Artificial Intelligence in Business

Artificial intelligence (AI) has fundamentally changed the way humans are able to gather and process information. Based on input from employers, alumni and our advisory board, we believe artificial intelligence literacy will become a key differentiating factor for future analytics professionals.

Because of this, we have made AI a key component of Minnesota Carlson's Master of Science in Business Analytics and Artificial Intelligence curriculum. Here, you’ll learn AI-related tools and techniques to help differentiate yourself in the job market.

 

What AI Skills You’ll Learn

In the MSBAi program, you will learn AI-related tools and techniques such as:

  • Machine learning and exploratory/predictive analytics
  • Deep learning and neural networks
  • Generative/agentic AI and large language models
  • Recommender systems and personalization technologies
  • AI-powered forecasting and time series analysis
  • Responsible AI, fairness, privacy and explainability
  • Data engineering and deployment of AI and analytics solutions
  • AI-assisted business decision making
Student presents a Power BI dashboard of YMCA participant data on a wall-mounted screen in a classroom.

How AI Relates to Business Analytics 

Business analytics and AI are increasingly intertwined. While analytics focuses on extracting insights from data, AI enables organizations to scale those insights, automate decision processes and augment human judgment. Together, business analytics and AI help organizations move from understanding what happened to predicting what will happen and recommending or automating what should happen next.

Artificial intelligence is transforming how organizations make decisions, serve customers, optimize operations and create new products and services. Across industries, AI is being used to automate routine tasks, generate insights from large volumes of data, personalize customer experiences, improve forecasting and support strategic decision-making.

What Sets our AI Curriculum Apart

Many AI programs focus either on technical model development detached from business contexts, or a managerial-oriented perspective that doesn’t delve deep into the underlying technology. Carlson’s MSBAi program takes a balanced approach that combines analytics, artificial intelligence and managerial decision-making to solve complex business problems.

You will learn how AI technologies work with sufficient rigor, but more importantly, you will learn when and how to apply them to solve business problems. The curriculum emphasizes translating AI capabilities into business value, evaluating organizational impact, managing implementation challenges and ensuring responsible and ethical use of AI.

Because the program is in the Carlson School of Management, you develop both technical fluency and the business acumen needed to lead AI initiatives and communicate effectively with executives, managers and technical teams.

Glass-walled Carlson Analytics Lab with students seated at tables, viewed through the open doorway.

How AI is Infused into the MSBAi Curriculum

Even before the release of ChatGPT, we steadily increased coverage of AI in our curriculum. Now, AI is a central and core part of the MSBAi program. 

AI is embedded throughout the MSBAi curriculum and is introduced progressively across the program. You will develop both foundational AI knowledge and practical experience applying AI to business problems. You will also gain hands-on experience applying these techniques to real business problems through coursework and experiential learning projects.

Required AI Courses

You will be exposed to AI concepts through required courses such as: 

  • Introduction to Business Analytics in R (1st semester)
  • Exploratory Data Analytics (2nd semester)
  • Predictive Analytics (2nd semester)
  • Responsible AI (3rd semester)
  • Time Series Analysis and Forecasting (3rd semester) 

These courses cover topics such as machine learning, deep learning, large language models, explainable AI, responsible AI and modern forecasting techniques. 

Elective AI Courses

If you want to deepen your AI experience, you can enroll in specialized courses such as:

  • Generative AI for Business Applications (3rd semester)
  • Prescriptive Analytics for Optimal Decision Making (3rd semester)
  • Recommender Systems Techniques and Applications (3rd semester)

These courses allow you to explore cutting-edge AI technologies used by leading digital platforms.

AI Topics Covered by Course

Multiple MSBAi courses introduce the key models and algorithms behind AI applications and their business applications to help you build your skills in this area.

CourseRequired or ElectiveSemester OfferedAI Topics Covered (may vary each semester)

Introduction to Business Analytics in R

Required

1st Semester

R, data acquisition, engineering, visualization, exploratory and predictive analytics, lifecycle of business analytics

Exploratory Data Analytics

Required

2nd Semester 

Autoencoder, large language models for explorative analytics

Predictive Analytics

Required

2nd Semester

Deep Neural Network, Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), sequence-to-sequence model, attention mechanism, transformer architecture

Generative AI for Business Applications

Elective

3rd Semester

Applications, limitations, ethical and social implications of generative AI, ChatGPT, Midjourney

Responsible AI

Required

3rd Semester

Explainable AI techniques, privacy-preserving AI techniques, fairness-aware machine learning models

Time Series Analysis and Forecasting

Required

3rd Semester

Use of deep-learning-based models such as deep autoregressive models, Long Short-Term Memory (LSTM), and transformers for forecasting

Prescriptive Analytics for Optimal Decision Making

Elective

3rd Semester

Optimization techniques, prescriptive models

Recommender System Techniques and Applications

Elective

3rd Semester

Context-aware and deep-learning empowered recommender systems, including neural collaborative filtering, deep factorization machine, and self-attentive sequential recommendation

View MSBAi Course Descriptions

 


Why AI is Important for Business Analytics Students

Our goal is to equip you with the skills and knowledge needed to succeed in business analytics roles after graduation. 

Based on insights gathered from employer surveys, alumni feedback and our advisory board, we have identified a growing demand for data professionals proficient in training and deploying AI models and utilizing generative AI tools. These skills are highly coveted in the current marketplace. The extensive AI coverage in our curriculum will enable you to develop a good understanding of these technologies and the ability to harness them responsibly and effectively to create value for businesses. 

No matter how much you choose to focus on AI when you’re in the MSBAi program, you will gain valuable analytics skills and business acumen that will enhance your career prospects.

 

Careers Shaped by AI: What You Can Do with an MSBAi Degree

AI has changed how analytics works, and employers want professionals who can turn data into insight and strategy. This article breaks down real job paths for MSBAi grads, from data analyst roles to analytics translators and AI product managers. It also shows how Carlson’s AI-infused curriculum prepares you to meet market demand and thrive in an AI-driven workplace.

Frequently asked questions

No. The required courses in the MSBAi curriculum build a solid foundation to understand advanced AI techniques.

Our Responsible AI course is designed exactly for this purpose. This course covers various ethical considerations of AI (e.g., algorithmic bias, privacy issues, AI security, and AI transparency). The course has hands-on components so you not only learn about the concepts but also specific techniques you can employ to build responsible AI systems and solutions.

No. You can take elective courses that do not focus on AI. However, AI will still be a core component of some required courses given how important it is in the industry today.

The full extent of the impact of AI on all industries is yet to be seen. A general consensus seems to be that generative AI is a productivity boosting tool, including boosting the productivity of data scientists and data engineers. AI hasn’t eliminated the role of data scientists/engineers; rather, it has enhanced their capabilities and reduced the time spent on repetitive tasks. Our belief is that the industry needs data science professionals who can understand and effectively leverage generative AI.

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