Build the right mental model first is backwards
The standard advice for a senior leader is to build the right mental model before touching the tool. Read about how AI works, then use it. That order is backwards. The mental model forms out of the work, not before it. Pick one task off your own desk this week and the understanding arrives with it.
Why does every answer start with build the mental model first?
Because that is the shape of almost everything written about learning AI. 23 of 40 answers written for senior leaders open with building a mental model or understanding the fundamentals first.
The shape suits whoever is selling the learning. A curriculum needs something to teach in hour one, and concepts fill an hour for 40 people at the same time. An hour spent inside one participant's own board deck does not scale to 40. So the concept module gets built, and the buyer gets hour one whether or not hour one is where the buyer's problem sits.
The institutions are credible here. The top business school executive programs do strategy fluency well, and senior people come out of those rooms with language they can use in front of a board. The gap is structural rather than a failure of teaching. A cohort and a curriculum never reach your own files, your own category, your own numbers.
The advice is also safe to give. Nobody can be shown wrong for recommending fundamentals, and nobody fails at reading. You can read about tokens and context windows for a month and end the month exactly where you started, with juniors who use the tool daily and a view you still do not trust.
The mental model forms out of the work, not before it
The mental model forms while you work. I learned that the expensive way. In early 2023 I had just exited my startup and had time for the first time in years, so I took every AI course I could find. Most were too basic, 10 hours to get what should take 30 minutes. The rest assumed I could code. I can't, and never have. By 2024 I knew this way of learning was broken for busy executives.
What worked was much smaller than a course. One question of my own work each day. "What's the one business problem I need to solve today, can AI help me solve it better and faster? That question changed everything." The model formed one task at a time, and it was mine rather than a trainer's.
Here is why the order matters for a senior operator specifically. The skill you are short of is judgment about output. Knowing when the answer on the screen is wrong, and what to do next. That judgment only forms against work where you already hold the right answer. Read a definition of hallucination and you have a word. Run last quarter's category review through the tool and find the claim that is not in the data, and you have the instinct. You have carried a P&L for years. You already know what a wrong number looks like in your own category. Nothing in a concept module uses that, and it is the most valuable thing you own.
How do I get better with AI?
You get better by running your own recurring work through it and checking the output against something you already hold. One task, repeated, beats a month of reading.
Take the document you touch every month. A category review, a distributor claims summary, a monthly sales pack for the country head, a supply note that explains a service-level miss. Give the tool the real file, not a tidied version. Say what you want back in the form you actually circulate: a table, a one-page note, five slides. Then do the part most people skip. Check three claims against the source. Find the number that does not reconcile. Find the recommendation that would be wrong in your market for reasons the file never states. Write down where the output was accurate, where it was vague, and where it was wrong.
That hour is the whole curriculum. You have learned the tone of a wrong answer in your own category, which no general explanation can teach you. Do it again next month with the same document and a shorter instruction. By the third run you are writing the brief in four lines and getting something you can send, and you still have not read a definition of a transformer.
If you want the mental model anyway, write it yourself after five tasks. One page, your own words, what the tool is reliable for in your function and where you stop trusting it. That page is worth more than any reading list because it was built out of your work.
What else do senior leaders ask about getting better with AI?
Do I need to understand how large language models work?
No, not to use one well. You need to know what it is reliable for in your function and where it drifts. That knowledge comes from checked outputs on your own documents. The technical explanation is interesting, and it changes nothing about whether you catch a wrong number in next week's pack.
Can I practice on work I cannot put into a public tool?
Yes. Start with material that is already outside the company: published competitor pages, industry reports, a market you are studying, your own draft thinking. You build the same judgment on that work. Settle the data line with whoever owns policy before anything with customer or employee information goes in.
How long before I can tell a good output from confident nonsense?
Faster than you expect, if you check. Three or four real tasks where you verify claims against a source you hold, and you start noticing the patterns: the confident summary with an invented cause, the number that almost reconciles. Reading about accuracy does not produce that. Checking does.