In 2026, artificial intelligence is no longer a technological novelty — it becomes a key element of business and digital infrastructure. Organizations are moving from experimenting with AI to implementing it at scale across operational, decision-making, and product processes.
With this transformation, data becomes a strategic foundation — its quality, governance, and the ability to interpret it determine competitive advantage and the effectiveness of AI systems. Based on this data, our AI assistant generates responses to our queries. And this is where we reach a critical point: not everyone can effectively handle the information provided by their AI assistant. Many assume that the information is correct, without deeper analysis or reflection — until, during a business meeting or even a casual conversation, someone points out that the information is false, fabricated, or — using technical terminology — a model hallucination.
AI IS BASED ON Probability, Not ON FACTS
Generative AI models are built on vast amounts of data and probability. They analyze our prompt and generate a response based on the likelihood of certain word combinations appearing within a given context.
The creators of these models strive to maximize this probability, but it will never reach one hundred percent. As a result, errors — or hallucinations — inevitably occur in the details.
Therefore, a key aspect of using AI tools is the ability to critically evaluate the answers obtained. This competence requires the ability to:
- read the information,
- understand its context,
- interpret its meaning,
- draw appropriate conclusions.
Data Literacy as an Infrastructure Competence
The ability to understand and interpret data — known as data literacy — is no longer limited to analysts working in BI departments. It is becoming a universal competence — a foundation for everyday functioning in a world where nearly every decision has numerical justification.
This is no longer an additional “nice-to-have” skill. It is a prerequisite for conscious action — both professional and personal.
Modern users cannot afford to ignore data. They are surrounded by it and, willingly or not, must confront it daily. Financial, health-related, consumer, organizational and sales data constantly influence our choices.
They show us how much we earn and how much we spend. They inform us about the quality of our sleep and the condition of our bodies. They indicate which products customers purchase and which business areas are growing faster than others.
We can ignore this data. We can also uncritically accept the first interpretation — often generated by an algorithm. However, only conscious analysis gives data real value. In each of these areas, the key factor is the ability to interpret data properly — understanding context, proportions, limitations and the consequences of decisions made.
In an era where AI can generate answers in seconds, the human ability to understand data becomes the stabilizing element of the system. Without it, technology remains fast — but not necessarily accurate. With it, technology becomes real support in making responsible decisions.
Practical Examples of Data Literacy
Let us look at our finances. We are in the tax settlement period. By analyzing our annual tax declaration, we can prepare a summary of the previous year — for example, calculating our average income. It is important to remember the possibility of applying tax deductions, which also requires analyzing available options, understanding which deductions apply to us and completing the relevant sections correctly.
We must ultimately verify this ourselves. The system will not do it perfectly for us. It may attempt to, but errors are common. Of course, an accountant can handle this if we use such services. Nevertheless, it is always worth reviewing how the settlement has been prepared.
Staying with finances, we can go further. If we have income, we also have a household budget that requires management. Analyzing how we allocate our hard-earned money — bills, rent, loans, groceries, and other expenses — helps us plan appropriately.
When analyzing expenses, it is important to remember several factors. Comparing month to month can be misleading. Months have different numbers of days. For example, in February we may spend less simply because it is shorter. Holidays and public breaks also influence spending patterns. Savings from one period can be allocated to future expenses, such as vacations or planned events.
Budget analysis also allows us to prepare for unexpected situations, increasing our financial security.
Another example concerns what many of us wear on our wrist: a smart band, smartwatch, or similar device. These devices monitor our daily activities — steps, heart rate, oxygen saturation, sleep quality, and more — all of which can later be analyzed in a connected mobile application.
How many people truly take the time to analyze this data and draw conclusions? To change something — if possible — in order to improve health parameters? We do it for our health and longevity.
The challenge is that applications do not provide fully personalized benchmarks, as many variables must be considered. In such cases, we need to consult external sources or professionals — for example, a doctor — to assess whether the measured parameters are appropriate.
It is worth doing — for ourselves.
The European Context
According to Eurostat data, approximately 85% of Europeans living in large cities possess basic skills in reading and understanding presented data. In smaller cities, this figure is around 80%, and in rural areas approximately 78%.
In Poland, the differences between regions are more pronounced: 88% in large cities, 83% in smaller towns, and 77% in rural areas. Meanwhile, countries such as the Netherlands, Iceland, and Norway show almost no regional differences, with data literacy levels reaching 96–98%.
Although Poland’s results are relatively good, there remains significant room for improvement — particularly in raising awareness of the importance of understanding what we read, how we analyze charts, and what conclusions we should draw.
Conclusions
In summary, full implementation of AI tools — both professionally and personally — requires more than access to technology. It requires maturity in reading, understanding, and verifying the information we receive.
Delegating responsibility for assessing data accuracy solely to an algorithm may lead to undesirable consequences — from minor operational errors to serious decisions based on inaccurate or incomplete premises.
Therefore, results generated by AI tools should always be confronted with the user’s knowledge, experience, and — when necessary — additional sources.
This moment of verification distinguishes unreflective use of technology from its conscious application.
Through critical analysis, the decisions we make become more accurate and better grounded in real data rather than in its seemingly convincing interpretation. As a result, we improve efficiency, enhance the quality of our actions, and increase the safety of decisions — both in everyday matters and in key business contexts.
AI can significantly accelerate decision-making processes. Responsibility for their quality, however, remains with us, humans.
