How to become an AI expert
Your guide to building the skills, strategies and career pathways that define modern AI expertise.
AI is rapidly transforming workplaces across every industry. Companies are restructuring teams, redesigning workflows and reimagining entire business models around AI-driven efficiency. Developing strong AI skills is a powerful way to future-proof your career, unlock new opportunities and position yourself as a leader in an increasingly automated world.
What is an “AI expert”?
Being an AI expert doesn’t just mean working in a traditional AI job. While roles like AI engineer, machine learning specialist or AI consultant are on the rise, expertise can also mean using AI strategically within any team or department.
An AI expert typically:
- applies AI tools to boost productivity and decision-making
- integrates AI systems into workflows and existing tech
- guides colleagues in adopting AI responsibly
- stays current on AI ethics, governance and risks
- connects technical AI capabilities with real business needs.
AI expertise now spans nearly every area of a business—from creative and customer-facing roles to technical, operational and strategic teams. Whether you pursue a dedicated AI career or become the go-to AI lead in your workplace, these skills give you a major edge in today’s fast-growing landscape of AI jobs and careers.
Types of AI careers and jobs involving AI
AI careers increasingly cover a broad mix of skills. On the technical side are roles that build, train, and optimise AI systems. On the non-technical side are roles that set the strategy for how AI is used, integrate AI into existing processes, guide responsible adoption and manage the organisational change that comes with AI transformation. Both sides play a crucial role in making AI work at scale.
Here are just a few examples—new AI roles are emerging quickly and many more will continue to appear as organisations expand their use of AI.
Technical roles
- Machine learning engineer: builds and trains the models that power AI systems.
- Data scientist: analyses data and creates predictive, AI-driven insights.
- AI product engineer: integrates AI models into products and develops AI-powered features.
Non-technical roles
- AI product manager: leads AI product strategy, user needs and implementation.
- AI consultant: identifies where AI can add value, recommends tools and designs AI pilots.
- AI strategist / governance & ethics specialist: shapes responsible AI use, policy and long-term AI direction.
Step-by-step: How to become an AI expert
Developing AI expertise can start with flexible, short learning pathways. You can build foundational skills, apply them in real-world contexts and progress into technical or strategic areas—choosing where to begin based on your experience and capacity.
A realistic roadmap to becoming an AI expert
- Build AI literacy
Develop a strong understanding of AI, machine learning and generative AI, and how these technologies apply to everyday tasks and workplace challenges. - Learn core tools and apply them to your work
Begin using AI for writing, research, planning, analysis and simple automation. Hands-on practice is one of the fastest ways to build confidence and capability. - Deepen technical or strategic expertise
You can then specialise in the direction that suits your ambitions, whether that’s a technical pathway involving programming, machine learning and neural network development, or a strategic leadership pathway focused on AI strategy, governance, ethics and organisational transformation. Build your AI capability with UNSW AI short courses. - Build a portfolio of AI projects
Document examples such as automated workflows, AI-enhanced processes, small pilots or technical prototypes. - Move into specialised AI or consulting roles
With strong foundations, practical experience and targeted training, you can transition into roles that build AI systems, implement AI across organisations or advise others as an AI consultant.
Core skills you need to become an AI expert
Foundational skills for any AI expert
- Conceptual understanding of AI and machine learning
- Data literacy (reading, interpreting and questioning data)
- Prompt design and evaluation for generative AI
- Critical thinking and problem-solving
- Understanding AI use cases and limitations
- Basic familiarity with automation and workflow tools
Business, leadership and governance skills
- AI strategy and roadmap development
- Change management and organisational transformation
- Risk, ethics and responsible AI
- Decision-making in AI-enabled environments
- Vendor and tool assessment
- Communication and stakeholder alignment
Technical AI skills (for specialist careers)
- Programming (e.g., Python) and basic statistics
- Machine learning fundamentals
- Neural networks, supervised/unsupervised learning, reinforcement learning
- Data engineering basics (data pipelines, preprocessing)
- Model evaluation and optimisation
- Understanding algorithms and computational thinking
Choosing the right AI course
UNSW offers a comprehensive suite of AI short courses designed not only for individuals but also for organisations seeking to upskill teams, build AI capability and prepare their workforce for an AI-enabled future. These programs are practical, accelerated and delivered by industry practitioners—ideal for both professional development and enterprise-wide training.
Beyond earning credentials, UNSW’s AI short courses deliver a guided, streamlined approach to learning AI that is far more efficient than navigating it alone.
Have questions about UNSW AI short courses?
Find answers to the most common questions about AI training, from course structure to learning outcomes, in our AI Short Courses FAQ page.