Insights
Thinking clearly about AI that has to work.
Written for the people who have to decide: the operator weighing a workflow and the engineer who will be asked whether it is safe. Plain language, real examples, no hype.
- Operating economics
- AI workflow automation
- Human-in-the-loop AI
- How to choose the first workflow to automate: the bottleneck economics testSkip the AI roadmap. A practical method for finding the one workflow whose cost, frequency and structure make it worth turning into a system first, with the questions to answer before anyone writes code.
- What AI workflow automation actually is (and what it is not)A working definition for operators: AI workflow automation moves work between systems, applies judgment where rules run out, and stops where a human must decide. Here is how to tell it apart from chatbots, RPA and demos.
- Where AI agents should stop: designing authority boundaries that holdHuman-in-the-loop is a design problem, not a checkbox. How to define what an AI system may do on its own, what needs bundled approval, and what needs a named human every time, and how to enforce it in code rather than prompts.
Next / The Bottleneck Diagnostic
Bring us the bottleneck.
You do not need an AI roadmap. You need one workflow worth fixing. Bring us the one that costs too much, moves too slowly, or only works when one person is in the office. We will tell you, honestly, what to do about it.