Hiring a full-time AI executive is a twelve-month commitment to a person you can't fully evaluate yet. A fractional engagement compresses the risk: you get senior judgment immediately, and the first month is where that judgment earns its keep.
Here's exactly how we spend it.
Week one: listen before touching anything
The fastest way to lose a team's trust is to arrive with a stack of recommendations on day two. So week one is deliberately quiet. We sit with the people doing the work, map where time actually goes, and find the gap between what leadership thinks is happening and what is.
- Interview every function that would touch an AI workflow
- Inventory the current tools, data, and who owns them
- Identify the one or two outcomes the business actually cares about
You can't automate a process nobody has written down yet.
Week two: find the highest-leverage opportunity
Most teams have a dozen places AI could help and the budget to pursue one well. The job is ruthless prioritization — scoring each opportunity by impact, feasibility, and how quickly we can show a result the team will believe.
We're not looking for the most impressive demo. We're looking for the first win that makes everyone want the second one.
Weeks three and four: ship something real
By the end of the month there is a working thing in someone's hands — not a slide, not a prototype that lives on a laptop. A real workflow, integrated where the work happens, with a metric attached so we know whether it moved.
What we deliberately leave alone
Governance, big platform migrations, and org-chart questions all matter — and none of them belong in month one. Trying to solve everything at once is how AI initiatives stall. We sequence them deliberately, and the roadmap we hand over says as much about what to wait on as what to do now.
By day thirty you should have three things: a clear view of where AI moves your numbers, one shipped outcome, and a roadmap your board can read. That's the bar.