Your actual job here
Somebody has asked you what your team is doing about AI. Possibly your CEO, possibly a board member who read something on a flight, possibly your own reports who have noticed that they are using it whether or not anyone approved it.
The unhelpful response to this is to go and learn how the technology works. You do not need to know how a transformer works to run a team that uses AI, any more than you needed to know how TCP works to approve a move to cloud email. What you need is the ability to make four decisions well, and to know which questions produce the information those decisions require.
This course is those four decisions.
Meet Kavita
Kavita heads customer operations at a mid-size general insurance firm in Mumbai. Thirty people, split across claims intake, policy servicing, and a small complaints desk. Volume is high, the work is text-heavy, and the team has been at ninety per cent capacity for two years.
Her CEO's question, at the end of a Tuesday review, was: "What's our AI plan?" She has one quarter to answer it and a budget she has been told is "reasonable, within reason".
She also has a fact she has not mentioned to anyone: at least eleven of her thirty people are already pasting customer correspondence into a free chatbot on their personal phones, because it makes drafting responses faster. Nobody approved it. No policy forbids it. This is true of almost every team of thirty in the world right now, and it is why "should we use AI" is rarely the real question.
The four decisions
Where does it fit? Which of your team's work is a genuine candidate, and which is not. This is a question about your operation, which means you are the only person who can answer it — and it is the decision most often outsourced to a vendor, whose answer will be "here".
Is it working? Whether a thing you deployed actually improved anything, measured in a way that would survive a sceptical CFO. Most AI deployments have never been measured against a baseline, because no baseline was taken before they started.
What is allowed? The rules your team operates under: what data may go where, what a human must check, who is accountable when it is wrong. Today, in Kavita's team, the answer to all three is nothing, which is a decision she made by not making it.
Who does what? Capability, roles, and the honest question of what changes about people's jobs.
Nothing else on your plate is really a fifth item. Vendor selection is a sub-question of the first, budget is a sub-question of all four, and "which model is best" is a question you should almost never need to have an opinion about.
What you actually need to understand about the technology
Three things, and they are all consequences rather than mechanisms.
It is fluent before it is correct. These systems produce well-formed, confident output regardless of whether the content is right. There is no wobble in the voice when it is wrong. Every process design decision in this course descends from that one fact.
It is excellent at text transformation and unreliable at recall. Summarising, drafting, restructuring, extracting from material you provide — strong. Remembering facts about your business that nobody told it — not a thing it does, though it will produce something that sounds like it.
It is probabilistic. The same input can produce different output. This is unremarkable for drafting and disqualifying for anything that must be identical every time, which is a distinction worth holding on to when someone proposes it for a calculation.
That is the whole technical foundation this course needs. If you want more, How AI Actually Works is the plain-English version and takes an afternoon, and everything in it is optional for what follows.
The frame worth carrying
You are not buying intelligence. You are buying a fast, tireless, occasionally wrong first draft.
Every good deployment is built around that sentence, and every bad one has quietly assumed something stronger. When a vendor's pitch requires the system to be reliably correct without a person checking, they are selling you something the technology does not currently do, whatever the demo showed.
The teams getting real value are not the ones with the most advanced tooling. They are the ones who found the tasks where a fast first draft is worth a lot and a mistake is cheap and visible, and who put a person at the point where it stops being cheap.
Do this today: find out, honestly, what your team is already using. Ask in a way that cannot get anyone in trouble — "I'm mapping this, not policing it". Whatever you learn is your actual starting position, and it is almost never zero.