Does AI actually understand what I ask it?

Don't assume it understands you the way a person would. A language model splits text into tokens and estimates a probability distribution for the next token, based on patterns learned in training and the context you provide. The fluent answer can still misread your request or contain false information — so check important output in proportion to the consequences.

Who is responsible if the AI gets something wrong?

Your business remains responsible for deciding whether to publish or act on an AI output — though legal liability is fact-specific and can also involve a provider or another party. An EU business using an AI system under its authority is generally a deployer under the EU AI Act, with obligations depending on the system and use; contracts, consumer and product-liability law, GDPR and professional duties can all matter too. In practice: treat every consequential answer as a draft a qualified person must approve.

What AI is — and what it isn't

Modern AI tools are not magic and not a thinking colleague — they are statistics. The assistants you have probably met — ChatGPT, Claude, Gemini — are products built around large language models, often combined with search, file handling and other tools. The model itself splits text into small pieces called tokens, represents them numerically, and estimates a probability distribution for the next token; the system picks or samples one and repeats. Next-token prediction is the foundation — a modern assistant layers post-training, instructions, your supplied context and sometimes external sources on top. What the fluency does not prove is understanding: don't assume the model grasps your intent, your situation, or your business beyond what's in front of it.

The right mental model

Think of AI as a very fast, tireless drafting assistant with broad pattern-based knowledge and no accountability — knowing only the business context you've actually given it, which may be incomplete or wrong. It hands you a draft in seconds; you provide the judgment and the sign-off. That framing protects you from the central trap — treating fluent output as authoritative. It gets you a useful starting point remarkably fast, and it cannot reliably tell you which parts are true.

What it is good for

Drafting emails, quotes, and meeting minutes so you start from text instead of a blank page.
Summarizing long documents or contracts into plain language — as a map for navigating the original, never a replacement for it: summaries drop exceptions and qualifications, so check the clauses that matter in the source (and mind what you upload — see below).
Generating ideas for social posts, campaigns, or job ads — followed by review for accuracy, discrimination, accessibility and brand fit before anything runs.
Structuring a messy customer inquiry and suggesting the questions you should ask back.
Making baffling official letters readable — then verifying the meaning, dates and required actions against the original, because a paraphrase can shift legal meaning.

What it is not for

Legal or tax advice. It does not know your accounts or your jurisdiction.
Executing binding actions — bookings, invoice approvals, payments — without explicit authorization and human confirmation. (Drafting and organizing financial paperwork is fine; unchecked authority over money is not.)
Facts you cannot check yourself — prices, deadlines, names, legal clauses.
Consequential decisions, unless a qualified person reviews the evidence, checks the output, and stays responsible for the call.

Hallucinations are a known failure mode

A language model generates plausible continuations; it does not guarantee its claims are true. It can confidently invent court rulings, legal paragraphs, and supplier names — convincingly, because the same process that produces its accurate output produces its inventions. Treat every concrete claim as unverified until checked against a real source. There is a whole method for catching these in spotting AI hallucinations.

Treat AI as a fast assistant that drafts while you keep the judgment, and it becomes one of the most useful tools in the office; treat it as an oracle and you create a serious risk of publishing or acting on false information.

What to do

Use AI for first drafts and summaries, never for final, unchecked output.
Check every fact, name, number, and date against a trusted source before it leaves your desk.
Keep confidential and personal data out of any AI service your business hasn't approved — contract, retention, security and GDPR safeguards verified, paid tier or not — see what data is safe to put into AI.
Build review into every AI task, matched to the consequences: a quick look for routine wording, source verification and a qualified reviewer for legal, financial, employment or customer-impacting work — the discipline is human in the loop.
Learn to brief it properly with better prompts — a clear brief improves the answer, though no prompt can make unsupported information reliable.