What Can You Do With an Artificial Intelligence Degree in 2025?

by Imed Bouchrika, Phd

Co-Founder and Chief Data Scientist

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What are the top jobs you can get with an artificial intelligence degree?

Artificial intelligence graduates enter one of the fastest-growing career fields, with opportunities that span technology, business, healthcare, finance, and more. As organizations increasingly integrate AI into their operations, demand is rising for professionals who can design algorithms, manage data, deploy machine learning systems, and ensure responsible use of emerging technologies.

Machine Learning Engineer

These professionals design, build, and deploy algorithms that allow computers to learn from data and make predictions or decisions without explicit programming. They work with large datasets, develop models, and optimize performance for real-world applications.

Machine learning engineers are highly sought after in industries where predictive analytics and automation can deliver significant value.

Data Scientist

While data scientists can come from various educational backgrounds, an AI degree provides strong expertise in advanced modeling, neural networks, and natural language processing. Data scientists interpret complex datasets, build analytical models, and communicate insights that influence strategic decision-making in business, science, and government.

AI Research Scientist

AI research scientists on pushing the boundaries of artificial intelligence through cutting-edge research. These professionals work in universities, government labs, or corporate R&D departments. They develop new AI architectures, exploring novel algorithms, and publishing their findings. This role is ideal for those who are interested in theoretical advancements and innovation rather than only application development.

Other exciting career paths include Computer Vision Engineer (specializing in image and video processing), Natural Language Processing (NLP) Engineer (building systems like chatbots, translators, and voice assistants), and Robotics Engineer (designing intelligent autonomous systems).

Many AI graduates also move into AI Product Management, where they oversee the development of AI-driven products from concept to launch and bridge the gap between technical teams and business strategy.

Each of these roles comes with high salaries, career growth, and the chance to work on technology shaping the future.

What skills does an AI degree help you develop?

An artificial intelligence degree equips students with a combination of technical, analytical, and problem-solving skills. The curriculum blends computer science, mathematics, and domain-specific knowledge to prepare graduates for both research and practical applications.

Beyond coding, it also fosters critical thinking, creativity, and the ability to design intelligent systems that address real-world challenges.

• Programming and Software Development – Students of the shortest online user experience programs gain proficiency in languages such as Python, Java, and C++. They also learn AI frameworks like TensorFlow and PyTorch.
• Machine Learning and Deep Learning – The degree helps understand algorithms for supervised, unsupervised, and reinforcement learning, along with neural network architectures. Students apply these techniques to tasks like image recognition, language processing, and predictive analytics.
• Mathematics and Statistics – Graduates build strong foundations in linear algebra, calculus, probability, and statistics. They use these tools to design and evaluate AI models effectively.
• Data Analysis and Management – Students learn how to preprocess, clean, and manage large datasets to extract meaningful insights. They develop skills in database systems, data visualization, and feature engineering.
• Problem-Solving and Critical Thinking – AI programs aim to strengthen the ability to break down complex problems into manageable components. They teach logical reasoning to design innovative AI solutions.

What industries actively hire graduates with AI degrees?

Artificial intelligence has become a cornerstone of innovation across numerous industries. Companies are using AI to automate processes, improve decision-making, and deliver personalized user experiences. As a result, AI professionals have opportunities in sectors ranging from:

• Technology and Software Development – Tech companies utilize AI for a wide range of applications, from search algorithms to virtual assistants. AI graduates help design smarter software, optimize cloud services, and develop innovative consumer applications.
• Healthcare and Biotechnology – AI supports medical imaging, drug discovery, patient monitoring, and predictive diagnostics. Graduates work alongside clinicians and researchers to improve healthcare outcomes through data-driven insights.
• Finance and Banking – Financial institutions apply AI for fraud detection, algorithmic trading, credit scoring, and customer service automation. AI experts are qualified for competitive wages, such as a high CFO salary, in exchange for creating systems that increase accuracy, efficiency, and security in financial operations.
• Manufacturing and Robotics – AI drives automation, predictive maintenance, and quality control in production environments. Professionals in this sector help design intelligent machines and optimize industrial processes.
• Transportation and Automotive – The rise of autonomous vehicles, route optimization, and predictive maintenance relies heavily on AI. Graduates contribute to innovations in self-driving technology, logistics, and traffic management systems.

For those interested in leadership roles at the intersection of technology and business, pursuing advanced credentials can also be rewarding. For example, understanding the MBA in engineering management salary potential highlights how combining technical expertise with executive training can prepare AI graduates to move into higher-paying leadership positions in engineering-driven industries.

The American public sees AI benefits as follows:

How do AI professionals transition from technical roles to AI leadership?

The transition from a technical AI role to AI leadership is a combination of credibility, communication, and vision. Those who succeed can reconcile engineering and executive decision-making, guiding AI adoption in ways that are both innovative and responsible.

Developing and deploying AI models

Professionals usually spend years developing and deploying AI models, understanding data pipelines, and solving real-world technical challenges. This deep technical credibility is essential because leaders in AI must be able to evaluate projects, guide technical teams, and make informed decisions about feasibility and priorities.

Expanding into specific leadership areas

The next step involves expanding beyond purely technical skills into project management, communication, and business strategy. Aspiring leaders start taking ownership of larger initiatives, mentoring junior team members, and collaborating with cross-functional departments. This exposure helps them understand how AI fits into the organization’s broader goals, from product development to operational efficiency.

For those exploring broader career flexibility, it’s similar to asking what can you do with an applied sciences degree—the answer often reveals just how many industries value the ability to combine technical knowledge with leadership and problem-solving skills.

Mastering strategic vision and ethics

To fully step into leadership, AI professionals must also master strategic vision and ethical considerations. Leaders are responsible for aligning AI initiatives with company objectives, ensuring projects deliver measurable value, and addressing issues like bias, transparency, and compliance. Many also pursue advanced business education, such as an MBA or executive leadership program, to strengthen their competencies

What advanced degrees can you pursue after an AI bachelor's?

After earning a bachelor’s in artificial intelligence degree, many graduates choose to deepen their expertise through a master’s degree in AI or machine learning. These programs focus on deep neural networks, reinforcement learning, computer vision, and natural language processing. A master’s degree can open doors to higher-level technical roles and is often preferred by employers for research-heavy positions. In fact, exploring the wide range of jobs after masters in artificial intelligence—from machine learning engineer to AI product manager—can help graduates decide which specialization best aligns with their career goals.

Another popular path is pursuing a Master’s in Data Science, which emphasizes statistical analysis, big data technologies, and applied machine learning. This option is ideal for AI graduates who want to broaden their skill set and work in areas where AI intersects with data analytics, business intelligence, and predictive modeling.

For those aiming to lead large-scale research projects or develop groundbreaking AI technologies, a PhD in Artificial Intelligence, Computer Science, or Robotics is a reasonable choice. Doctoral programs involve years of independent research, contributions to academic publications, and deep specialization in a niche AI domain. A PhD is especially valuable for careers in academia, corporate R&D labs, and high-level innovation roles.

Some AI graduates also venture into fast-track online MBA entrepreneurship programs with a focus on technology management or innovation. This path is well-suited for professionals who want to combine technical expertise with strategic business acumen. An MBA can be a powerful stepping stone to executive roles overseeing AI-driven business initiatives.

Which universities offer cutting-edge AI graduate programs?

Here’s a refined overview of universities globally renowned for their cutting-edge AI graduate programs:

Massachusetts Institute of Technology (MIT)

MIT is consistently recognized as a global leader in AI research and education. Its Computer Science and Artificial Intelligence Laboratory (CSAIL) serves as a powerhouse for innovation in robotics, machine learning, computational biology, and more. Meanwhile, its Schwarzman College of Computing amplifies AI integration across disciplines.

Stanford University

Stanford’s Artificial Intelligence Laboratory (SAIL), established in 1962, continues to be an epicenter of AI innovation. The university promotes cutting-edge research in NLP, deep learning, and robotics. It's augmented by collaborations with Silicon Valley’s leading tech firms.

Carnegie Mellon University (CMU)

CMU’s School of Computer Science is a longstanding authority in AI and robotics. It offers a spectrum of graduate programs, including AI, machine learning, and AI engineering. It has strong industry ties and a foundational role in autonomous systems development.

University of California, Berkeley (UC Berkeley)

Home to the Berkeley Artificial Intelligence Research (BAIR) Lab, UC Berkeley leads the way in reinforcement learning, computer vision, and human-compatible AI. The integration of AI efforts across technical and societal domains solidifies its reputation for both research excellence and ethical leadership.

What is the average salary for AI graduates?

Entry-level professionals with an artificial intelligence degree credential start in the range of $85,000 to $110,000 annually, while those with a master’s degree can expect $100,000 to $130,000.

Graduates entering the high-demand machine learning engineer, data scientist, AI software developer, or video game designer career path may see salaries even higher, especially in tech hubs like San Francisco, Seattle, and New York.

With experience, AI professionals tend to see rapid salary growth. Mid-level professionals earn between $130,000 and $160,000. Senior experts, AI architects, or research leads can make $170,000 to $200,000+. In addition to base pay, many receive bonuses, stock options, or profit-sharing, especially when working for leading technology companies or AI-focused startups.

What is the job market demand for AI graduates?

The job market demand for artificial intelligence degree graduates is exceptionally robust and continues to grow as artificial intelligence becomes an integral part of business operations, scientific research, and consumer products.

Organizations across industries are integrating AI into everything from predictive analytics and automation to natural language processing and robotics, creating a constant need for skilled professionals. This demand is not limited to technology companies—fields like healthcare, finance, automotive, manufacturing, and even agriculture are actively recruiting AI talent to maintain competitiveness.

Industry reports consistently project double-digit annual growth in AI-related job openings over the next decade. Roles such as machine learning engineer, AI researcher, and data scientist are among the fastest-growing occupations, with demand often exceeding the available talent pool. For instance, the BLS sees a 36% growth for data scientists from 2023 to 2033. 

Interestingly, the rise of AI is also influencing related fields. For example, if you’ve ever wondered what can you do with a master's degree in sports analytics, the connection to AI becomes clear—sports organizations increasingly use machine learning to analyze player performance, predict outcomes, and improve fan engagement. This crossover highlights how AI is shaping both traditional and emerging industries in unexpected ways.

Data scientist is the 2nd most popular job posting in the U.S.

What job search strategies work for AI degree holders?

Graduates with an AI degree can position themselves for success by demonstrating technical expertise and a strategic approach to job hunting. The key is to:

• Build a Strong Portfolio – Showcase projects, research, and contributions to open-source AI tools on platforms like GitHub or Kaggle. A portfolio shows practical skills beyond academic credentials.
• Leverage Professional Networks – Attend AI conferences, meetups, and webinars to connect with industry professionals. Networking leads to job referrals and insider information about upcoming roles.
• Tailor Applications to Each Role – Customize your resume and cover letter to highlight relevant AI skills and experience for specific job postings. Emphasize measurable achievements and the impact of your work.
• Stay Current with Industry Trends – Follow AI research publications, online courses like game design schools online, and thought leaders. Employers value candidates who keep their skills and knowledge up to date.
• Use Specialized Job Boards – Browse platforms dedicated to AI and tech roles, such as AIJobs.com, Machine Learning Jobs, and LinkedIn’s AI-focused listings. These resources often feature opportunities not found on general job sites.

What are the best practices for AI graduates entering a changing workforce?

For AI graduates entering a rapidly changing workforce, embracing lifelong learning is essential. The pace of innovation in artificial intelligence means new tools, frameworks, and methodologies emerge regularly. To thrive, graduates should focus on:

• Continuous learning: Staying updated ensures skills remain relevant and competitive, while also signaling adaptability to employers. If you’re interested in upskilling, consider resources like the accelerated data analytics graduate certificate online.
• Cross-disciplinary expertise: AI is applied across industries such as healthcare, finance, manufacturing, and creative fields. Understanding domain-specific challenges, regulations, and workflows helps AI professionals design more practical, impactful solutions.
• Soft skills development: Communication, teamwork, and problem-solving are vital for working with non-technical stakeholders, translating complex AI concepts, and ensuring alignment with organizational goals. Employers increasingly value professionals who can combine technical leadership with business strategy.
• Ethical and responsible AI mindset: With growing scrutiny on bias, privacy, and transparency, graduates should prioritize fairness and accountability in system design. This builds trust and positions professionals as thoughtful contributors to the future of AI.

Here’s What Graduates Have to Say About Their Artificial Intelligence Careers

Other Things You Should Know About What You Can Do with an Artificial Intelligence Degree

What’s the difference between an AI degree and a computer science degree?

An AI degree focuses specifically on the theories, tools, and applications of artificial intelligence. It covers areas like machine learning, natural language processing, computer vision, and robotics in depth. While a computer science degree provides a broad foundation in programming, algorithms, systems, and software development, it usually includes only a few AI-related courses as part of a wider curriculum.

In short, computer science offers versatility across all areas of computing, whereas an AI degree provides specialized expertise for careers directly centered on intelligent systems and automation.

Is artificial intelligence a good degree?

Yes, artificial intelligence is a strong degree choice for students interested in working at the forefront of technology and innovation. It offers specialized skills that are in high demand across industries, leading to prominent salaries and diverse career opportunities. With AI continuing to shape everything from healthcare to transportation, graduates enter a field that is both future-focused and full of growth potential.

Is AI a difficult degree?

AI can be a challenging degree because it combines advanced topics in mathematics, programming, and data science with complex problem-solving. Students need to be comfortable with subjects like linear algebra, probability, algorithms, and neural networks, as well as staying current with rapidly evolving technologies.

However, for those who enjoy analytical thinking and innovation, the challenge is often part of the appeal and leads to highly rewarding career opportunities.

Is math hard in AI?

Math in AI can be challenging, especially because it involves topics like linear algebra, calculus, probability, and statistics applied to real-world problems. These concepts form the backbone of algorithms and machine learning models, so a solid understanding is a must.

While it can feel complex at first, consistent practice and applying math to practical AI projects often make it more intuitive and rewarding.