From AI Use to AI Competence: The Difference That Matters
Artificial intelligence use is growing rapidly. But the percentage of people using AI alone says very little about how well they actually know how to work with artificial intelligence.
In 2025, 32.7% of people aged 16 to 74 in the European Union had used generative artificial intelligence tools in the previous three months.
In Romania, the percentage was only 17.8%.
Among young people aged 16 to 24, usage was already much more widespread: 63.8% at EU level and 44.1% in Romania.
At the company level, the figures measure a different phenomenon and should not be confused with individual use.
In 2025, 20% of EU companies with at least 10 employees were using at least one AI technology, compared with only 5.2% in Romania.
Indicator, 2025 | European Union | Romania |
People aged 16-74 who used Generative AI | 32.7% | 17.8% |
Young people aged 16-24 who used Generative AI | 63.8% | 44.1% |
Companies with at least 10 employees using AI | 20.0% | 5.2% |
These percentages do not measure the same thing and should not be compared directly.
The methodology, the population analysed, and even the definition of “AI adoption” can significantly change the reported figures.
A useful comparative analysis of these differences is also published by Alice Labs - AI Adoption by Country 2026.
But regardless of which indicator we use, a much more difficult question remains:
How many of those who use AI actually know how to work with it correctly?
There is currently no credible global percentage that answers this question.And this is precisely where I believe the next stage of the discussion about artificial intelligence begins.
Having access to AI, using AI and having competence in using AI are three different things.
What you will find in this article
the difference between access to AI, AI use and AI competence;
why high adoption does not automatically demonstrate an understanding of the technology;
why the fact that a project was created with AI assistance does not mean that “AI did the project”;
what I have observed after approximately two years of professional AI-assisted work;
why prompts, rules and context reduce errors but do not eliminate them;
how a highly convincing answer can still be wrong;
real examples in which unverified AI-generated results have reached professional environments;
why the subscription used matters less than the competence of the person using the system;
what it actually means to know how to work with AI.
Table of contents
AI has become easy to access. Not easy to master
Access, use and AI competence are not the same thing
Most consumer AI use is free
“You did it with AI” does not explain the work behind the result
What I have learned after approximately two years of AI-assisted work
Why an answer that sounds correct can still be wrong
AI errors already have real professional consequences
Prompt Engineering helps, but there is no magic prompt
From Prompt Engineering to Context Engineering
Why professional expertise is becoming more important
An AI subscription does not solve the competence problem
What AI competence actually looks like
AI can accelerate both good work and mistakes
Frequently asked questions
Conclusions
1. AI has become easy to access. Not easy to master
One of the reasons Generative AI has been adopted so quickly is the simplicity of the interface.
You do not need to know how to program.
You do not need to understand the architecture of a Large Language Model.
You do not need to install complex infrastructure.
You open ChatGPT, Gemini, Claude, Perplexity or another system, enter a question and receive an answer.
This simplicity is one of the major advantages of the technology.
But it can also create a false perception: if AI is easy to use, then it must be just as easy to use it well.
It is not.
Eurostat also shows that, among people in the EU who had not used Generative AI, 14% said the reason was that they were not sure how to use these tools.
The main reason remained the perceived lack of need, mentioned by 64% of non-users.
Technical access is no longer the main barrier for a very large number of users.Competence, however, remains a separate issue.
2. Access, use and AI competence are not the same thing
The difference can be summarised simply:
Level | What it means | What it does NOT demonstrate |
Access to AI | You can use an AI tool | That you understand how it works or what its limitations are |
AI use | You formulate requests and obtain results | That the results are correct or well-founded |
AI competence | You know how to define the problem, context and sources and verify the result | That the system will never make mistakes |
This last distinction is important.
AI competence does not mean eliminating errors.
It means increasing the user's ability to prevent, identify and correct errors.
In the MirioDev analysis on AI adoption in Romania, I have already discussed the significant gap between Romania and other European countries in terms of AI use among companies.
But quantitative adoption is only part of the problem.
We can increase the percentage of people using AI without increasing, at the same pace, their ability to evaluate what AI produces.
3. Most consumer AI use is free
There is another factor contributing strongly to the expansion of AI: the very low cost of access.
The Menlo Ventures report “2025: The State of Consumer AI”, based on a study of more than 5,000 adults in the United States and estimates of the global market, estimates approximately 1.8 billion AI users worldwide, around 600 million of whom use AI daily.
More interesting for this discussion is the ratio between free and paying users.
Menlo Ventures estimates that only approximately 3% of consumer users pay for premium AI services, which means that approximately 97% use free versions.
It is important to treat this figure for what it is: a market estimate produced by Menlo Ventures, not a global census of all AI users.
But the order of magnitude tells us something important.
Hundreds of millions of people now have access, at no direct cost, to systems capable of producing texts, analyses, images, code, presentations, explanations and recommendations that can look highly professional.
That is extraordinary.
But this very accessibility can also create the impression that the result is as simple as accessing the tool itself.
4. “You did it with AI” does not explain the work behind the result
In my professional activity, I have already encountered this reaction several times:
“Ohhh, you did it with AI.”
The wording sometimes creates the impression that, if a project was created with the assistance of artificial intelligence, the work behind it becomes almost non-existent.
As if there were a button.
You press it.
You get the project.
And anyone with access to the same tool could achieve the same result.
The reality is very different.
A professional can use AI for research, structuring, analysis, comparisons, checks, generating alternatives or accelerating certain stages.
But the system must receive the right information.
It must be guided.
It must be corrected.
It must be verified.
And the final result must be evaluated by someone who understands the problem well enough to recognise an error.
The fact that an architect uses design software does not mean that the software designed the building.The fact that a photographer uses professional image processing and editing software does not mean that the software took the photograph.
In the same way, the fact that a professional project is created with AI assistance does not mean that artificial intelligence has replaced the expertise, research, decision-making and responsibility of the person building the project.
Working with AI assistance is not the same thing as letting AI do the work for you.
5. What I have learned after approximately two years of AI-assisted work
I have been working with the assistance of AI systems for approximately two years.
I am not talking about occasional use for a question or for generating a simple text.I am talking about professional projects in which, over time, information, working rules, approved models, restrictions, terminology, structures, previous examples and very clear criteria regarding the desired result have accumulated.
For certain activities, there are precise rules regarding what must be preserved, what can be modified, what information must not be invented, how prices and calculations must be handled, which sources are relevant, which structures must be respected or which information belongs to one project and must not automatically be transferred to another.
There is history.
There are examples.
There are accumulated corrections.
There is context.
And yet errors still occur. Sometimes an instruction is ignored.
Other times, a correct rule is applied in the wrong context.
Information that should have been preserved may be changed.
A claim that does not exist in the source may be introduced.
A requirement may be misinterpreted.
Or the result may be very well written and still solve a different problem from the one that was requested.This experience has also changed the way I look at working with AI.
The more I use these systems, the less I become inclined to verify less.
Quite the opposite.
I learn more clearly where errors can occur and what needs to be checked.
That is much closer to competence than simply being able to write a prompt.
6. Why an answer that sounds correct can still be wrong
One of the most misleading characteristics of Generative AI is the quality of its presentation.
A system can produce fluent text, a very clear table, convincing arguments, apparently logical calculations, firm conclusions and even references to sources.
And one of the central pieces of information may still be wrong.
Fluency is not proof of correctness.
a well-structured table does not demonstrate that the values in it are correct.
a detailed explanation does not demonstrate that the initial premise was correct.
a list of sources does not automatically demonstrate that the sources exist or that they support the claims being made.
And this problem becomes greater precisely when the user knows less about the field.
If you are a specialist, you can more easily notice that a conclusion does not make sense, that a figure is unlikely or that an important condition is missing.
If you are using AI precisely because you do not know the subject, you may not have the tools required to identify the error.
This creates an important paradox: The less you know about a field, the more impressive a well-formulated AI answer may appear and the more difficult it may be to notice when it is wrong.
7. AI errors already have real professional consequences
We are no longer talking only about amusing screenshots shared on social media.
AI errors are already making their way into professional documents and can have real consequences.
On 17 September 2026, Reuters reported that errors associated with the use of artificial intelligence had been identified in at least 1,395 federal and state court cases in the United States.
These included citations to non-existent cases, fabricated information and other errors introduced into court documents.
These cases are relevant not because legal professionals should not use AI.The issue is something else.
An AI-generated result was treated as verified information.
Using AI and delegating responsibility to AI are not the same thing.
You can use artificial intelligence for research, analysis, drafting or preliminary checks.
Responsibility for the final document, however, remains with the professional using it.
8. Prompt Engineering helps, but there is no magic prompt
A significant amount of AI-related content in recent years has focused on Prompt Engineering.
How do you formulate the request?
What role do you assign to the model?
How do you structure the instructions?
What examples do you provide?
All of these matter.
But the idea that there is a perfect formula that transforms any request into a correct answer is wrong.
AI Understanding - Prompt Engineering treats the prompt as part of a broader system: defining the outcome, clarifying assumptions, using trusted sources, establishing quality criteria and maintaining human verification where accuracy matters.
A better prompt can reduce ambiguity.
It cannot create information that does not exist.
It does not turn a weak source into a good source.
It does not guarantee that every rule will be followed.
And it does not transform a generated response into a verified fact.
That is why AI competence cannot be reduced to “I know how to write good prompts”.
9. From Prompt Engineering to Context Engineering
If I tell a system: “Create the best commercial presentation for my company.”I have formulated an instruction.
But the system does not automatically know:
which company it is;
what it sells;
who its audience is;
what its real advantages are;
which claims can be supported;
what tone it uses;
what must be avoided;
what result I consider acceptable.
This is where context becomes important.
The article Prompting is overrated. Context is what matters summarises five important elements: background, the actual working material, constraints, examples and success criteria.
For professional use, the difference can be viewed as follows:
Component | Role | Limitation |
Prompt | Tells the system what it needs to do | Does not automatically provide the information it needs |
Context | Provides data, rules, examples and restrictions | May be incomplete, contradictory or misinterpreted |
Verification | Confirms whether the result can be used | Requires criteria and evaluation competence |
More context can improve results. But context does not completely eliminate errors either.
In Before AI takes a task, test the whole chain, AI Understanding highlights an important problem: real-world tasks are often chains of operations, and the fact that the final result looks good does not demonstrate that every stage leading to it was correct.
An error at the beginning of the process can produce a highly coherent final conclusion.
And a very wrong one.
10. Why professional expertise is becoming more important
AI is sometimes presented as a technology that democratises expertise.
To some extent, it does.
A person can now obtain information, explanations and assistance in a very short time that would have been much more difficult to access in the past.
But access to information and expertise are not the same thing.
An experienced professional can use AI more effectively precisely because they can notice:
when a figure does not add up;
when a conclusion does not follow from the data;
when important information is missing;
when a source does not support the claim;
when a wording changes the meaning;
when the system has combined two different contexts;
when the answer is generic;
when the proposed strategy cannot work under real-world conditions.
AI can reduce the time required for execution but does not eliminate the need for professional judgment.
In many cases, it makes that judgment even more important because the volume of information and the speed at which results can be produced increase significantly.
11. An AI subscription does not solve the competence problem
The fact that most consumer AI use is free explains part of the speed at which the technology has spread.
But it would be wrong to reduce the issue to: free = weak and paid = good.
A more advanced model or plan may offer better capabilities, higher limits, access to additional tools and the ability to work with more complex contexts.
All of these matter.
But a more expensive subscription does not automatically give the user:
domain expertise;
project context;
the ability to formulate the problem correctly;
judgment;
the right sources;
experience;
the ability to verify.
A more capable system can increase the user's potential. It does not guarantee the user's competence.
12. What AI competence actually looks like
AI competence does not mean memorising 100 prompts.Nor does it mean knowing every feature of a tool.
For professional use, it means being able to control the entire process between the initial problem and the final result.
You need to be able to define what you want to achieve, select the relevant information, provide sufficient context, establish restrictions, distinguish verified data from assumptions and evaluate the answer.
There is, however, another essential ability: knowing when you cannot verify the result yourself.
If AI generates a conclusion in a field where you do not have the knowledge required to evaluate it, your level of trust should be different.
This is one of the reasons why competence in using AI cannot be completely separated from professional competence.
AI can extend expertise. It cannot assume it.
13. AI can accelerate both good work and mistakes
AI is an extraordinary accelerator.
It can reduce hours of research.
It can analyse large volumes of information.
It can compare documents.
It can identify patterns.
It can structure ideas.
It can generate alternatives.
It can help a professional move much faster from information to results.
But speed works in both directions.
If the problem is defined incorrectly, AI can accelerate an answer to the wrong problem.
If the source is wrong, it can very quickly build the wrong conclusion.
If context is missing, it can very quickly fill in the gaps.
If the user does not know the field, they can very quickly accept false information.
If the result is not verified, an error can be transmitted, published or introduced into a decision much faster than in the past.
AI can significantly accelerate a professional's work. But it can just as easily accelerate a mistake when the user does not know what they are doing.
This is the difference between use and competence.
14. Frequently asked questions
What does AI competence mean?
AI competence means the ability to use an AI system while understanding the task, context, sources, limitations and result well enough to evaluate whether the answer can be used.
What is the difference between using AI and knowing how to work with AI?
Using AI means interacting with a system and obtaining a result. Knowing how to work with AI means correctly defining the task, providing the necessary context, identifying missing information, recognising the system's limitations and verifying the result.
How widely is Generative AI used in Europe?
According to Eurostat, in 2025, 32.7% of people aged 16 to 74 in the EU had used Generative AI in the previous three months.In Romania, the percentage was 17.8%. For the 16-24 age group, the figures reached 63.8% in the EU and 44.1% in Romania. Source: Eurostat.
Do most users use AI for free?
The 2025 Menlo Ventures report estimates that approximately 3% of consumer AI users pay for premium services, which would mean that approximately 97% use free versions. This is a market estimate, not a global census.
Does paid AI automatically provide correct results?
No. Models and plans may offer different capabilities, but the price of the subscription does not guarantee the correctness of an answer. The model, prompt, context, sources, nature of the task and verification of the result all continue to matter.
Does a good prompt eliminate AI errors?
No. A good prompt can reduce ambiguity and improve the result, but it does not guarantee factual accuracy and does not replace verification.
Is human expertise still necessary when using AI?
Yes. In professional activities, expertise is what allows the user to recognise incorrect information, omissions, misinterpretations and situations in which an AI-generated result should not be used.
15. Conclusions
AI has dramatically lowered the barrier to accessing tools capable of generating, analysing and structuring information.
This is one of the major technological changes of recent years.But accessibility can also create confusion.
The fact that a tool is simple to use does not mean that it is simple to use correctly.
My experience working with AI has demonstrated exactly this.
More context improves results.
Rules improve results.
Models and examples improve results.
Well-constructed prompts improve results.
More capable tools can improve results.
But none of these components completely eliminates the possibility of error or the responsibility of the person using the result.
That is why the next stage of AI adoption should not be measured only by the number of people or companies that say they use artificial intelligence.
We should increasingly talk about AI competence.
About the difference between asking and verifying.
Between generating and understanding.
Between receiving an answer and knowing whether that answer can be used.
Because using AI does not automatically mean knowing how to work with AI.
And as the technology becomes available to more and more people, this difference becomes increasingly important.
AI does not eliminate competence. It makes it even more important!
