Research and articles
What the evidence says.
Dated research notes on measured studies and public data, and articles on systems engineering and practical AI. Every source is linked.
· 5 min read
AI at work: what the measured studies show
Most claims about AI and productivity come from surveys of how people feel. A handful of studies measured real work. They agree on more than the headlines suggest: the gains are real, uneven, and they reverse when the task is outside what AI does well.
Read the note· 4 min read
What doing it by hand costs, measured
Healthcare administration is one of the few fields that measures the cost of the same transaction done by hand, partly automated, and fully electronic. The numbers show where the savings are, and why adding a tool on top of a manual process captures only part of them.
Read the note· 6 min read
Agentic AI vs traditional automation: what actually changes
Agentic AI can decide and adapt; traditional automation follows fixed rules. Here's the real difference, when each one fits, and why deterministic-first still wins for most of the work in a business.
Read the note· 6 min read
How to decide which business processes to automate
Most automation projects pick the wrong process first. Here's the practical test for which work to automate, in what order, and why the boring high-volume handoffs beat the flashy ones every time.
Read the note· 6 min read
Deterministic before intelligent: where AI actually belongs
The instinct is to point AI at everything. The discipline is to ask one question first. Can a rule decide this reliably? Reserve AI for the small slice where the answer is genuinely no. Here's the test we use.
Read the note· 7 min read
Make it auditable, or don't ship it
Trust in an automated system dies the first time an output is wrong and nobody can explain why. The fix isn't a better model. It's an audit trail designed in from day one, so "what happened?" is a query, not an investigation.
Read the note· 7 min read
How to trust AI document extraction (without trusting the AI)
"How accurate is the AI?" is the wrong question. A document-extraction system is trustworthy because of the validation built around the model, not because the model is good. Here are the four checks that actually catch errors.
Read the note· 5 min read
Why most AI automation pilots stall
The model is rarely the problem. Pilots stall because the systems underneath can't give the AI clean data, a way to act, or an audit trail. Here's the pattern, and the order that actually works.
Read the note· 6 min read
Your tools don't talk to each other. That's the whole problem.
Most growing businesses end up running five to eight tools that each hold a piece of the truth, and none of which agree. The fix usually isn't another tool. It's unifying what you already run into one source of truth, in the right order.
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