Career·7 min read·

Building a Career in AI: What You Actually Need

Everyone wants to work in AI. But what skills, qualifications and experience do you actually need to break into the field?

The demand for AI talent has never been higher. Job postings requiring AI skills have grown by over 300% in the last three years, and the gap between supply and demand shows no sign of closing. If you are thinking about building a career in AI, the timing has never been better — but the path is less straightforward than many career guides suggest.

The first thing to understand is that 'AI' is not a single career. The field encompasses data engineering, machine learning engineering, AI research, AI product management, AI ethics and governance, and AI implementation consulting, among others. Each of these roles requires a different combination of skills, and the entry requirements vary significantly.

For technical roles — machine learning engineer, data scientist, AI researcher — a strong foundation in mathematics (particularly linear algebra, calculus and statistics) and programming (Python is the dominant language) is essential. Most practitioners in these roles have at least a bachelor's degree in a quantitative field, and many have postgraduate qualifications.

But the fastest-growing segment of AI careers is not the technical roles — it is the roles that sit at the intersection of AI and business. AI product managers, AI implementation consultants and AI governance professionals do not need to be able to build models from scratch. They need to understand what AI can and cannot do, how to identify use cases, how to manage AI projects and how to think about the ethical implications of AI systems.

For professionals looking to transition into AI from other fields, the most effective path is usually to develop AI literacy in your current domain first. A finance professional who understands how AI is being applied in financial services, a supply chain manager who understands AI-driven demand forecasting, a marketer who understands AI-powered personalisation — these professionals are extremely valuable because they combine domain expertise with AI knowledge.

The certification landscape for AI is maturing rapidly. While a degree from a top university remains valuable for research roles, there are now excellent certification programmes that can give you the practical skills and credentials you need to move into AI-adjacent roles. The key is to choose programmes that emphasise real-world application over theoretical knowledge, and that are taught by practitioners who are actively working in the field.