Online Master of Science in Artificial Intelligence Management
The University of Cincinnati's online Master of Science in Artificial Intelligence Management (MSAIM), launching in Fall 2026, is designed for professionals and recent graduates ready to lead the future of business through artificial intelligence. Whether you’re looking to upskill for career advancement or pivot into an AI-focused role, this 30-credit-hour program helps you develop the tools to drive strategic innovation with confidence.
AI Management Program Highlights
High Quality Education
Led by Lindner College of Business faculty who conduct leading-edge research and have extensive experience in both industry and academia, this flexible, fully online program focuses on the practical application of AI in business environments. From strategy and deployment to governance and ethics. You’ll gain the skills to communicate technical concepts to stakeholders, manage cross-functional teams, and implement responsible AI practices across industries.
In-demand skills you’ll build:
- AI strategy and governance
- Project and product management
- Business intelligence and analytics
- Data ethics and risk management
- Communication and stakeholder engagement
Position yourself as a bridge between AI innovation and strategic business execution. Prepare to lead with integrity in one of today’s most dynamic and fast-growing fields.
Flexibility
- Built for part-time students, this 30-credit-hour program can be completed in less than two years.
- Designed for working professionals balancing personal and professional commitments, our 100% online, asynchronous courses allow you to build a schedule that fits around your personal and professional commitments.
- Integrates graduate certificates for flexible skill-building
- Stackable pathway to a full degree – Transfer credits from the Artificial Intelligence in Business Graduate Certificate
Support from Application through Graduation
At UC, you’ll have a full support team behind you:
Enrollment Services Advisor: Your partner through the application process, getting enrolled, and starting your program
Student Success Coordinator: Helping you prepare for classes and stay on track
Access to Resources: Access to university resources that will support you through your program including online learning expectations and resources, health and wellness resources, and academic support
Lindner College of Business' online MS in AI Management is a 30-32 credit hour program. Full time students can complete the program in three semesters (12 months). Part time students will be able to complete the program in approximately nine semesters (36 months).
| Course | Title/Description | Credit |
|---|---|---|
| IS7001 | Foundations for Applied Artificial Intelligence This preparatory course provides essential knowledge for students entering the MS in Applied AI program. Covering core Artificial Intelligence (AI)/Machine Learning (ML) concepts, basic programming skills, and an introduction to end-to-end AI systems, this course equips students with foundational tools to understand and engage with AI applications from a managerial perspective. Students will learn the vocabulary of AI, grasp key programming concepts, and explore the structure of AI systems from data flow to outputs, building a solid base for further AI studies. |
1 |
| Course | Title/Description | Credit |
|---|---|---|
| IS7065 | Generative Artificial Intelligence for Business This course examines the technology underlying modern generative artificial intelligence / machine learning models from a business perspective, including their uses in coding, professional and artistic applications, and the various controversies and challenges to work and/or society they may pose. |
2 |
| IS7085 | Governance of AI/ML Systems This course teaches students how to develop, scale-up, and sustainably manage high-performing Artificial Intelligence/Machine Learning systems in business organizations. It introduces concepts and techniques that enable the development of surrogate approaches to explain AI/ML models, build redundancy in AI/ML systems, and calculate and minimize risk of failures while using such approaches. |
2 |
| BANA7075 | Machine Learning Design for Business This course provides a framework for developing real-world machine learning systems that are deployable, reliable, and scalable. Designing machine learning systems is the process of defining the software architecture, infrastructure, algorithms, and data for a machine learning system to satisfy specified business requirements. Without deliberate design, machine learning systems can get outdated quickly because (1) the tools continue to evolve, (2) business requirements change, and (3) data distributions constantly shift. |
2 |
| IS8036 | Survey of Machine Learning and Artificial Intelligence This course is a survey of Machine Learning (ML) and Artificial Intelligence (AI) from the Data Scientist’s perspective. It explores ML and AI topics, current and emerging technologies, and applications for students to gain understanding of the successful implementation of ML and AI to address key business and industry problems. |
2 |
| OM8050 | Artificial Intelligence Strategy and Operational Transformation This integrative, case-based course examines the strategic role of AI in transforming operations across industries. Students will explore the AI industry landscape, analyze the evolution of major players, and study cases that highlight AI’s operational impact in both mature and high-tech industries. Emphasis will be placed on frameworks for identifying high-ROI AI initiatives, managing AI-driven transformations, and navigating legal and compliance issues in AI adoption. Through analysis and discussion, students will develop a strategic mindset to drive AI-enabled operational advancements and position firms competitively in the evolving AI landscape. |
2 |
Students are required to complete 20 hours of electives. At least 8 hours must be selected from the AI Electives section. The remaining 12 hours may be completed through a Lindner Graduate Certificate or graduate level IS, BANA, or OM courses. Courses from the BUFN certificate may not be used to fulfill these 12 elective hours.
| Course | Title/Description | Credit |
|---|---|---|
| BANA7038 | Data Analysis Methods This course covers the fundamental concepts of applied data analysis methods. Various aspects of linear and logistic regression models are introduced, with emphasis on real data applications. Students are required to analyze data using major statistical software packages. BANA 7038 should not be taken for credit by MS-Business Analytics students. |
2 |
| BANA7046 | Data Mining I This is a course in statistical data mining with emphasis on hands-on case study experiences using various data mining/machine learning methods and major software packages to analyze complex real world data. Topics include data preprocessing, k-nearest neighbors, generalized linear regression, subset and LASSO variable selection, model evaluation, cross validation, classification and regression trees. |
2 |
| BANA7047 | Data Mining II This is a course in statistical data mining with emphasis on hands-on case study experiences using various data mining/machine learning methods and major software packages to analyze complex real world data. Topics include advanced trees: bagging, random forests, boosting; nonparametric smoothing methods; generalized additive models; data preprocessing/scaling; neural networks; deep learning; cluster analysis; association rules. |
2 |
| BANA7080 | Artificial Intelligence and Machine Learning Applications in Decision Modeling This course helps develop knowledge of how to integrate Machine Learning (ML) and Artificial Intelligence (AI) within data-driven decision modeling pipelines. The class presents a managerial perspective on existing traditional techniques in data-driven decision modeling and contrast them with emerging approaches in which ML and AI are integrated into the decision-making pipeline. Students are introduced to state-of-the-art software tools and packages and will have the opportunity to implement basic data-driven ML/AI-based optimization models. |
2 |
| FIN7047 | The main objective of the course is to introduce students to fintech and cryptocurrency. The goal is to understand the fundamental concepts underlying financial technologies and their applications. Examples of these include artificial intelligence, machine learning, and blockchain in financial markets, such as business activities, financing, and investments.
|
2 |
| FIN7053 | Algorithmic Trading This course provides a comprehensive treatment of the fundamental principles required to design and implement algorithmic trading models in financial markets. The course will introduce the best practices and the formal process of generating trading ideas, the differences between low-frequency and high-frequency trading signals, back-testing and its associated biases, optimization techniques, and industry metrics for evaluating algorithmic trading models’ performance. Students will have the opportunity to implement basic algorithms in well-known paper-trading platforms. |
2 |
| IS7036 | Data Mining for Business Intelligence This course is designed for the in-depth learning of data-mining knowledge and techniques in the context of business intelligence. The topics include association rules, classification, clustering and text mining. Students will apply and integrate the business intelligence knowledge learned in this course in leading software packages. |
2 |
| IS7088 | Artificial Intelligence-Powered Bots This course explores the design, development, and deployment of AI-powered bots, focusing on task automation and conversational AI. Students will learn to automate predefined workflows and gradually incorporate generative AI to enhance bot intelligence and adaptability. Through hands-on projects and case studies, students will gain practical experience in creating AI-powered bots while examining ethical considerations and strategic business impacts. |
2 |
| IS8034 | Big Data Integration This course presents an overview of the principles of data integration, the fundamental basis for developing useful and flexible business intelligence platforms. Modern data integration needs differ from traditional approaches in four main dimensions that parallel differences between big data and traditional data: volume, velocity, variety, and veracity. |
2 |
| MGMT7019 | Artificial Intelligence in Organizations This course explores the transformative impact of artificial intelligence (AI) on modern organizations, examining both the opportunities and the challenges that AI presents in the business environment. Students will develop an understanding of AI technologies and their practical applications, while critically analyzing ethical considerations, societal and workforce implications, and strategic implications of AI adoption. The course delves into specific organizational functions, with special emphasis on firm strategy, human resource management, and operational efficiency. Through a combination of theoretical frameworks and real world applications, students will learn to evaluate AI implementation strategies, assess risks, and develop guidelines for responsible AI use in organizations. Students will also gain the knowledge needed to lead AI initiatives, manage human-AI collaboration, and drive innovation in their organizations. |
2 |
| MKTG7043 | Artificial Intelligence for Marketing Managers MKTG 7043 is designed to provide the student with a cohesive understanding of how to use various artificial intelligence platforms across the entire continuum of marketing management through the exploration of marketing problems with an emphasis on qualitative and quantitative analysis, integrative marketing decision-making, and strategy formulation. |
2 |
| OM7065 | Artificial Intelligence in Healthcare Operations This course explores the transformative role of artificial intelligence (AI) in healthcare, addressing both technical and ethical dimensions. Students will gain an understanding of how AI techniques are revolutionizing patient care, diagnostics, and healthcare management. Through hands-on projects and/or case studies, students will learn to develop, evaluate, and implement AI models tailored to clinical data and healthcare applications. The course will also cover key issues such as data privacy, regulatory frameworks, and the importance of model transparency and fairness in clinical settings. By examining current trends and innovations, including AI in medical imaging, personalized medicine, and public health analytics, students will be equipped to critically assess and contribute to AI advancements that drive better health outcomes. This course is designed for graduate students interested in leveraging AI to address real-world healthcare challenges. |
2 |
| OM7085 | Artificial Intelligence Applications for Supply Chain Management This course explores how leading firms leverage artificial intelligence (AI) to drive innovation in operations and supply chain management (SCM). The course relies heavily on the case method of instruction. Students will examine AI’s role in the management of operations productivity/efficiency, process management, sourcing/supply management, logistics, and in advancing sustainable practices. Through analysis and discussion of case situations, students will develop a nuanced understanding of how AI shapes modern supply chain management and learn to critically evaluate AI-driven solutions within SCM. By the end of the course, students will be prepared to identify and propose creative use of AI for supply chain decisions and develop a strategic mindset to address the evolving landscape of artificial intelligence as applied to supply chain management. |
2 |
| Course | Title/Description | Credit |
|---|---|---|
| BANA7XXX+ IS7XXX+ OM7XXX+ |
BANS, IS, OM electives Students must complete an additional 12 elective hours. Any 12-credit LCB graduate certificate other than the Business Foundations certificate can be used to satisfy the elective requirements. Or 12 hours of OM, BANA, or IS Graduate Courses. |
12 |
All individuals with an undergraduate degree from a regionally accredited institution, regardless of field of study, are eligible to apply for admission to the online Master of Science in Artificial Intelligence Management program at the Carl H. Lindner College of Business.
Admission to the Lindner College of Business AI Management program is selective and based on a combination of factors, including academic and professional achievement, strong communication skills, and a proven record of effective leadership. Applicants should have at least a B grade average (or equivalent) in undergraduate coursework, or otherwise provide evidence of academic promise that is satisfactory to the admitting department.
Applications are reviewed in a holistic manner, with careful consideration given to all aspects of your application portfolio. Following review by the admissions committee, accepted applicants will be notified directly.
Admission Materials
To apply, you will be asked to provide the following information:
- Current Résumé
- Goal Statement/Essay
- Unofficial Transcripts
- One Letter of Recommendation
- IELTS or TOEFL Score (International applicants only)
- All Other Test Scores Optional
Complete the online application and submit the application fee.
Standard Application Fees:
- $65.00 for domestic applicants to most degree programs
- $70.00 for international applicants to most degree programs
- $20.00 for domestic applicants to Graduate Certificates
- $25.00 for international applicants to Graduate Certificates
- Application fees are waived for Summer 2026 applications submitted by March 1st, 2026
- Application fees are waived for Fall 2026 applications submitted by July 1st, 2026
- Fee waivers are automatically applied for applicants who:
- are currently serving in the US armed forces
- are veterans of the US armed forces
Unofficial transcripts are required (including University of Cincinnati transcripts).
* Once accepted, send official transcripts to grad.admissions@uc.edu or mail to the following address:
Graduate School at the University of Cincinnati
110 Van Wormer Hall
P.O. Box 210627
Cincinnati, OH 45221-0627
International Bachelor’s Degrees: For Admissions review, transcripts must be translated into English.
If you have a three-year international bachelor’s degree, please use the free WES Degree Equivalency Tool (WES) to verify its U.S. equivalency. WES also provides free tools to help you calculate your GPA.
If admitted, you will need to obtain an official NACES course-by-course evaluation, which must be completed within your first semester of the program.
Contact your Enrollment Services Advisor for additional guidance.
One letter of recommendation is required.
- Please provide name and email of recommender.
Professional Resume:
A resume relevant to academic and professional data. It should include the applicant’s name; phone; email; colleges attended with degrees, dates conferred, and grade point average; employment history; professional experience; present employer; and names of references.
Curriculum Vitae:
A detailed look at the applicant’s career path, including achievements, publications, and awards. Including comprehensive information about candidate’s research background, presentations, publications, committee memberships and other experience of an academic, clinical, or scientific nature.
At the University of Cincinnati Lindner College of Business, we value thought-driven leaders. With this in mind, please upload a personal goal statement expressing the following:
- What specific professional changes are you seeking, and how do you believe this program will facilitate those changes?
- How will you leverage the University of Cincinnati Lindner Alumni network and connections with business leaders for your career advancement?
- What aspects of the program appeal to you and why?
- What global issues are you interested in, and how do you see these elements benefiting your career?
Formatting: We recommend the personal goal statement be between 250 and 500 words, double-spaced.
The GMAT and GRE are not required for admission to the MS AI Management program. Applicants can submit test scores if they would like them to be considered as part of their application portfolio.
All applications are reviewed holistically, with careful consideration given to the entire portfolio.
Admissions Review:
- You must demonstrate your proficiency in English (if not your native language). This includes submitting an IELTS, TOEFL or Duolingo test score. Please reference the Graduate College website for information on the minimum score needed to be eligible for the program.
- For English proficiency we also accept Duolingo. Please reference the College of Business' admission checklist for more information.
- Transcripts must be translated into English.
- If you have a three-year international degree, use the free WES degree equivalency tool to verify its U.S. equivalency.
If Admitted:
- Obtain an official NACES course-by-course evaluation (this must be completed by the end of your first semester).
| Term | Application Deadline | Classes Start |
|---|---|---|
Fall 2026 Spring 2027 |
July 1, 2026 November 15, 2026 |
August 24, 2026 January 11, 2026 |
The University of Cincinnati's online course fees differ depending on the program. On average, students will accrue fewer fees than students attending on-campus classes.
The one fee applied across all UC Online programs is the distance learning fee. Students living outside the state of Ohio must also pay an additional “non-resident” fee to enroll in courses at UC Online. This fee is lower than the out-of-state fee for traditional on-campus programs.
To view tuition information and program costs, visit the Online Program Fees page.
The Lindner College of Business is accredited by the AACSB and has been recognized as one of the best business schools in the country by The Princeton Review and U.S. News and World Report.
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We offer over 130 degrees from undergraduate to doctoral programs. Each program is supported by a team of Enrollment Services Advisors (ESAs) who are here to help answer any questions you have.