initiatives that survive those questions and stop the rest. Then I make sure a channel is actually wired to act on the output, which is the step that gets skipped and the reason personalisation so often turns out to be a first name in a subject line.
The foundation underneath
Identity, consent, ownership and definitions. Four organisational problems and one technical one, which is why these projects run long. Build them properly and the AI conversation becomes straightforward. Skip them and no model will rescue it.
Where this comes from
I led a cross brand behavioural data foundation in a large regulated organisation, built the business case, got agreement across several companies, and ran the first machine learning and personalisation work on top of it. I also hold an executive programme in applying AI to business, covering implementation, scaling and the ethics of it.