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 Robots do not fail only because their “brain” is too small. They fail because the system asks the wrong component to do the wrong job, at the wrong time, with the wrong view of the scene, and then acts surprised when the banana does not land in the bowl. Shocking, yes. ...

TL;DR for operators Robot-learning teams do not usually run out of model ideas first. They run out of clean demonstrations on the exact robot, in the exact setup, with the exact action labels needed for behavioural cloning. The paper behind GLAM attacks that bottleneck directly: instead of asking whether cheap auxiliary demonstrations can be thrown into the training pile, it asks whether their effects can be translated into actions the target robot can actually execute.1 ...