UVA’s MSBA Program Prepares Professionals for the AI Era
As artificial intelligence (AI) continues to reshape industries, educational institutions are racing to equip students with the necessary skills to thrive in this new era. The University of Virginia’s Master of Science in Business Analytics (MSBA) program, a collaborative initiative between the McIntire School of Commerce and the Darden School of Business, stands out for its comprehensive approach. Since its inception in 2018, the 12-month program has been designed for working professionals seeking to lead AI-driven transformations in their organizations.
AI is not just about technology—it’s about strategic leadership. That’s the philosophy driving UVA’s MSBA curriculum, which integrates technical training with leadership development. The program responds to the urgent need for professionals who can not only build AI models but also strategically implement them to generate real business value.
Bridging the Gap Between AI Technology and Business Strategy
According to Professor Jingjing Li, the program’s co-academic director and McIntire’s Andersen Alumni Associate Professor of Commerce, organizations today demand more than just technical expertise. “It is not enough to teach how to build models,” she said. “Organizations need business leaders who understand how AI reshapes culture, operations, customer experience and strategy.”
To address this demand, the MSBA program is structured around two central pillars: technical mastery and leadership training. Students dive deep into technical topics such as SQL, Spark, large language models (LLMs), deep learning, survival analysis and time series analytics. This rigorous foundation enables them to build and evaluate AI models responsibly and effectively.
Leadership Development Through Real-World Projects
Leadership skills are fostered through capstone projects, strategy and ethics coursework, global immersions, and direct engagement with business sponsors. These elements help students not only assess the business value of AI but also manage complex projects and lead cross-functional teams.
Professor Raj Venkatesan, co-academic director and Darden’s Ronald Trzcinski Professor of Business Administration, emphasized the program’s integrated approach. “The MSBA is intentionally designed as a fully integrated analytics and AI curriculum in which every module builds toward a real organizational application,” he noted.
Venkatesan and Li outlined five guiding pillars of the MSBA’s AI learning strategy:
- Master the latest AI and machine learning technologies
- Teach AI as a system, not a collection of isolated skills
- Apply knowledge to real-time, real-world AI challenges
- Develop leadership skills for AI project execution and management
- Embed AI into overall business strategy, including change management and value realization
“Graduates acquire technical, strategic and leadership capabilities required to drive AI-enabled innovation inside complex organizations,” said Venkatesan.
A Curriculum Built Around Real Organizational Needs
The MSBA program is divided into five modules, each culminating in a team-based capstone project. These projects are not theoretical exercises but real-world initiatives sponsored by actual organizations. Students are tasked with developing AI-powered solutions that address specific business challenges.
Early modules introduce fundamental skills in analytics, data engineering, communication, and strategy. As students progress, the complexity of the capstone projects increases. By the final module, students are expected to lead projects from problem identification to the development of actionable recommendations.
“Students solve progressively more sophisticated AI problems,” said Li. “They have the opportunity to actually build production-style AI applications, such as web and mobile apps powered by machine learning and deep-learning models.”
Hands-On Learning from Day One
The program’s focus on applied learning begins in Module 1, where students collaborate on a project that integrates AI, machine learning, and analytics to solve a real corporate challenge. This hands-on approach continues throughout the program.
In Module 2, for example, students use tools like ChatGPT, Python, LLMs and Tableau to build a machine learning model that predicts donor participation in a university fundraising campaign. The outcome? Actionable, AI-driven recommendations that can make a measurable impact.
This method not only reinforces technical skills but also teaches students how to communicate findings, navigate organizational dynamics and drive adoption of AI solutions. By the time students graduate, they are prepared to lead AI initiatives that are ethical, scalable and strategically aligned with business goals.
This article is inspired by content from Original Source. It has been rephrased for originality. Images are credited to the original source.