Tech companies no longer look only for profiles that "know about AI": they look for people who know how to work with AI. When reviewing hundreds of real postings on LinkedIn, Indeed, and job portals, a clear pattern appears: the most requested skills are not theoretical, but practical and cross-cutting to almost all roles. This article organizes what they are and where the gap is between what companies ask for and what candidates offer.

Understanding what's asked for lets you better prepare your interviews and prioritize what to learn first. Because the question "what AI skills should I learn so as not to fall behind?" today has a fairly concrete answer.

The most requested AI skills today

• Applied prompt engineering: knowing how to ask a model the right thing to get useful and reproducible results.

• Automation with AI: integrating models into workflows with tools like n8n, Make, or Zapier.

• AI-assisted data analysis: using AI to explore, clean, and visualize data faster.

• Responsible use and judgment: detecting errors, biases, and "hallucinations", and knowing when NOT to trust the model's output.

• Technical fundamentals: understanding what an LLM, an embedding, or an agent is, even if you don't program the model from scratch.

What's interesting is that many of these skills are asked for in roles that are not AI roles: marketing, sales, product, design. AI became a horizontal competency, as Excel was at its time.

What's asked for according to the role

If you want to go deeper, we already covered the 5 AI skills most in demand by companies, a good complement to prepare your profile.

The gap between what they ask for and what candidates know

The big bottleneck is not the theory, it's the application. Many candidates know "what" ChatGPT is, but few can show a concrete case where they used AI to solve a real business problem. That distance between knowing and applying is exactly what companies evaluate in interviews.

Market data confirms it: reports like the World Economic Forum's Future of Jobs Report and McKinsey's State of AI place AI competencies among those with the highest growth in demand, while the supply of trained talent advances more slowly. Translated: those who demonstrate practical AI skills have an enormous competitive advantage.

How to prepare for interviews

The key is to arrive with evidence. Prepare to answer with examples: what tool you used, what problem you solved, and what result you obtained. Keep on hand a mini portfolio with two or three cases, even if they are personal projects. Practice explaining decisions ("why did you choose this tool and not another?") because that's where judgment shows, which is precisely what an algorithm doesn't have.

Recommended Coderhouse courses

Closing the skills gap is a matter of guided practice. These courses cover different levels:

• Base: the Introduction to Artificial Intelligence Course gives you the conceptual framework that companies assume you already have.

• Applied: the AI Automation Course teaches you to integrate AI into workflows, one of the most requested skills.

• Advanced: the AI Engineering Course is for those who want to build AI solutions, not just use them.

Frequently asked questions

Do I need to be a programmer to have valued AI skills?

Not necessarily. Many in-demand skills (prompt engineering, no-code automation, use with judgment) don't require programming. They help, but they're not mandatory in non-technical roles.

Which AI skill is it a good idea to learn first?

Applied prompt engineering is the best starting point: it's cross-cutting to all roles and shows results fast. Afterward it's a good idea to add automation.

Do companies ask for AI certifications?

They value practical evidence more than a certificate itself. That said, a course with projects gives you both the skill and the demonstrable material for your portfolio.

How do I demonstrate AI skills if I don't have work experience with it?

With your own projects: automate a task, create content, solve a real problem, and document the process. A concrete case is worth more than saying "I know how to use AI".