Most of my career has been spent figuring out how complicated work actually gets done.
Not in theory. In the messy middle where teams, budgets, systems, data, priorities, and people all have to work together.
I build the operating structures behind that work: workflows, work-management systems, reporting, governance, financial processes, and increasingly the data foundations that make AI useful.
A lot of the job is figuring out what the real problem actually is, stripping away what doesn’t need to be there, and building something people can use.
That’s generally the kind of work I like doing: complicated enough to be interesting, practical enough to actually matter.