Artificial intelligence boosts productivity but requires expert judgment

Tasks that once took hours of research can now be completed in minutes. A first draft of a text can be generated in seconds, and a programming tool can suggest solutions that would otherwise take time to develop manually. With increasingly sophisticated tools, artificial intelligence expands what a professional can produce alone.

However, this progress raises a less obvious question: the more technology can do, the more important it becomes for someone to know how to evaluate the results.

Producing an answer is no longer the only step in the process. Understanding whether it makes sense, recognizing errors, and deciding how to apply it requires knowledge that does not come automatically with access to the tool.

Why professional experience is becoming even more important

A study published in the scientific journal Organization Science, which involved 758 consultants from a global company, found that generative AI can improve productivity and the quality of deliverables in certain tasks.

However, when participants used the technology for tasks in which it did not perform well, the quality of the work declined.

The study shows that the benefits depend both on the task and on the ability to recognize the tool’s limitations.

This means that two people can receive similar answers from an AI and arrive at very different results.

Those who are experts in the field are better able to identify incorrect information, notice what was left out, and question conclusions that seem convincing but do not hold up.

Technology speeds up execution, but context still matters.

Using AI does not replace expertise in a field

The ease of using artificial intelligence tools can give the impression that all you need to do is learn to write good commands to perform any task.

However, efficient use depends on a combination of skills: knowledge of the field, ability to formulate questions, critical reading, and familiarity with the possibilities and limitations of the technology.

A study published in the journal Management Science suggests that mastery of algorithmic tools can complement professional experience.

The research highlights the importance of disseminating these skills among specialists from different fields, rather than concentrating them only in technical teams.

How to prepare for this change

For those building a career, the path involves combining practice with continuous learning.

It is worth choosing tools that are appropriate for your field, testing applications in real tasks, and comparing the results with known quality criteria.

The ability to explain decisions and take responsibility for results becomes more important in this scenario.

As execution becomes more accessible, the knowledge that enables one to guide, review, and improve work may become even more valuable.