- Client
- QueueStone
- Role
- Strategic technology consultant
- Focus
- AI integrationEngineering operationsCost optimization
The client
QueueStone runs an ERP platform for mental-health providers — the software a practice depends on to keep its day-to-day operations moving. Systems in that position get judged on reliability before anything else, which is exactly what makes them hard to modernize.
The challenge
The platform had to move forward while it stayed in daily use. Development and QA cost set the ceiling on how much modernization was affordable, and release cadence set the pace at which any of it reached users. Pushing on one of those normally costs you the other.
What I did
AI in the delivery lifecycle
I introduced AI tools into the software delivery lifecycle, chosen for the work the team did every day rather than for what demos well. Adoption is a process question first: which step of the workflow the tool sits in, and who stays accountable for what it produces.
Development and QA cost
I brought the cost of building and testing down directly, rather than by moving the work somewhere cheaper and calling that a saving. Most of that cost sits in rework and waiting, which is why removing it also shortens the path to a release.
How the team runs
I reworked the team's process around what actually stalls delivery: how work gets sized, who decides, and what a change goes through before it reaches users.
The outcome
Releases came faster, and nothing was burned to get there. The cadence did not come from a bigger team, a bigger bill, or a stretch of overtime someone would have paid for later.


