"AI" now covers two genuinely different technologies that get evaluated, bought and blamed as if they were one. Traditional automation executes a process someone has already mapped in full; an agent is asked to make judgement calls inside a process nobody fully wrote down. Confusing the two is where most disappointing pilots start.
Our coverage here stays close to the decisions that come before a build starts: whether a workflow is actually mapped well enough to automate, what data and permissions a pilot needs before it can reach production, and how a platform choice that used to sit with IT became a question the board now expects an answer to.
The angles this subject is covered from, and what each one is for.
AI agents and traditional automation solve different problems and fail in different ways. We write about where each one fits, what an agent needs in order to be trusted with a task, and why a workflow that was never mapped cannot be automated.
Data, permissions, process definitions and approval gates decide whether an AI programme reaches production. Our coverage stays on that preparatory work, because it is the part most organisations discover late.
Enterprise software decisions that used to sit with IT now shape competitive position. This subject follows how those decisions are being made, and what changes when a platform choice becomes a strategic one.
Reading about a subject is the first step. LGS can take it from understanding the opportunity to structuring and executing the next move.
