Human skills take center stage as AI becomes a workplace staple

Programming is no longer a prerequisite for using artificial intelligence in daily work, but that doesn’t mean curiosity and access to a chatbot are enough. Market research shows that companies now require a new kind of training for professionals in fields like sales, HR, finance, and communications.

Current indicators of AI use in companies

Catho’s 2026 Trends Survey reveals that 58% of professionals used AI at work in 2025, a 19% increase from the previous survey. According to Eber Duarte, CTO of Redarbor Brasil, which owns Catho, companies are seeking employees who can interpret information, automate processes, and apply technology critically. He notes that even those not directly working with technology need to understand how AI impacts processes, customers, and results.

Behavioral skills gain importance in non-technical roles

LinkedIn’s 2026 Skills in High Demand in Brazil study shows that, alongside technical expertise in areas like large language models and cybersecurity, behavioral skills—such as active listening, systemic thinking, and strategic leadership—are becoming essential for integrating different parts of organizations. In practice, this means knowing how to craft effective commands for tools, validate responses, protect confidential data, and translate results for managers.

Critical judgment comes before technical skills

Gartner predicts that by 2026, half of global organizations will start requiring “AI-independent” skills assessments, fearing that overreliance on generative systems will weaken critical thinking. Recruitment processes are increasingly valuing problem-solving, evidence analysis, and the ability to make decisions without relying on technology. In other words, those who use AI without questioning its outputs risk losing ground in the market.

When programming still makes a difference

Fernando Mantovani, general manager of Robert Half in South America, argues that technological progress doesn’t diminish the importance of human skills. He believes professionals who combine automation, decision-making, and strategic vision gain a competitive edge.

However, in roles involving development, training, and implementation of models—such as AI engineering and machine learning—technical expertise remains essential. In these positions, programming logic and proficiency in languages like Python are still key differentiators in hiring and compensation.

How to start using AI without writing code

A practical approach for non-programmers is to select a routine task—such as summarizing reports, qualifying leads, or organizing data in spreadsheets—and test AI tools for that purpose. It’s helpful to measure time saved, compare the quality of results with manual work, and document what worked. This record helps demonstrate value in internal evaluations and job applications.