MS in AI vs. MS in Applied AI: Which Is Better?

The demand for professionals with artificial intelligence (AI) skills is high, with the U.S. Bureau of Labor Statistics forecasting that many jobs that utilize AI will experience faster-than-average growth. At the same time, it’s estimated that workers with AI skills will command higher wages, up to a 56% wage premium compared to other professionals.

As a consequence of AI skills being in such high demand, there are many different AI career paths available to you. Whether you’re an engineer, computer scientist, or tech enthusiast who’s thinking of a career change, there’s a role you can play with AI. The question is: What kind of AI work do you want to do?

You should start by deciding whether you want to work in general AI development or in applied AI. The difference is that, while general AI work focuses on improving how computer systems simulate human intelligence, applied AI is the practical implementation of those systems to solve real-world problems.

Your answer will determine your course of study, the classes you take, and your future career opportunities. What resources do you need access to to engage in practical AI implementation and build real-world experience? For example, Pace students can take advantage of the Pace Artificial Intelligence lab for hands-on research and training.

Here’s what you need to know about the difference between AI and applied AI studies, and which might be the better master’s degree for your career.

What Is an MS in Artificial Intelligence?

A general AI program is primarily about the science and theory of artificial intelligence. This study and work explore the mathematical and computational foundations that make AI systems work or not work, and why.

A master’s program in AI will focus on the development and advancement of core AI theory, algorithms, and models. It is a highly technical program with advanced math and programming, designed to help you build strong technical skills in modern AI, including:

• Machine learning (ML): The statistical and algorithmic engines that allow systems to learn from data, covering everything from classical regression models to deep neural networks and how AI can affect human decision-making.
• Large language models (LLMs): The architecture, training dynamics, and capabilities behind systems that directly interact with users, such as the ones powering today's AI assistants
• Natural language processing (NLP): How machines parse, interpret, and generate human language, a field that now underpins everything from search engines to customer service automation
• Robotics: How AI agents perceive physical environments, make decisions, and take actions in the real world, blending computer vision, control theory, and planning
• Generative AI: The models and techniques behind AI-created images, text, audio, and video, including the ethical questions those capabilities raise

This line of study would be a good fit for those interested in core theory and research-focused practices and who want to become AI researchers, developers, or engineers.

Benefits of a Degree in AI

A general AI master’s degree gives you the freedom to develop a rigorous grasp of why intelligent systems behave the way they do. You’ll study the mathematics, the logic, and the underlying models while simultaneously designing, implementing, and evaluating those systems yourself. A general AI graduate will have a strong understanding of theory and practice and be just as capable of architecting a system and then reasoning carefully about why it succeeded or failed.

Additionally, general AI programs that offer interdisciplinary areas of study will enable you to build specialized skills. For example, a program that offers coursework that includes neuropsychology, human learning, and cognitive psychology would provide a framework for thinking about how humans reason, form beliefs, and make decisions under uncertainty — all of which can provide important benchmarks against which AI systems can be measured and critiqued.

Duration of Study

In most cases, a full-time master’s degree program will take two years to complete. Some accelerated programs may be completed sooner, in as little as 12–18 months.

AI Specializations

A general AI curriculum tends to focus on the foundations that make AI systems work. Depending on your interests and choice of electives, you might spend significant time on:

• Machine learning theory: Understanding why algorithms learn, not just how to run them, to help them improve over time
• Research methodology: Designing experiments, reading and writing academic papers, and contributing to open problems
• Natural language processing: Optimizing how computers understand, interpret, and generate human language
• Computer vision: Teaching machines to interpret and analyze visual information from the world, used in medical imaging and self-driving cars.
• Ethics and policy: Questions about what intelligence means, and the obligations that come with building it

What Is an MS in Applied Artificial Intelligence?

Applied AI takes existing tools and techniques and focuses on deploying them to solve real problems. The emphasis is on translating AI capability into systems that operate, often to solve day-to-day life problems in different domains, including healthcare, manufacturing, finance, and retail.

A master’s in applied AI will focus on the practical application and integration of AI technologies within an organizational context. These programs supplement their curriculum with project-based learning informed by real-world examples and opportunities to develop actual working solutions.

In a master’s program in applied AI, you might explore:

• Building and fine-tuning AI models for specific domains, such as healthcare, finance, or robotics
• ML engineering projects, including model deployment, monitoring, scaling, and infrastructure
• Designing data pipelines and data engineering to optimize system data quality
• Solutions-oriented projects or partnerships with companies to solve live problems

An applied AI program would be ideal for someone who wants to become a technical expert in AI deployment, strategy, or governance.

Benefits of a Degree in Applied AI

Graduating with a degree in applied AI should give you the capability to walk into a professional environment and immediately begin designing, implementing, and managing AI systems that produce measurable results. In general, you’ll:

• Understand how to train, evaluate, and deploy cutting-edge tools to integrate LLMs and ML into real workflows
• Be proficient in model deployment, data engineering, cloud infrastructure, API integration, and system monitoring
• Be capable of building production-ready solutions such as chatbots, forecasting pipelines, and decision dashboards across sectors like healthcare, finance, cybersecurity, and media.

Having learned in a project-based environment, you’ll have the professional judgment to confidently make decisions under real constraints. You’ll have experience in learning to scope a problem, choosing appropriate tools, managing tradeoffs, and iterating when the first approach doesn't work. Upon graduation, you’ll also have built a portfolio of tangible work that clearly demonstrates your competence.

Duration of Study

Just like a general AI master’s degree program, a full-time master’s in applied AI will typically take two years to complete. Some accelerated programs may be completed sooner, in as little as 12–18 months.

Specializations

Some concentrations in applied AI will focus on designing AI systems that work well for people, while others will specialize in managing data or building advanced intelligent technologies. While there is a wide array of specializations, you can think of them as falling into one of these categories:

• Human-Centric AI focuses on how people interact with AI systems. Students may study user experience (UX) and design, cognitive science and technology, or human–computer interaction to create AI tools that are intuitive, ethical, accessible, and responsive to human needs.
• Data-Centric AI emphasizes the data that powers intelligent systems. Students learn big data and data engineering skills to collect, organize, manage, and process large datasets. They also develop the ability to mine, interpret, and analyze data to support strategic decision-making and help organizations gain a competitive edge.
• Computational Intelligence centers on the technical foundations of AI. Students explore machine learning, NLP, and computer vision to build systems that can recognize patterns, understand language, generate insights, and automate complex tasks.

Differences Between an MS-AI and an MS-AAI

Coursework

MS in AI

Core courses establish rigorous mathematical and computational foundations and then use those foundations to explore the full range of AI as an intellectual discipline. Students don't just learn to use existing tools; they learn to reason about why those tools work, where they break down, and how they might be improved.

In the study of intelligence, the MS in AI may draw on electives from cognitive science, neuroscience, philosophy of mind, linguistics, and psychology.

MS in Applied AI

Courses are more hands-on and project-driven from the start, and the progression of the program is designed to move students from foundational skills to production-ready competence as directly as possible. Students will likely spend substantial time on the practical infrastructure of AI: data pipelines, model deployment, cloud platforms, system monitoring, and performance evaluation.

In an applied AI program, the capstone or practicum experience is frequently the centerpiece of the degree, designed to simulate the full lifecycle of an AI project from problem to solution.

Prior Experience (Career & Academic)

For the most part, neither an MS in AI nor an MS in Applied AI will require a computer science degree for admission. For students who are entering from non-technical fields — or who studied computer science years ago and feel their skills have grown rusty — many programs offer bridge courses designed to close the knowledge gap.

Prospective Careers

MS in AI

An education in general AI will prepare graduates for environments where advancing the state of the art is the primary mission. Research labs are a common destination, as are universities and institutions where AI is being studied and applied. Graduates can also find a place at companies building advanced AI technologies.

The MS in AI is also a bridge to a PhD in AI. A doctorate in AI is an essential step toward a tenure-track faculty position, a role at a frontier AI lab, or a research scientist position at a major technology company.

MS in Applied AI

The most common roles for graduates of an applied AI degree are those that focus on outcomes, mainly measurable improvements in efficiency, accuracy, cost, or customer experience. This can include AI engineers, who are responsible for designing and maintaining the systems that put AI models into production; data engineers, who are responsible for building predictive models from complex datasets; and AI consultants, who help organizations understand AI solutions and guide implementation.

Roles that emphasize theory, model development, advanced algorithms, or deep technical research will be a better fit for a degree in general AI studies, while roles that focus on implementing AI in organizations, solving business problems, managing products, or operationalizing tools will be better suited to applied AI programs.

* Sourced from Glassdoor in April 2026, based on the New York Metro area.

How an MS-AI and MS-AAI Compare

How to Choose Between an MS in AI or an MS in Applied AI

There are lots of opportunities in both general AI and applied AI fields, as well as some degree of overlap between them. In many ways, you can’t go wrong with choosing a path, but if you’re interested in working in a specific field or having a certain job title, it can be helpful to understand which degree will serve you best.

1. What do you want to study?

Are you interested in pushing the boundaries of AI? Or are you drawn to open-ended problems and exploring the theory and practice of AI solutions? Then you’d enjoy a general AI degree.

Or, do you enjoy the satisfaction of shipping real systems and seeing immediate impact? Do you want to learn how to apply AI solutions to a particular field or functional area, such as finance, marketing, or human resources? Then consider studying applied AI.

2. What careers interest you?

If you want to build core AI systems, then general AI may be the stronger route. If you want to apply AI in business, industry, or product settings, then applied AI may be the better fit. If you’re interested in flexible career options, then you may find either path valuable depending on your specialization choices.

3. What work environment do you prefer?

A general AI degree is often the stronger fit for research-driven, highly technical environments, such as tech companies, robotics labs, research institutions, autonomous vehicle firms, or innovation teams within major enterprises. An applied AI degree is better suited for business and industry settings where AI is used to solve practical challenges, improve operations, and drive strategy, such as healthcare organizations, financial services, consulting firms, marketing teams, and product-focused companies.

4. What do you want beyond academics?

Think about whether you want to build specific knowledge, expand your skills, grow your professional network, or all three. Professional development and networking opportunities may depend more on specific program formats and location than on the degree itself.

Pace University’s location in the heart of New York City provides our students with direct access to some of the largest and most distinguished healthcare facilities in the country, as well as many of the Fortune 500 companies established within steps of campus.

Innovating the Future of AI with Pace

Both Pace University’s Master’s in Artificial Intelligence and Master’s in Applied AI programs offer rewarding paths for graduates, with each tailored to distinct professional goals. An MS in AI is ideal for those who want to excel as innovators, developers, and problem solvers. The MS in applied AI program prepares problem-solvers to design, implement, and manage AI systems across different industries.

If you’re ready to take the next step in your AI career, explore the MS-AI and MS-AAI programs at Pace University, where you’ll build the skills, connections, and experience needed to make a meaningful impact in your field. Reach out today for personalized guidance on how Pace can support your journey.

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FAQ

What’s the difference between applied AI and AI?
Artificial intelligence (AI) is the broad field focused on building systems that can learn, reason, and make decisions. Applied AI focuses on using those tools to solve real-world business and industry challenges. A master’s in AI may emphasize theory, algorithms, and model development, while a master’s in applied AI often centers on implementation, strategy, ethics, and using AI in fields like healthcare, finance, and technology.

Is applied AI a thing?
Yes. Applied AI is an increasingly important field focused on putting AI to work in practical settings. Rather than concentrating only on theory or research, applied AI teaches students how to use AI tools, data, and machine learning to improve operations, products, and decision-making. Applied AI is a helpful career specialization, as employers increasingly seek professionals who can bridge technical knowledge with real-world application.

What are examples of applied AI?
Examples of applied AI include AI systems that help doctors analyze medical images, fraud detection tools used by banks, recommendation engines on streaming platforms, chatbots for customer service, smart logistics systems that optimize delivery routes, and predictive maintenance tools in manufacturing. Any examples of AI solving specific problems, improving efficiency, or creating better user experiences are examples of applied AI.

Is a master’s in applied AI worth it?
Yes. A master’s in applied AI is worth it because it can help you build in-demand skills in machine learning, automation, data strategy, and ethical AI use. It may open doors to careers in technology, healthcare, finance, consulting, and other sectors. It can be especially valuable for professionals who want to lead AI projects, apply AI in their industry, or stay competitive in a changing job market.