What AI Skills are in High Demand?: Top 5 You Need to Learn

AI Skills

AI aims to be the next “revolution” that will completely change the way we work, and just as we learned from the Industrial Revolution, the only thing left for you is to start using these technologies to your advantage.

This isn’t an “every man for himself” scenario, instead, it’s a wake-up call to get hands-on with AI skills.

According to the “AWS Accelerating AI Skills US Report,” 73% of employers prioritize on hiring leaders with AI skills, as they could improve productivity through automation while also improving the work quality.

But if that doesn’t convince you, let me tell you that some niches like IT and Marketing could get a salary increase from 30 to 40% if they know how to work with AI.

Convinced? Yeah… that’s what I thought.

But before going all in with the AI school, we should ask, what are the AI skills in high demand? Where should you focus more or what you can start learning to be ready for the future of work?

Let’s find it out in this article.

Prompt Engineering

Who is going to train AI language models? That’s right prompt engineers, which is no wonder why this position will be highly demanded for years to come.

As organizations across various sectors adopt AI technologies to streamline operations, improve customer service, and generate actionable insights, they urgently need more specialists who can effectively communicate with these systems.

According to Grand View Research, North America dominated the market and accounted for over 34.0% share in 2023, and with the continuous rise of AI and ML startups there’s room for many aspiring prompt engineers.

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A prompt quality and specificity can significantly pact the AI’s response, making this skill so crucial for leveraging this technology.

For this goal start learning NLP (Natural Language Processing) to let text models help students with their essay writing, allow chatbots to answer people’s most elaborate questions, and image generators to let designers unleash their imagination.

Machine Learning

Traditional programming isn’t enough for today’s computational advancements and data availability, now we need systems that learn and make decisions based on patterns and data.

Every industry can benefit from machine learning applications as its adaptability brings invaluable applications that go from predictive analytics in finance to personalized recommendations in e-commerce, and even complex problem-solving in healthcare diagnostics.

The rapid expansion of big data led to a significant increase in the demand for machine learning. This factor creates the need for skilled professionals to develop algorithms that can effectively handle large datasets and extract valuable insights impossible with traditional methods.

This scenario means that machine learning is no longer a “cool skill” for companies, but more a necessity to keep a competitive edge.

Machine learning is also a diverse skill as applicants should develop different techniques like supervised learning, unsupervised learning, neural networks, and reinforcement learning to navigate this dynamic landscape and drive their AI initiatives forward successfully.

Data Analysis

Contrary to popular belief, AI won’t replace data scientists anytime soon.

To back this affirmation, The Bureau of Labor Statistics states that data scientists might experience a 35% change from 2022 to 2035, meaning there will be about a third more jobs in data science than in 2022.

Various industries, such as marketing, e-commerce, and insurance, will continue to require individuals who can interpret the vast amounts of data generated by AI and translate it into actionable insights.

But that’s not it, as this skill can also help you utilize machine learning algorithms and statistical models to analyze data, uncover patterns, and make predictions about future outcomes.

Computer Vision

Giving eyes to computers, that’s what this skill is all about.

Initially, computer vision let machines recognize faces, objects, or vehicles in images or videos, but the vision of these machines continues to improve over time. Among the most promising applications of computer vision, we have:

• Analysis and classification of medical images that will allow specialists to identify tumors or other anomalies quicker.
• Automation in quality control.
• Facial and emotional recognition.
• Augmented reality to overlay digital information in the real world.
• Surveillance and security systems with object detection.
• Driving and assistance on the road as we have seen in Tesla models.

Basically, computer vision enhances the capabilities of AI systems by enabling them to comprehend and interpret visual data.

This skill opens up a vast array of possibilities in various fields, including perception, data analysis, human-computer interaction, automation, safety, and immersive experiences.

Through the fusion of computer vision with other AI methods, such as machine learning and natural language processing, AI systems can attain a more profound understanding of the environment and engage with it in a more human-like manner.

AI Ethics Governance

This is where we come to a skill, which I don’t know if I would call a soft skill, as it doesn’t deal with data and codes, but even so, it’s one on which the good balance between society and AI depends.

Are these professionals perhaps the ones who will prevent the future of AI from looking like Terminator or from living in a technological utopia? Maybe, but let’s look deeper at why engineers should develop an affinity for AI ethics.

You see, to a certain extent, it has all been fun and creativity when seeing the multiple applications that AI has.

We are already tired of seeing images of kittens amplified to the highest degree of cuteness, we have been impressed by seeing Homer Simpson sing Queen songs and how AI has taken images of politicians or celebrities that look too real, doing “not so good things”. This is where I want to stop and ask what the limit is and who will define it.

The need for AI specialists who can review projects to pinpoint potential concerns or identify risks and make sure the product complies with the standards in place is imperative at this point in the genesis of AI, since its uses are reaching limits that are uncomfortable in too much of our human vulnerability.

We saw how the strike of screenwriters and actors in Hollywood included as one of its negotiating points the use of AI for scriptwriting since there is a risk of massive loss of jobs due to the abysmal production of scripts made from prompts.

Going a little further, ethics in AI can put limits on the number of decisions that AI can start making for us, such as whether or not you can get a job in AI.

On the eve of this new technological revolution, we need the ability to establish rules of the game so that instead of being our competitors, AI can be an advance that benefits us in the long run.