Field notes · Topic
Enablement.
Technology changes nothing until routines change. These notes are for leaders, HR and L&D teams and managers who want people to use AI well, safely and in their own language.
7 notes
Notes on this topic
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A useful system needs a new everyday practice.
Adoption doesn’t come from a launch email. It comes from new routines, champions who help colleagues, and managers who ask for the new output in their weekly meetings.
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AI literacy: what every employee should know before using AI at work.
AI literacy isn’t knowing how models work inside; it is knowing what they are good and bad at, which data must never go in, how to check an answer and when a person decides. All of it fits on one page.
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What to teach each role about AI, from the board to the front line.
One course for everyone teaches that the tool exists. A curriculum by role teaches when it helps, where its limits are and what each person is now expected to do differently.
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What to tell your teams about AI, and how to say it.
People fill silence with the worst case. Say early and specifically which tasks change, which decisions stay with people, what the time saved is for and what training comes, and give managers words for the questions they will get.
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Better prompts at work: context, examples and a way to check.
A good prompt is a good brief: context, the goal, an example of what good looks like, the format you need and how the answer will be checked. There are no magic words, and the best prompts become shared templates.
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What your software engineers need to learn to build AI systems.
Good software engineers already have most of what it takes. The new parts are an evaluation mindset, output that varies between runs, retrieval and data handling, and judgment about where a person decides, learned fastest by shipping one workflow with someone who has done it.
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Why most AI hackathons lead nowhere, and how to run one that doesn’t.
Many AI hackathons end with demos that vanish. Build the event around real workflows whose owners attend, approved examples, a small evaluation set per team and a path, agreed in advance, from the winning idea to a funded discovery or pilot.