The global demand for artificial intelligence (AI) skills is accelerating as industries integrate AI to enhance efficiency and unlock new value. According to Coursera’s Job Skills Report 2025, generative AI (GenAI) is the fastest-growing skill, with a staggering 866% year-over-year enrollment increase among enterprise learners.
“There is a huge cloud skills gap with millions of jobs unfilled. AI, big data, and cloud skills are critical, but the biggest change is coming in the tools we use for our roles. Advancing AI powered tools, and not just generative AI, will make our work more innovative and creative. Staying ahead of the curve by learning and using these tools, whether to boost your current role or help you grow into a new one, is where we all need to be focused.,” the report states.
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Popular courses like Prompt Engineering for ChatGPT and Introduction to Generative AI highlight this trend. GenAI is also projected to become the third-highest priority for corporate training by 2027, reflecting its growing importance for career development.
However, gender disparities persist. Women account for just 28% of GenAI course enrollments and 22% of AI professionals globally. “Encouraging women to pursue AI skills…is crucial to building a diverse talent pool, capable of generating work that’s more inclusive as well as potentially increasing global gross domestic product by 20%,” the report emphasizes.
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Here are top 10 fastest-growing AI skills in 2025
1. Generative AI (GenAI)
Generative AI involves using AI models to create text, images, videos, or other forms of content. Popular tools like ChatGPT and DALL·E have demonstrated its utility in content creation, marketing, and problem-solving. The skill is critical for those looking to innovate or enhance user experiences.
2. Artificial Neural Networks
Artificial neural networks (ANNs) mimic the way human brains process information. They form the foundation of many AI applications, enabling computers to identify patterns, recognise speech, and even predict outcomes. Knowledge of ANNs is essential for building AI systems that require complex decision-making.
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3. Computer Vision
Computer vision focuses on teaching machines to interpret visual data, such as images and videos. It is widely used in areas like facial recognition, medical imaging, and autonomous vehicles. Mastering this skill allows AI practitioners to expand the ways technology interacts with the physical world.
4. PyTorch
PyTorch is a machine learning library that enables developers to build and test AI applications efficiently. It is known for its flexibility and ease of use, making it a preferred choice for researchers and practitioners working on deep learning projects.
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5. Machine Learning
Machine learning (ML) is the science of teaching computers to learn from data without being explicitly programmed. Applications range from personalised recommendations to fraud detection. This foundational skill underpins many AI systems in use today.
6. Applied Machine Learning
Applied machine learning takes ML a step further by focusing on real-world applications. Professionals use this skill to solve specific problems, such as predicting customer behaviour or improving supply chain efficiency. This practical approach bridges the gap between theory and implementation.
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7. Deep Learning
Deep learning, a subset of ML, uses large datasets and complex algorithms to train AI systems. It is instrumental in creating solutions for tasks like natural language processing and image recognition. Professionals skilled in deep learning drive advancements in AI capabilities.
8. Supervised Learning
Supervised learning involves training AI systems with labelled datasets. This method is commonly used in predictive modelling, spam detection, and customer segmentation. It is a straightforward yet powerful approach for building effective AI models.
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9. Reinforcement Learning
Reinforcement learning trains AI through a system of rewards and penalties based on trial and error. It is used in robotics, gaming, and optimising business processes. By mastering this skill, practitioners can create AI systems that adapt and improve over time.
10. Machine Learning Operations (MLOps)
MLOps is the practice of managing and deploying machine learning models in production environments. It ensures that AI systems remain effective, scalable, and reliable. This skill is essential for organisations aiming to integrate AI into their operations seamlessly.
Chisom Michael
Chisom Michael is a data analyst (audience engagement) and writer at BusinessDay, with diverse experience in the media industry. He holds a BSc in Industrial Physics from Imo State University and an MEng in Computer Science and Technology from Liaoning Univerisity of Technology China. He specialises in listicle writing, profiles and leveraging his skills in audience engagement analysis and data-driven insights to create compelling content that resonates with readers.
