Misuse of AI: asking without knowing why
An instruction like “improve my business” easily produces a broad and convincing response. However, it does not specify the problem, the available resources, or the expected result. The user risks spending time putting together a list of tips that does not correspond to their situation.
I would start with a narrower sentence: “I receive incomplete requests; help me spot the missing information in these fictional examples. » We then know what the tool must prepare, what material it works on and how to judge the result. Precision is not a magic formula: it simply makes the task controllable.
The CNIL and France Num offer factsheets for small businesss and small businesss, particularly on uses and precautions. Their interest is to bring the choice of tool back to a real need. The company remains responsible for defining useful work before seeking to accelerate it.
Publish more without adding knowledge
Producing ten generic texts is sometimes faster than writing a single useful article. But ten versions of the same banalities do not respond better to a reader. The risk is particularly visible in communication: a brand may become incapable of explaining what it really knows, because its language is so similar to that of all the others.
AI can help organize reasoning, spot repetition or suggest formulation. The material must remain identifiable: verified facts, authorized real examples, opinion of the author or fictional scenarios announced. Asking the tool to invent a customer success story to make the text more compelling destroys the trust the content should build.
For this blog, my opinion on the shops of Saint-Brieuc is therefore not presented as a survey of dozens of shops. The text dedicated to local commerce separates this view from municipal sources. This distinction matters more, to me, than the appearance of an article full of spectacular numbers.
Entering too much data into the wrong tool
Copying an entire document is convenient, but not all of its information is necessary for the task. A request for reformulation can often be tested on a fictitious example. A filing exercise doesn’t always need a customer’s name, address, or detailed history.
Before using internal information, you must know the framework authorized by the company and the conditions of the chosen solution. The CNIL details the questions to examine when using a generative AI system, including data and uses. Warranties depend on offering, settings and deployment; the name of a tool is not enough to deduce them.
My practical advice is to prepare a small test set without sensitive data, then have more demanding uses validated. This is not a demonstration of legal compliance. This is a way of not discovering essential questions after having distributed a file that you should not have transmitted.
Confusing a suggestion with authorization to act
An AI that prepares an email does not necessarily have to send it. An AI that offers a ranking does not necessarily have to delete elements that it considers unnecessary. Between producing a suggestion and triggering an action, there is a change of consequence that a fluid interface can make almost invisible.
Fictional example: an assistant prepares a response to a customer and spontaneously adds a 15% discount. The text is polite and consistent, but the delivery was never authorized. The problem isn't just fixed with better spelling. It is necessary to define what the assistant can propose, what he must not invent and who validates the commitment.
For first use, I would keep sending, publishing and important changes behind a human decision. Our article on real AI capabilities distinguishes what a model can write from what a connected system can execute. This difference helps set understandable boundaries.
Save time at the beginning and lose it later
A first response in thirty seconds can impress. If it then requires checking each line, searching for missing references and resuming the document, the gain is less obvious. The useful time to measure is that which leads to an acceptable result, corrections included.
Let's take a fictitious calculation: a task took 30 minutes. With AI, preparation takes 5 minutes, generation 1 minute, proofreading 10 minutes and corrections 8 minutes. The total is 24 minutes: the gain is 6 minutes, or 20%, not 29 minutes. This test only becomes interesting if the quality remains comparable.
Errors discovered later must also be counted. An incorrect reference in an order can cost more than several minutes of writing time. Our method of verifying an AI response handles content controls. Here, the complementary subject is organization: who controls, at what time and with what possibility of going back?
Losing the know-how that allows you to control
If we never do a task without assistance again, we can less clearly identify what is missing in the answer. This risk does not mean that we must refuse the tools. It invites us to maintain moments of explanation, quality references and people capable of taking control.
A team can, for example, reread together a rejected result and explain why. It can maintain a correct sample document, a list of essential points and a manual fallback procedure. The learning then concerns the profession as much as the instructions sent to the software.
I am in favor of an AI that helps us progress: ask for several leads, understand their differences, redo a calculation and explain the decision made. I am less convinced by the practice of accepting the first exit and no longer thinking about the subject. Immediate comfort should not make the company dependent on work that it no longer knows how to evaluate.
Establish a simple rule of good use
For each task, write four elements: what the tool receives, what it prepares, what a person checks, and what authorizes the final action. This description can fit on one page. It is useful if the people involved can understand it and apply it in a normal day.
Add a reason for stopping: missing information, inconsistent result, request outside scope or unauthorized data. The wizard should be able to leave a point unresolved instead of filling in all the blanks. An incomplete but honest answer is often preferable to an impeccable document that makes up what it needs to be.
After a few tries, deciding to continue, modify or abandon the use remains a good outcome. We do not need to make AI indispensable everywhere for it to be useful somewhere. My criterion is the work actually improved, not the number of automations that can be shown.
Frequently asked questions about the misuse of AI
Is using AI to write necessarily a bad practice?
No. It can help to structure and reformulate. The problem appears when the text replaces absent knowledge, invents facts or is published without appropriate control.
Does a very detailed instruction guarantee a correct answer?
No. It clarifies the expected work, but does not guarantee the facts or conclusions. Important information must remain verifiable and calculations controllable.
Should everything be automated after a few successful tests?
No. It is also necessary to test for incomplete cases and errors. The level of autonomy must correspond to the possible consequences and the human capacity for recovery.
How to spot a use that wastes time?
Measure the entire duration, from preparation to validated result, then subsequent rework. Compare it to a usual method for similar tasks and equivalent quality.
An idea to try.
Take an AI-assisted task and write who checks the result, what triggers the action and in which cases it is necessary to take control again.
