This is an illustrative example of how a diagnosis runs, not a specific client engagement.
A regional logistics operator knew it should be ‘doing something with AI’ but had no idea where it would actually pay back, and no appetite for a strategy deck that ends up in a drawer.
What a diagnosis looks at
We go into the business and follow the work: where time is lost to repetitive tasks, where revenue leaks, and where a person is the bottleneck for something a machine could do reliably. We look at the tools already in place, the data that exists, and what it would realistically take to build against each opportunity.
What you get
Not a report. A costed, prioritised build plan: the handful of places AI pays back first, what each is worth, what it would cost to build, and the order to do them in. You decide what to build knowing the number attached to it.
Why it leads to something running
A diagnosis is build-led. Every opportunity on the plan is one we could build, and even the diagnosis leaves something concrete behind: a baseline to measure against once the work ships.