AI Demystified: 10 Simple Answers Every Executive Needs
Generative AI is powerful but confusing. Business leaders don’t need technical jargon, just clarity. Here are 10 answers to the toughest AI questions executives keep asking in boardrooms
During my workshops, I receive dozens of questions about how large language models (LLMs) and generative AI tools really work. In our book Building Creative Machines, we answered hundreds of them in Chapter 12, Top Questions from the Community.
However, as time passes and as new workshops and executive sessions continue, leaders are still asking the same questions. That’s a good thing: it means these are the right questions to ask.
Here, I’ve collected the Top 10 Questions from Executives who want to understand enough about AI to make smart business decisions. You don’t need to be a technical expert. You just need a clear picture of what’s happening under the hood.
1. How does an AI know when to stop writing an answer?
Short answer: It doesn’t decide on its own. The system running it tells it when to stop.
Explanation:
An AI like ChatGPT writes text step by step, one small piece at a time (these pieces are called “tokens” — think of them like puzzle pieces of words). As it is written, the system checks for stopping rules:
Sometimes the AI predicts a special “end” token that means “I’m done.”
Sometimes the system cuts it off when it reaches a maximum length.
Developers can also set their own rules — for example, “stop after a bullet list.”
Therefore, stopping is a joint process: the AI predicts, but the system decides when to terminate it.
2. If I correct an AI’s mistake, does it learn immediately?
Short answer: No, it doesn’t learn on the spot.
Explanation:
If you correct ChatGPT today, it won’t update its knowledge instantly. Your feedback may help the company improve future versions (if you choose to), but that process can take weeks or months. Some apps can remember personal details (such as your name and preferences), but that’s memory for personalisation, not real learning.
3. How can AI “remember” things from past conversations?
Short answer: The AI itself doesn’t remember; the app memory does.
Explanation:
LLMs only see what you type in the current conversation. But some tools store your past chats in a memory system. When you start a new session, that system feeds reminders back into the AI as context.
So, it appears that the AI remembers last week’s conversation, but in reality, it’s being prompted by the software.
4. If an AI only knows things up to its training date, how can it answer questions about recent events?
In short, only if it has access to live information (mainly the internet).
Explanation:
LLMs are trained on data up to a cutoff point (for example, 2024). They don’t automatically know what happened after. Some tools, like ChatGPT with browsing capabilities, can search the web in real-time. Without live access, they may still make guesses based on older patterns, which can be incorrect.
5. If I upload my company’s policy document, will the AI use only that?
Short answer: Not completely.
Explanation:
Even if you give an AI a document, it may still mix in what it already “knows” from training. Techniques like retrieval-augmented generation (RAG) can make it focus on your document, but you can’t force it to ignore all outside knowledge.
6. If the AI gives me an answer with citations, can I trust them?
Short answer: No.
Explanation:
AI sometimes fabricates sources (“hallucinations”) or misuses real sources. Some systems try to double-check references, but mistakes still slip through. Best practice: always click the link or check the source yourself.
7. If modern AI can handle very long documents, do we still need RAG?
Short answer: Yes, RAG is still useful.
Explanation:
Even though new AIs can handle millions of words at once, giving them everything isn’t always wise:
They may miss important details buried in the middle.
More text = more cost and slower answers.
Quality often improves when only the most relevant information is fed in.
That’s why RAG, selecting and prioritising the right info, is (still) a valuable step.
8. Can we completely eliminate hallucinations (made-up answers)?
Short answer: No, not today.
Explanation:
AI generates text by predicting patterns, not by fact-checking against a database. That means hallucinations are built into how it works. Not a bug, a feature.
But we can reduce them with:
Careful prompts. Learn how to write them here
Feeding the AI your own trusted data (RAG)
Post-checking answers with external tools or rules
We can lower the risk, but we cannot eliminate it.
9. Since mistakes happen, how do we check answers efficiently?
Short answer: Mix automation with human review (“human-in-the-loop”).
Explanation:
For open-ended work (such as reports), humans should review, but not every answer; only the risky or important ones.
For structured tasks (like generating code or tables), you can test or validate automatically.
Some companies use an AI judge (a second model that checks the first). This is faster but not perfect; it can also hallucinate.
The best strategy is a balance: let machines check what they can, and have people review the rest.
10. Can I guarantee that AI gives the same answer every time?
Short answer: No.
Explanation:
Even if you ask the same question word-for-word, AI answers may vary slightly. You can reduce variation by:
Fixing the randomness setting (“temperature = 0”)
Locking the model version
Running the system yourself instead of through a vendor
But to truly guarantee identical wording, you’d need to store the first answer and reuse it. Otherwise, expect differences. Please read our detailed explanation about this here.
Executives don’t need to become AI engineers, but understanding these basics helps avoid costly mistakes. With the right mental model, you’ll know when to trust the AI, when to verify, and how to guide your teams in using it effectively.