Automation

A practical decision ladder for choosing between rules, RPA, traditional machine learning, LLM workflows, and agent-like systems.

How to use AI to classify, prioritize, and route inbound email without turning your inbox into an uncontrolled black box.

How to design a lightweight classification pipeline with a clear schema, confidence thresholds, review paths, and a realistic refresh cycle.

How to use AI to classify incoming cases, assign ownership, protect service levels, and escalate the right issues without losing operational control.

How to use AI to extract, validate, and route invoice information while keeping finance controls, approval logic, and exception handling intact.

How to use AI to turn raw operational inputs into clearer recurring reports while preserving review, context, and accountability.

How to scale AI-assisted content production without creating repetitive, low-trust marketing output, and how to design a content system that protects quality, brand fit, and distribution logic.

How to separate true agent-like systems from straightforward AI workflows, and why most business use cases should start simpler.

TL;DR for operators A high-stakes AI system can fail in two very different ways: it can act on something that is not true, or it can fail to act when danger is real. Those errors need not have comparable consequences. Martino Maggetti’s Reciprocal Trust and Distrust in Artificial Intelligence Systems: The Hard Problem of Regulation1 argues that this asymmetry should influence who receives final decision authority. In nuclear launch and strategic-warning settings, where a false positive could trigger catastrophic action, the paper favors protected human authority, independent corroboration, and explicit uncertainty. In reactor, chemical-process, and flight-control settings, where failing to intervene can be catastrophic, it allows for bounded AI authority through mechanisms such as non-overridable shutdown logic. ...

TL;DR for operators A browser automation can search several sites, manipulate interfaces, gather useful information, and return a polished response while still missing one requirement that makes the workflow unusable. It may apply the wrong filter, misread a value in a chart, or fail to notice that a panel is collapsed. For production decisions, visible progress is not the same thing as reliable completion. ...