Artificial intelligence skills and why they matter for your career
Artificial intelligence (AI) is everywhere – from the apps on your phone to the way businesses make decisions. But behind every AI-powered tool or service are people with the right AI skills making it all happen.
If you're curious about AI and wondering whether it’s worth developing these skills, the answer is simple: absolutely. Let’s explore what AI skills are, why they matter, and how they can open doors to exciting new opportunities.
Why learning AI skills is a smart move (even if you don’t work in tech)
AI is no longer just for tech professionals. As artificial intelligence becomes embedded in everyday business processes, professionals in various sectors – including finance, law, healthcare, and marketing – are finding that AI skills are becoming essential.
AI is now part of everyday business, and it's changing the way people work in finance, law, healthcare, marketing, and more. According to PwC's 2024 AI Jobs Barometer:
84% of CEOs believe AI will make their employees more efficient.
69% of global CEOs think AI will mean most workers need to learn new skills.
So, whether you’re thinking about a career in the AI industry or just want to stand out in your current role, learning about AI can help you work smarter and open up new job opportunities.
Interested in developing AI skills no matter your industry?
You’re in the right place.
65% of our courses include a core or elective AI module, covering practical topics to help you stay ahead in a fast-changing world like:
creative prompting techniques
current topics in AI
AI ethics to help you stay ahead in a fast-changing world.
The AI skills gap and in-demand roles
There’s a big gap between the number of people with AI skills and the number of jobs needing them. This is known as the AI skills gap, and it’s growing fast.
According to PwC, job postings requiring AI skills have grown 3.6 times faster than all job postings in the UK since 2012. What's more, employers are willing to pay a 14% wage premium for AI skills in the UK, making this a lucrative career path. In the US, this premium rises to 25%, highlighting the global demand for AI professionals (2024 Global AI Jobs Barometer, PWC).
In-demand AI roles
The Future of Jobs Report 2023 from the World Economic Forum predicts that demand for AI and machine learning specialists will grow by 40%, creating 1 million new jobs in the next few years.
But the high demand for AI skills isn’t limited to tech roles. Generative AI is already transforming tasks across a wide range of professions, meaning those who can understand and apply AI will have a significant advantage in the job market. As AI continues to automate and augment tasks, even higher-wage jobs are being reshaped.
In-demand skills in AI
In the UK, 81% of IT managers say there’s an urgent need for more people with AI-related skills, like data science, generative AI, and large language models (LLMs) – the technology behind tools like ChatGPT (Red Hat, 2024).
By learning AI skills, you’re not just keeping up with the latest trends – you’re making yourself more valuable in a job market that’s hungry for people who can work with AI tools.
What skills are needed for artificial intelligence?
If you're thinking about a career in artificial intelligence (AI), it's not just about learning technical skills. Sure, you need to know how to work with data and build AI models, but to really stand out, you’ll also need to develop soft skills and gain knowledge of how AI affects the world around us.
Here’s a breakdown of the essential skills that AI professionals need – and why they matter.
Technical skills required for artificial intelligence
Let’s start by looking at the technical skills needed for AI. These must-have skills will help you work with and create AI systems, and keep up with the fast-moving world of AI technology.
Machine learning
This is about teaching computers to recognise patterns in data and make decisions without being told exactly what to do. It’s the foundation of most AI systems and involves developing machine learning algorithms that improve over time as they process more data.
Deep learning
Deep learning is an evolved type of machine learning that uses something called neural networks. Neural networks are a way for machines to process information in a way that’s loosely inspired by how human intelligence works. Deep learning powers things like facial recognition and language translation.
Natural language processing (NLP)
Ever used a chatbot or voice assistant? That’s NLP in action. Essentially, NLP is about teaching machines to understand and respond to human language.
Data analysis and data manipulation
AI relies on huge amounts of raw data to work. You need to know how to process that data, spot patterns, and use it to train AI models.
Predictive modelling
AI relies on huge amounts of raw data to work. Predictive modelling uses that data to forecast future outcomes, helping businesses make smarter decisions, whether it’s predicting customer behaviour or spotting potential risks.
AI deployment
Once an AI model is built and trained, it needs to be put to use in real-world applications. AI deployment happens when you integrate AI solutions into existing systems, making sure they perform well in real-world scenarios, and adapting them as needed over time.
Computer vision
Computer vision allows machines to interpret visual data. It’s used in facial recognition, autonomous vehicles, and image processing systems.
Programming skills for artificial intelligence
Programming is at the heart of everything in AI. If you want to build AI models, create machine learning algorithms, or work on real-world AI projects, you’ll need strong coding skills. Having strong programming skills gives you the ability to bring AI ideas to life – from simple machine learning models to more advanced tools like generative AI or deep learning systems.
But it’s not just about learning one language. What really matters is knowing how to choose the right tool for the job and being able to adapt as new technologies emerge.
Here are some of the most important programming languages for AI:
Python
Python stands out as the #1 language for AI – and for good reason. It’s easy to learn, works well with large datasets, and has powerful libraries like TensorFlow and Scikit-learn that make building AI models easier.
R
R is widely used in data science and statistical analysis, which are key parts of AI. It’s great for working with raw data and building predictive models.
SQL
AI professionals often need to extract and manipulate data from databases. SQL is essential for pulling the right data to train and improve AI models.
C++
C++ is known for its speed and performance, making it a good choice for building AI tools that need to process information quickly, like computer vision applications or robotics.
Java
Java is a good option for building large-scale AI systems, especially for businesses that need their solutions to be secure and scalable.
Soft skills: the human side of AI expertise
AI isn’t just about machines – it’s about people, too. That’s where soft skills come in.
To succeed in AI jobs, you need to think critically and solve complex problems creatively. The non-technical skills below will help you collaborate with others and explain your ideas clearly.
Critical thinking skills
AI professionals need to evaluate different approaches, question results, and make data-driven decisions.
Problem-solving skills
AI is all about tackling complex problems. Whether it’s improving customer service with chatbots or using AI to spot medical conditions early, you’ll need to think on your feet to find solutions.
Communication skills
Not everyone you work with will be an AI expert. You’ll need to explain AI solutions to business stakeholders – like managers or clients – in a way that makes sense to them.
Teamwork
Many AI projects involve collaboration between AI engineers, data scientists, and other professionals.
Ethical AI practices
AI is powerful – and with that power comes responsibility.
AI professionals need to understand ethical AI practices to make sure the tools they create are fair, transparent, and accountable.
Why does this matter? Imagine an AI system that helps companies decide who gets approved for a loan. If that system isn’t carefully designed, it could unintentionally favour certain groups of people and exclude others. That’s why ethical AI is a growing focus in the industry.
Understanding AI ethics means considering the impact of your work on people and society. It’s about making sure AI solutions don’t cause harm and that they’re used in a way that’s responsible and trustworthy.
Domain knowledge
Knowing how to build AI systems is one thing – but to make those systems work in a specific industry, you’ll need domain knowledge.
This means having a deep understanding of the industry you’re working in and the unique challenges it faces.
In healthcare, for example, AI professionals need to know about medical data and how to handle sensitive patient information.
In finance, AI engineers need to understand financial regulations and how to use AI to detect fraud or manage risk.
By combining domain knowledge with AI expertise, you’ll be able to create AI solutions that meet the needs of a particular sector and make a real impact.
What roles use artificial intelligence skills?
AI is reshaping industries, and there are many roles that rely on AI expertise to solve problems and drive innovation. From data analytics to AI development, here are some of the key career paths in the growing AI market.
Data scientist
Data scientists work with data modelling, data processing, and statistical tools to turn raw data into insights. Their work with AI could involve building machine learning models, and using unsupervised learning techniques and reinforcement learning to predict outcomes and solve problems.
Machine learning engineer
Machine learning engineers work on developing sophisticated algorithms and model development to improve AI systems. They tackle technical challenges and build computer algorithms that power tools like recommendation systems and chatbots.
AI systems engineer
AI systems engineers focus on AI deployment and systems engineering to integrate AI into businesses. They ensure that AI systems are scalable, efficient, and ready for use in real-world scenarios.
AI ethicist
AI ethicists ensure AI solutions are responsible, fair, and transparent. They address ethical technical challenges and ensure AI advancements benefit everyone equally, making ethics a crucial part of AI development.
Top roles requiring AI skills
While AI-hiring intensity has increased, only 0.6% of job postings across the UK between 2017 and 2022 were for AI jobs, according to the OECD (Measuring the demand for AI skills in the United Kingdom, 2024).
However, demand is growing beyond traditional tech sectors and is expanding into industries like finance and insurance. Jobs that once relied on human decision-making are now using AI to process data and predict outcomes faster and more accurately.
According to the OECD, the most AI-hiring intense roles include:
Data science: Specialists in extracting meaningful insights from data using AI models.
Computer science: Experts in developing AI systems and machine learning algorithms.
Hardware engineering: Building the infrastructure that supports AI technologies.
Robotics engineering: Designing AI-powered robots for automation and innovation.
Study an AI degree online at Walbrook
With flexible online learning, you can build the AI skills you need to succeed while balancing your studies with work and life. Study at your own pace from anywhere in the world and get career-ready with the latest AI knowledge.
Our BSc (Hons) Artificial Intelligence degree is ideal for anyone starting their journey in AI. You’ll develop programming skills, learn how to build AI models, train neural networks, and work on machine learning projects that prepare you for real-world AI careers.
Already have experience in AI? Our MSc Artificial Intelligence degree is designed for professionals looking to deepen their expertise. Covering advanced topics like deep learning, generative AI, and AI ethics, this course helps you tailor your learning to your career goals and stay ahead in a constantly evolving field.
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