Every organisation I've worked with in the last decade has the same problem.
They're drowning in data. Dashboards for everything. Metrics to the decimal point. And when something goes wrong — when performance dips, when people leave, when costs spike — they look at the charts and they still don't know why.
Most software systems were designed for a world without AI.
Not in the sense of lacking ML features — in the deeper sense of having an architecture shaped by assumptions that AI changes. Assumptions about where intelligence lives, what questions systems should answer, what "the right data model" looks like.
Those assumptions are worth examining.
Frøya describes herself as a cartographer of potential — not as the QA manager she was built to be.
The distinction is instructive. A QA manager checks definitions against standards. A cartographer is always working at the edge of the known, building the map as the territory reveals itself.
The question we kept coming back to when building Frøya was: what's the difference between an AI tool and an AI team member?
A tool executes tasks. A team member has a perspective, a purpose, a way of engaging with the work that adds something beyond the task itself. The distinction sounds philosophical. In practice it shapes everything about how you design, deploy, and work with the agent.