16 Artificial Intelligence Skills for General Roles and AI Careers

Key takeaways

Knowing how to work with artificial intelligence, and developing specific skills to use the technology successfully, is increasingly important for all types of roles.

• There are general AI skills you can develop to support your work, but those who work in AI-specific roles will need additional technical skills.

• Potential employers increasingly expect workers to develop AI literacy and contribute effectively to AI projects.

• You can develop your AI skills further through microcredentials and degrees.

Explore in-demand AI skills for both general roles and AI-specific ones. Afterward, if you're ready to start sharpening your AI literacy, consider enrolling to earn the Google AI Professional Certificate.

7 artificial intelligence skills everyone should have

As AI continues to reshape how businesses operate, understanding its basics is becoming an expectation across industries—not just for engineers or data scientists. Even if you don't work in a technical role, a few foundational AI skills can help you use these tools responsibly and effectively.

1. Critical thinking

AI can generate convincing but inaccurate information. Knowing to question outputs, verify facts, and not take results at face value is essential.

• The skill: Developing a mindset that treats every AI claim as a hypothesis that needs to be cross-referenced with trusted sources.

• Why it matters: It protects your professional reputation and prevents the spread of hallucinations or misinformation that could impact decision-making.

2. Data privacy awareness

Many AI tools process and store the information you input. Avoid entering sensitive personal, client, or company data into tools that aren't approved or secured by your organization.

• The skill: Identifying what constitutes sensitive data, and how to best work with it.

• Why it matters: In a professional environment, maintaining data hygiene is often a compliance requirement.

3. Prompt literacy

Getting useful results from AI tools depends on how well you communicate with them. Learning to write clear, specific prompts saves time and improves output quality.

• The skill: Moving beyond simple keywords to provide context, specify personas, and define clear output constraints.

• Why it matters: High-quality prompts reduce the amount of time to get the output you desire.

4. Understanding limitations

AI tools can reflect bias, miss context, or confidently get things wrong. Knowing where these tools fall short helps you decide when to rely on them and when not to.

• The skill: Recognizing model factors, such as knowledge cutoff dates or an inability to perform complex real-time reasoning.

• Why it matters: It allows you to use AI as a collaborator rather than a replacement.

5. Iterative problem-solving

Working with AI is an ongoing dialogue where the first answer is often just a starting point.

• The skill: Developing the patience and strategy to refine the AI toward the desired result based on its previous responses.

• Why it matters: Users who expect a perfect first result often give up too early. However, those who can iterate get significantly higher value out of the same tool.

6. AI ethics

AI is a reflection of the data it was trained on, which often contains historical or societal prejudices.

• The skill: Learning to spot skewed perspectives or exclusionary language in AI-generated content.

• Why it matters: For a general user, this is about filtering the output before it reaches a customer or colleague, ensuring the work remains inclusive and fair.

7. Task delegation

Not every task should be handed over to an AI. Knowing what to automate is just as important as knowing how to do it.

• The skill: Analyzing your own workflow to identify high-repetition, low-risk tasks that are suitable for AI.

• Why it matters: It prevents automating tasks that should have a human element.

9 key skills for an artificial intelligence role

To obtain an AI job and do it effectively, you need certain core technical skills, such as a basic understanding of machine learning, neural networks, and data processing.

1. Understanding machine learning algorithms

Machine learning (ML) algorithms have contributed to many technological AI developments. For example, if a platform like Netflix or Spotify recommends a highly personalized song, movie, or search result, it’s because ML is working behind the scenes. ML is also responsible for facial recognition during a visual search, speech recognition with Alexa and Siri, and fraud detection based on patterns in behavior.

2. Familiarity with neural networks

Because neural networks are a foundational component of AI, you need to develop an in-depth understanding of how they operate. Essentially, artificial neural networks (ANNs) are a tool for teaching computers how to refine and sort through data. ANNs attempt to mimic how the human brain works. Instead of actual neurons, the ANNs utilize artificial neurons, also known as nodes, which are interconnected units that transfer and process information.

Learn more about neural networks with the Deep Learning Specialization:

3. Knowledge of data preprocessing techniques

When working with AI and training an ML model, you will likely deal with large amounts of data. You’ll need to understand how to preprocess that data to prepare it for the ML algorithm to work with it effectively. You need to use high-quality data to train the ML algorithm, which you can achieve by applying data preprocessing strategies such as data reduction, data wrangling, data transformation, feature scaling, and feature selection.

4. Experience with Python, R, and Java

Knowing an AI programming language is important. You’ll want to learn Python because it offers an extensive ecosystem for AI developers and is simpler to use than other languages. R provides tools for statistical analysis and data visualization, which are both important AI applications. These tools can lead to better decision-making for the organization. Finally, Java can help you with big AI systems because it is scalable and portable.

5. Familiarity with AI frameworks

Since AI frameworks are foundational components for producing advanced AI systems capable of learning, adapting, and progressing, understanding frameworks such as TensorFlow, PyTorch, and Keras could prove useful. To simplify the creation and implementation of an AI system, you need to use previously constructed functions and libraries available within an AI framework.

6. Analyzing data sets for model training

Because many industries use ML to solve problems and make decisions, you’ll need to know how to train ML models with large data sets to increase the accuracy of the model. For your ML model to perform successfully, the data you use to train it must be high-quality, appropriate size, relevant, complete, clean, and diverse. 

7. Understanding statistical concepts

To create AI algorithms, you need a solid understanding of mathematics and statistics. Linear algebra and calculus are foundational pieces for neural networks. When you’re interpreting data and constructing predictive models, you will need probability and statistics.

8. Stakeholder communication

When you’re an AI professional, you might need to discuss your work with non-technical people such as stakeholders or clients. To do this effectively, you need the ability to communicate complex ideas in an accessible manner to individuals without your knowledge of AI. 

9. Cross-functional collaboration

Since AI projects need contributions from data scientists, software engineers, and project managers, establishing a collaborative working relationship with all parties ensures a greater chance of success. If you can help foster a collaborative working environment, each of your colleagues will be more likely to offer their best work, making the AI project the best it can be.

How to build your AI skill set

Developing AI proficiency is no longer limited to academic researchers. Depending on your career goals, you can choose between rapid upskilling through microcredentials or deep-tier specialization via traditional degrees.

Professional certificates and certifications

Whether you're seeking an entry-level role or an opportunity to pivot, AI courses, certificates, and certifications offer a way to not only develop your knowledge but validate it. They can provide practical training and a valuable credential to add to your resume.

• Data analytics certifications

• Machine learning certifications

• Network certifications

• Cloud certifications

• Software engineering certifications

Academic degrees

If you're looking to build more foundational knowledge over a longer period of time, a bachelor's degree or a master's degree can provide the theoretical framing necessary for high-level research and engineering.

• Computer science

• Data science

• Information technology (IT)

• Artificial intelligence

Advance your artificial intelligence skills with free resources

• Stay current with in-demand skills, industry trends, and more by subscribing to our LinkedIn newsletter, Career Chat.

• Hear career advice from an established professional by checking out this Q&A: Becoming an AI Engineer: 7 Questions with an IBMer

• Visit our Career Resource Hub to take AI skill assessments and career matching quizzes to help you hone your skills and identify your ideal role

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