I spend most of my time around data platforms, internal tools, and early-stage products. The technology changes, but my basic test does not: can someone rely on it when the work is real?
The problem comes first
I do not start with a tool and look for somewhere to use it. I start with the part of the work that is slow, unclear, or easy to get wrong. With a data pipeline, that might mean a report that arrives late or numbers that different teams cannot reconcile. With an AI product, it might be a task that takes too much manual effort. The tool only matters if it improves that situation.
I plan for the bad day
A clean demo can hide a lot. I care about what happens when a source is late, a schema changes, or a user does something unexpected. Someone should know who owns the system, where to look when it fails, and what to do next. That is less exciting than a launch video, but it is what makes a system useful.
Show the work
I would rather show a working product, a clear workflow, or a measurable result than make a big promise. I also think it is important to be honest about what a system cannot do yet. That makes the work easier to evaluate and easier to improve.
This is the standard I try to apply across my enterprise data work and the products I am building through LearnFi, Hired, PilotComply, and OmniSolve.