Employers in 2026 are looking for professionals who can use artificial intelligence to solve real problems, assess its outputs and explain its value.
Technical knowledge remains important, but it's most useful when combined with responsible judgement, commercial awareness and clear communication.
The World Economic Forum identifies AIs and big data as the fastest-growing kills towards 2030.
Alongside these, it highlights human capabilities such as analytical and creative thinking, along with the resilience to keep learning as technology changes. This reflects a wider shift in recruitment: employers need people who can work with AI, not simply operate one tool.
Whether you want to move into a specialist AI role or apply AI within your current profession, the following skills can help you prepare for an evolving job market.
AI skills matter because artificial intelligence is changing tasks across technical and non-technical roles. LinkedIn Work Change Report estimates that 70% of the skills used in most jobs will change between 2015 and 2030, with AI acting as a major catalyst.
This doesn’t mean every employee needs to become a machine learning engineer. It means organisations increasingly value people who can identify a useful AI application, work confidently with data, judge whether output is reliable and understand the risks involved.
The most employable candidates can connect AI capability to a business, public service or social need. They can also show how they’ve used AI to improve a process, support a decision or create measurable value.
Employers are looking for a blend of technical, analytical and human skills. The exact balance depends on the role, but these eight capabilities are relevant across many sectors.
AI literacy is the ability to understand what AI can do, where it can add value and where its limitations lie. It includes recognising the difference between technologies such as machine learning, deep learning and generative AI.
An AI-literate professional does not accept every output at face value. They understand that AI systems may produce inaccurate information, reflect bias or perform poorly outside the context in which they were developed. Employers value this awareness because it supports safer and more effective adoption.
Data literacy means being able to interpret, question and communicate information derived from data. AI systems depend on data, so professionals need to understand data quality, relevance, privacy and potential bias.
In practice, this could involve selecting suitable data for a project, identifying gaps in a dataset or deciding whether a model’s results support a business decision. Strong statistical foundations also help professionals distinguish meaningful patterns from misleading correlations.
Specialist AI roles often require programming and machine learning skills. Python is widely used to prepare data, develop models and work with AI libraries. Knowledge of machine learning and deep learning helps professionals understand how models learn, how their performance is measured and how they can be improved.
Employers may also look for practical evidence. A portfolio project that defines a problem, documents the data and evaluates the result can demonstrate more than a list of tools on a CV.
Problem framing is the ability to decide whether AI is the right response to a challenge. Before choosing a model or platform, professionals need to define the desired outcome, constraints, users and measures of success.
For example, a retailer might use AI to improve demand forecasting, while a healthcare organisation might explore its use in analysing medical data. In both cases, technical performance is only one consideration. The solution must also fit the organisation’s processes.
Employers need people who can test AI outputs rather than assume they are correct. Evaluation can include checking accuracy, reliability, fairness, explainability and performance over time.
For generative AI, this may involve verifying claims against trusted sources and checking whether the response meets the intended brief. For a predictive model, it may mean choosing suitable performance measures and examining errors across different groups. Human oversight remains central to both tasks.
Responsible AI involves developing and using AI in ways that are fair, transparent, accountable and safe. Employers need professionals who can consider privacy, discrimination, security, intellectual property and the effects of automated decisions.
This skill is becoming more important as organisations formalise AI governance and respond to regulation. Professionals should be able to document how a system is used, identify who is accountable and raise concerns when an application creates unacceptable risk.
AI projects often bring together technical specialists, managers, subject experts, legal teams and the people who will use the system. Clear communication helps these groups agree on the problem, understand trade-offs and make informed decisions.
Employers value professionals who can explain a model or recommendation without unnecessary jargon. Listening is equally important. An effective AI solution depends on understanding the needs and expertise of people across the organisation.
AI tools, techniques and expectations continue to change. Adaptability means learning new approaches while retaining the underlying principles needed to assess them critically.
The World Economic Forum identifies resilience, flexibility, agility, curiosity and lifelong learning among the skills growing in importance. In an AI career, these qualities help professionals respond to new technology without chasing every trend or relying on one platform.
Employers need both technical and human skills. Technical expertise enables professionals to build on and evaluate AI systems. Human skills support them in choosing worthwhile problems across disciplines considering how a solution affects people.
The balance varies by role. An AI engineer may need advanced programming, machine learning or model deployment skills. A manager using AI to improve a service might require stronger AI literacy, governance and change management skills. Two things both professionals need: critical thinking and the confidence to challenge unreliable outputs.
The strongest evidence of AI capability is a clear example of how you applied your knowledge. A project, portfolio or workplace case study can show both your process and your judgement.
When describing an AI project, explain:
This structure gives an employer more useful evidence than a broad claim such as “experienced with AI”. It also shows that you can connect technical work to organisational goals.
A master’s degree can provide the structured technical knowledge, critical thinking and applied experience needed for an AI-focused career. It can also help professionals move beyond using AI tools to understanding how AI systems are designed, tested and governed.
The University of Hull’s online MSc in Artificial Intelligence combines AI foundations, Python programming, machine learning, deep learning, applied AI and the ethical, regulatory and social aspects of fair AI. Students can complete a research or consultancy project focused on a real-world issue, including a challenge from their current workplace.
The programme is 100% online and studied part-time over two years, allowing working professionals to develop their expertise alongside existing commitments.
The AI skills employers are looking for in 2026 extend beyond technical knowledge. Organisations need people who can apply AI to worthwhile problems, evaluate its performance, manage risk and communicate clearly with others.
Discover how postgraduate study could support your next career step.