8 Important AI Skills to Build in 2025

Artificial intelligence (AI) is transforming our world, but for many of us it still feels out of reach if not downright intimidating. Some want to avoid it, fearing it will take jobs, while others are a little too eager to use it without fully understanding how it works.

The truth is, neither path leads to growth.

Whichever side of the great debate you're on, AI is an essential part of our future. But, what if AI isn’t here to replace us, but to help us thrive?

A recent New York Times article supports this optimistic view: "2024's Nobel Prize in physics was awarded to two scientists who helped computers ‘learn’ closer to the way the human brain does. A day later, the Nobel Prize in Chemistry went to three researchers for using A.I. to solve a problem that stumped biologists for decades, yet could be solved by A.I. in minutes."

That's remarkable. The truth is, AI is a powerful tool that can amplify our strengths, streamline our work, and even enhance our quality of life. And while automation will undoubtedly replace some jobs, it will also create new opportunities in fields like STEM, creativity, and business.

A PwC report suggests AI could add $15.7 trillion to the global economy in 2030 and give many countries a bump to their GDP in the neighborhood of 26%. The reason is two-fold: increased productivity and higher consumer demand. 

In Skillsoft's IT Skills and Salary Report, we found that AI remains the top investment priority for IT decision-makers. However, 65% say there is a significant skills gap within their teams. 

What this means is that AI skills are in demand and make those who have them highly coveted by organizations of all kinds. 

But what skills are we talking about specifically? Let's get into it. 

AI skills encompass a blend of technical expertise, critical thinking, and ethical understanding. These skills allow us to work effectively with AI tools, develop AI systems, and address both the challenges and opportunities AI presents.

Let's explore some important AI skills:

Programming Skills

Programming is the backbone of AI development. Python is particularly popular due to its simplicity and the vast array of libraries available for AI and machine learning, such as NumPy, pandas, and TensorFlow. 

For example, developers use TensorFlow to build and train deep learning models for tasks like image recognition or natural language processing. R is also used for statistical analysis and data visualization, making it ideal for analyzing trends in datasets. Java is often used in large-scale systems, such as integrating AI into enterprise-level applications or building AI-powered chatbots.

See what each language entails by exploring Codecademy's AI Course Catalog.

Mathematics and Statistics

Linear algebra and calculus are essential for understanding how algorithms work, especially in neural networks. 

For example, training AI models involves a lot of math. Calculus helps adjust the model’s settings (called weights and biases) to make better predictions. Probability and statistics are used to understand patterns in data and to measure how well the model is working. This includes checking things like accuracy or how balanced the predictions are.

Machine Learning

Machine learning involves understanding different types of learning (supervised, unsupervised, reinforcement learning) and being able to implement algorithms like decision trees, neural networks, and support vector machines. 

For example, supervised learning is often applied to train models to classify emails as spam or not spam. Familiarity with machine learning frameworks, like Scikit-learn or PyTorch, helps efficiently build and deploy models for applications such as forecasting sales or detecting fraud.

Data Analysis and Visualization

Skills in data cleaning, processing, and visualization are important for preparing data for AI models and interpreting the results. 

Cleaning raw data by removing duplicates or filling in missing values is critical before training an AI model. Tools like Matplotlib, Seaborn, and Tableau are commonly used for creating insightful visualizations, such as heatmaps that highlight correlations in data or dashboard reports that summarize AI predictions to stakeholders.

Problem-Solving

AI often involves tackling complex, open-ended problems. Strong analytical skills and creativity are needed to design algorithms that can effectively address these challenges and improve over time. 

For example, solving a problem like optimizing delivery routes for a logistics company requires building an AI model that takes into account traffic, weather, and delivery time windows, offering a solution far better than traditional methods.

Read Next:   10 Transferable Skills that Can Help Anyone Adapt to Any Career

Domain Knowledge

Understanding the specific context in which AI is applied (e.g., healthcare, finance, automotive) allows for the development of more tailored and effective AI solutions. 

For example, in healthcare, domain knowledge helps AI developers create predictive models for diagnosing diseases using patient data or medical imaging. This knowledge ensures the AI is not only technically sound but also practically relevant.

Ethics and Bias Awareness

AI systems can inadvertently perpetuate biases present in training data. Being aware of these issues and implementing strategies to mitigate bias is crucial for developing fair and ethical AI applications. 

For example, when building a hiring algorithm, developers must ensure that the training data does not disproportionately favor certain demographics, implementing techniques like balanced datasets or fairness-aware algorithms to address potential biases.

Communication Skills

AI professionals often need to work with cross-functional teams and explain technical concepts to stakeholders who may not have a technical background. 

For instance, an AI developer might need to present the results of a predictive sales model to a marketing team, breaking down how the model works and how it can drive actionable insights. Clear communication ensures that AI solutions are understood and effectively integrated into business processes.

Developing skills to work with AI involves a combination of technical education, hands-on experience, and staying updated on industry trends. 

Here are a few approaches to build and enhance these skills:

Online Courses and Certifications

Several providers offer a breadth of AI training to help organizations upskill their workforces quickly. Taking on-demand courses can make learning in the balance of work and life more manageable. 

Certifications from reputable organizations, such as Google’s TensorFlow Developer Certificate or Microsoft’s AI Engineer Associate, can help validate expertise and improve employability.

Hands-On Projects

Practical experience is key to mastering AI concepts. Working on real-world projects — such as building a chatbot or training a neural network — can deepen understanding of AI frameworks and methodologies. Open-source datasets and platforms like Kaggle provide excellent opportunities for experimentation.

This one-hour course from Codecademy teaches learners to build their own GPT. 

Participating in Hackathons and AI Communities

Joining hackathons or AI-focused meetups allows for collaborative learning and problem-solving. Engaging with communities on platforms like GitHub, Reddit, or specialized AI forums fosters networking and helps uncover new development techniques.

Staying Updated with Research

AI is a rapidly evolving field, so staying informed about the latest advancements is essential. Reading research papers, following AI blogs, and subscribing to newsletters from prominent organizations can help you keep pace with new tools, technologies, and ethical guidelines.

According to a recent Deloitte report, 94% of executives believe investment in AI will be critical to business success over the next five years. But our own C-Suite perspective report found that 57% of tech leaders say their team’s current skills in AI are low. Recruitment of new talent is expensive and time-consuming. Training and upskilling existing employees can help bridge the skills gap, giving your organization a competitive advantage.

Skillsoft’s AI-powered learning platform helps organizations identify and measure skill proficiencies to ensure their workforce stays relevant. The platform makes skilling personalized and accessible, offering a blend of self-paced courses, hands-on practice, virtual live online classes, and coaching to close skill gaps. 

When we embrace learning AI skills, we’re fueling the human curiosity and drive that have always propelled us forward. AI isn’t here to replace us; it’s here to empower us. By learning essential AI skills, we can position ourselves and our businesses for a future of innovation and growth.