Notes on shipping AI that pays off
Field notes on feasibility, delivery, and getting AI past the pilot stage — from the team that builds and runs it, not just advises on it.
Do you need a full-time AI hire — or a fractional one?
Hiring a full-time AI leader is a 12-month bet made before you know what the work is. Here is how to tell whether you need the headcount yet, or a fractional partner who ships the first win first.
Read the postHow to choose an AI consultant — seven questions that separate builders from deck-makers
A buyer’s checklist for hiring AI help: the questions that reveal whether a consultant will ship a working solution on your infrastructure, or hand you a strategy deck and an invoice.
Read the postResearched, validated, built: how a low-risk AI engagement actually runs
Most AI proposals ask you to commit to an outcome before anyone has proven it is reachable. Here is the stage-by-stage alternative that keeps the risk at the front, where it is cheap to walk away.
Read the postHow to tell if an AI project is worth doing — before you build
A three-question feasibility check you can run in an afternoon, so you commit budget to AI work that pays off instead of a demo that stalls in month three.
Read the postWhy most AI pilots stall — and the accountability gap behind it
Most AI pilots fail for lack of clarity and ownership, not technology. Here is exactly where they break, and how a KPI-anchored, stage-by-stage engagement avoids it.
Read the postWhere AI pays off first in regulated work
In healthcare, legal, cybersecurity, and education the AI conversation stalls on the same worry: getting it wrong is costly. That fear also points straight at where AI is safe to start.
Read the postBook a free discovery call
No pitch, no obligation — a free 30-minute call to talk through whether AI fits your problem, and what a first, fixed-scope stage would look like.