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Shadow AI: your staff already pasted it into ChatGPT
Blocking the tools does not remove the demand, it removes your visibility. What actually works is giving people the same models somewhere you can see.
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Tips, trends, and deep-dives on AI, security, and the future of work.
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Running the same question across several models at once is not a benchmark. It is a way of seeing which disagreements matter and which are just style.
Nine questions that separate a vendor with an answer from a vendor with a slide. Most have nothing to do with the model and everything to do with your files.
Pointing a model at your documents solves a narrow and valuable problem. Most disappointment with retrieval comes from expecting it to solve a different one.
Licences get bought, logins get issued, and usage plateaus in week three. The gap is rarely the tool itself, and a one-off workshop does not close it.
What to decide before the first licence is issued, what can safely wait, and the three decisions that prove expensive to change once people are working.
Posts come out of questions that arrive more than once, usually from someone already partway through a rollout and finding that the hard part was not the technology. They are written for the person who has to make the decision and then live with it, rather than for a feed. There is deliberately no news coverage here. A model released this week will have been superseded by the time it could matter to a policy you are still drafting, and there are faster places to read about it. What changes slowly is what is worth writing down: how a decision ages, where a rollout loses momentum, and which questions to ask a vendor before the documents are already in their system.
Where this connects
Most of what is argued here in a thousand words is taught properly in the course library, which is the difference between agreeing with a post and being able to act on it on a Monday morning. The FAQ covers the shorter questions, and the contact page reaches a person if yours is not there.