Most senior people we meet are past the “what is AI” stage. They use it every day, they have views on where it goes, and they are quietly frustrated that it still feels like a cleverer search box. The gap is not knowledge. It is having built something that runs on its own.
Start with a task, not a tool
The instinct is to pick a tool and look for uses. Reverse it. Choose one job you do again and again that eats real time: the weekly board update, the first draft of a proposal, sifting inbound, turning a transcript into actions. A repeated job with a clear input and a clear output is the right size to build against.
If you cannot describe the job to a new hire in two minutes, it is too vague to hand to a machine. Sharpen the task first.
Break it into narrow steps
A single prompt asking for everything gives you a mediocre everything. The agentic move is to split the job into a few steps, each narrow enough to be reliable, and let them hand off:
- a step that gathers the raw material,
- a step that produces a first draft,
- a step that checks the draft against what good looks like.
None of this is magic, and none of it removes your judgement. The machine does the scaffolding; the decision at the end stays yours. What changes is that the part which used to take a morning now takes a fraction of it.
Keep a human at the decision, not the grunt work
The failure mode is either trusting the output blindly or doing everything yourself. The useful middle is to automate the gathering and drafting, and spend your attention on the one judgement that matters. Build the workflow so that judgement is the last step, not the whole thing.
Where this gets practical
Reading about this only gets you so far. The point at which it clicks is when you have built one working pipeline against your own task and watched it run. That is what a day at the Oxford Agentic Bootcamp is for: you bring the task, you leave with the workflow, and you take home the pattern to build the next one.