HyperTeamsThings to watch out for
What you will learn Three limits of AI, and how to decide what you must verify.
This chapter is not here to scare you. Knowing how far you can trust it actually makes it easier to use. It is like knowing where your car's blind spots are — that is what lets you drive relaxed.
Limit 1 — It invents things, plausibly
The most important limit.
What happens
When AI meets something it does not know, it sometimes does not say "I don't know" and instead produces something plausible. This is usually called hallucination.
The problem is that a wrong answer arrives with exactly as much confidence as a right one. You cannot tell them apart by tone.
How it actually shows up
AI cannot possibly know your company's revenue. And yet a number appeared. In the course of producing something shaped like an answer, it filled in a plausible figure.
Or this:
Those books may not exist. Title, author, and year all look plausible; look them up and they are not there.
Why this happens
In what AI is we described AI as "a program that got very good at guessing the next word." A plausible answer is a more natural continuation than "I don't know," so sometimes it goes that way.
What to do about it
| For this | Do this |
|---|---|
| Numbers, dates, statistics | always verify |
| Names, book and paper titles, legal citations | always verify |
| Internal company information | it cannot know. You have to supply it |
| Polishing, summarising, translating | generally safe (it works on what you gave it) |
| Suggesting ideas | safe (you are choosing anyway) |
Rule of thumb: work that processes material you provided is safe; work where AI has to fill in information it does not have is risky.
One thing that helps Adding "if you're not sure, say you don't know" reduces how often it invents things. It does not eliminate it.
Limit 2 — It may not know current information
What happens
AI is built from material up to a certain point in time. It does not know what happened afterwards.
What to do about it
- Facts about today (rates, weather, news, share prices) → use search
- Rules or regulations that recently changed → verify
- Things that rarely change (Excel functions, writing, general knowledge) → fine
That said, AI with tools attached is different. Connect it so it can search or look through internal material directly and it can answer with current information. That is tools, covered later. At this stage, assume "by default it does not know."
Limit 3 — Before you paste in company information
What to be careful about
You need to know where what you type goes. It differs by service.
Be especially careful with:
- Customer personal data (names, contact details, national ID, card numbers)
- Contracts, unpublished financials
- Passwords, API keys, connection details
- Business plans not yet announced
What to do about it
1. Check your company's policy first. There may already be rules.
2. Strip what is not needed. If you want a contract reviewed, the company name and the contact's name can go. The clause text alone is enough to review.
3. If it is for work, use a work environment. A personal account and an environment your company has contracted for handle data differently. Some cases need an air-gapped setup.
4. Check whether your conversations are used for training, and turn it off if you can. Depending on the service, what you type may be used to train the model further. Most have a setting for it somewhere.
The default differs by service and by plan. Personal plans tend to have it on; an environment your company has contracted for tends to have it off. So "our company has a contract, so we are fine" and "my personal account must be the same" are two different statements.
Once, right after you sign up. Turning it off usually does not reach back to conversations you already sent, so the order matters — turn it off, then paste in company material.
So when do you verify?
It comes down to one question.
What happens if this is wrong?
graph TD
A["You got an answer"] --> B{"What if it's wrong?"}
B -->|"just redo it"| C["Use it as-is<br/>polishing · ideas · summaries"]
B -->|"money, trust, or law"| D["Always verify<br/>numbers · external docs · regulations"]- The wording in an email draft is awkward → fix it. Just use it.
- An amount going to a customer is wrong → that is an incident. Always verify.
This same test is used at the organisational level later — it is exactly how you decide where to put human approval.
Common questions
"If I can't trust it, why use it?"
Separate producing a draft from final judgement. AI takes the first, a person takes the second. Editing a draft is far faster than starting from a blank page, and that time saved is larger than the cost of checking.
"Checking takes longer than doing it myself."
Then that task is not one to use it for. You do not have to use it for everything. If verification costs more than writing, write it yourself.
This judgement matters because the same mistake repeats at organisational scale — the third item in how transformations fail is exactly this case.
Check yourself
1. AI answered "our revenue last year was $4.7 million." What is wrong?
Answer
AI cannot know your company's revenue. Instead of saying so it produced a plausible number. That is hallucination, and because a wrong answer carries the same confidence as a right one, tone gives you no signal.
2. What separates safe use from risky use?
Answer
Processing material you supplied is safe (summarising, translating, polishing); filling in information AI does not have is risky (numbers, sources, factual claims).
3. What single question tells you whether verification is needed?
Answer
"What happens if this is wrong?" If you can just redo it, use it. If money, trust, or law is on the line, always verify.
That is individual use
You have finished “New to AI”. You should now be in this state:
- You know what AI is and how it differs from search
- You know what in your work you could use it for
- You have used it yourself and know how to get the answer you wanted
- You know how far to trust it
From the next part the story moves from the individual to the organisation. One person using it well and a company changing how it works are different problems, and most organisations stall in between → Adoption is not transformation