The Proper Use of AI: Why Trusting It Without Scrutiny Is Dangerous
Artificial intelligence inspires, impresses and is becoming an increasingly commonplace part of our daily lives. Whether it’s drafting text, analysing processes or carrying out legal assessments, many tasks suddenly seem simpler, quicker and more efficient. Yet it is precisely this apparent ease that harbours risks. In this article, we use a real-life example to illustrate the dangers of uncritical AI use.
What happens if you rely on AI-generated content without checking it first? What does this mean for accountability, quality and trust, particularly in a professional context? Read on to find out why the human filter of experience remains irreplaceable, what you should look out for in AI-generated content, and how you can integrate artificial intelligence effectively into your work without losing sight of reality.
Artificial intelligence has gone from being a topic of the future to an everyday tool in the blink of an eye. Texts, concepts, analyses, code, legal assessments or market overviews – everything seems to be available at the touch of a button. That is precisely where its greatest strength lies. And, at the same time, its greatest danger. A customer recently shared an AI chat log with me – complete with references. He presented the content as fact. However, upon manual verification, it turned out that the AI was completely wrong. The sources cited did not support the statements; in some cases, they even contradicted them.
The problem: the customer had relied on AI. Without checking it themselves. Without putting it into context. Without filtering it through their own experience. This example is not an isolated case, but rather symptomatic of a trend that we are currently seeing in many companies, agencies and among the self-employed. That is why it is high time we talked about the correct use of AI – beyond the hype and the promises of productivity and efficiency.
AI systems such as ChatGPT, Claude and Gemini are not sources of knowledge in the traditional sense. They are probability engines that generate plausible answers based on training data, patterns and context. This leads to a dangerous fallacy: „It sounds logical, so it must be true.“ However, logic, language and factuality are not the same thing.
AI can:
- to phrase convincingly
- come across as self-assured
- simplifying complex issues
- Cite sources (or at least make it look as if you have)
However, AI cannot:
- Taking responsibility
- Guarantee the truth
- Understanding context like someone with experience
- distinguish between established knowledge and a plausible assumption
This is precisely where users’ responsibility begins.
The first – and perhaps greatest – risk associated with the use of AI arises where there is a lack of specialist experience. Anyone who is new to a subject does not yet have an internal reference framework:
- no comparative figures
- no typical fault patterns
- no gut feeling for inconsistencies
The AI provides a clearly structured, fluently worded answer – and it comes across as competent. Without experience, there is no internal warning signal to say: „Hang on a minute – that sounds good, but something doesn’t quite add up here.“
Particularly in areas such as
- Law
- Taxes
- Medicine
- IT security
- Funding
- Employment law
A plausibly incorrect answer can have serious consequences.
The correct use of AI therefore requires: AI does not replace basic professional competence – it enhances it. Those who know nothing do not automatically acquire knowledge. They are given text.
The second risk is more subtle – and affects even experienced users. AI is fast. Very fast. And that is precisely what leads people to skip verification steps that used to be taken for granted.
The new way of working:
- Ask a question
- Received a reply
- Forward/Embed/Publish
What’s missing?
- Source verification
- Cross-check
- Validation check
- Comparison with one’s own experience
The efficiency of AI is becoming an excuse for a lack of accountability. Why is that dangerous?
As soon as AI results are passed on without being checked,
- Support turns into a risk
- the responsibility shifts to the tool
- in the long run, one’s own credibility suffers
And when it comes to customer relations, the following applies: Errors made by AI are always the fault of the person using it. The correct use of AI therefore means: GSpeed must never take precedence over resilience.
Many users treat AI as if it were a better version of Google Search. However, modern AI systems offer a wide variety of modes – of very varying quality.
Example: Deep Research
Features such as
- In-depth Research
- Browsing
- Citation style
- Source verification
are often
- didn't understand
- not used
- misjudged
This leads to two problems:
- Users expect depth, but only superficial knowledge is generated
- Users think they have done their research – but have merely generated text
To use AI properly, you need to know that,
- When a quick draft will do
- when thorough research is necessary
- When AI is unsuitable
Not every task is an AI task.
A key principle for the proper use of AI is: AI enhances what is already there.
- Expertise leads to greater productivity.
- Experience becomes scalable.
- Structure is becoming faster.
However:
- What is not known remains unknown
- Uncertainty is expressed convincingly
- Errors become reproducible
That is why AI is no substitute for:
- Thinking
- Responsibility
- Experience
- Reflection
It becomes particularly dangerous when AI cites sources.
Because:
- Sources may be misinterpreted
- Statements cannot appear in their original form
- Correlations may be fabricated
- Quotations may be true to the spirit of the original, but not verbatim
Many users simply check whether a source has been cited. They do not check whether the source is reliable.
Using AI correctly means:
- Open original source
- Read the statement there for yourself
- Check the context
- Please note the date and whether the information is up to date
Anything else is unwarranted trust.
A common fallacy: „The AI said that.“ No. You’ve used it.
Whether:
- Presentation
- Offer
- Strategy paper
- Blog post
- Customer recommendation
The responsibility always lies with the individual. AI is not an author. Not a consultant. Not an expert. It is a tool.
The risks increase particularly sharply when:
- Decisions that have legal consequences
- financial implications arise
- Customers affected
- Knowledge is passed on
- authority is implied
In such cases, AI should:
- just prepare
- just organise
- just support
Never make a decision.
1. Never use AI without context
At least have a basic understanding of the subject.
2. Always check your results
At least on a random basis.
3. Read the sources yourself
Don’t just take over.
4. Choose AI modes carefully
Quick Answer ≠ Research.
5. Taking responsibility
AI output is your output.
6. Inform customers
Transparency builds trust.
In future, what will matter is not who uses AI, but who AI correct uses. The correct use of AI is becoming a core competence, much like Research skills, Media literacy or critical thinking. Companies, the self-employed and teams must learn to, To question AI, to interpret results and Accept the limitations of the technology. Otherwise, this useful little aid will quickly become a pitfall with costly consequences.
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