By Jay Hodgkins
In December 2025, The New York Times reported that artificial intelligence (AI) is “the hot new college major,” with students flocking to degree programs that focus on developing AI-related skills.
But for such a new and rapidly evolving field, “AI learning” is only loosely defined, with no set standard for which technical skills, real-world applications, and theoretical concepts like AI ethics should be taught. In the University of Virginia’s Master of Science in Business Analytics (MSBA) Program, presented jointly by the McIntire School of Commerce and Darden School of Business, a holistic approach to AI learning has been embedded throughout the 12-month degree program for working professionals since it launched in 2018.
“It is not enough to teach how to build models,” said Professor Jingjing Li, Co-Academic Director of the UVA MSBA and McIntire’s Andersen Alumni Associate Professor of Commerce. “Organizations need business leaders who understand how AI reshapes culture, operations, customer experience, and strategy.”
Among the biggest needs, Li said, organizations require leaders who can:
- Understand and evaluate AI models
- Transform data infrastructure to support AI
- Manage cross-functional AI projects
- Implement AI ethically and at scale
- Demonstrate business impact from AI investments
To deliver on those needs, UVA’s MSBA program uses a business lens to focus AI skill development across two areas: technical mastery and leadership training. Students gain technical mastery by developing critical skills in SQL, Spark, large language models (LLMs), survival analysis, deep learning, time series analyses, and more so that they can build and assess AI models responsibly. Leadership training is achieved through the program’s capstone projects, strategy and ethics courses, global immersions, and business sponsor engagement, all of which help students assess how AI can create business value, implement AI responsibly and ethically, and manage AI projects and teams.
“The MSBA is intentionally designed as a fully integrated analytics and AI curriculum in which every module builds toward a real organizational application,” said Raj Venkatesan, Co-Academic Director of the program and Darden’s Ronald Trzcinski Professor of Business Administration.
Li and Venkatesan said the UVA MSBA program’s AI learning strategy is built on five pillars:
- Develop skills in the latest AI and machine learning systems.
- Teach AI and analytics as an end-to-end system, not isolated skills.
- Emphasize real-time, real-organization AI challenges.
- Prepare students to lead AI initiatives, not just execute them.
- Integrate AI into a robust organizational strategy, including change management and value realization.
“Graduates acquire technical, strategic and leadership capabilities required to drive AI-enabled innovation inside complex organizations,” Venkatesan said.
AI Embedded from Start to Finish
The UVA MSBA is divided into five modules, each concluding with a team capstone project. Each module includes a set of coordinated courses. Across modules, students develop skills in analytics, data engineering, strategy, communication and project design. Capstones aren’t hypothetical exercises. Rather, they are sponsored by real organizations and require students to build AI-powered applications that address the real needs of sponsor organizations. Together, the modules and capstone projects progress from relatively simple to increasingly complex as students learn and develop skills. For example, for the final capstone project, student teams lead the project from problem formulation to recommendation development.
“Students solve progressively more sophisticated AI problems,” Li said. “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.”
From the start of the program, Module 1 prepares students for an applied team project requiring them to integrate AI, machine learning, and other analytics skills into a strategic business recommendation for a real corporate scenario.
In Module 2 using tools including ChatGPT, LLMs, Python, and Tableau, students develop a machine learning model to predict donors likely to participate in an upcoming fundraising campaign for a large university and deliver AI-informed recommendations.
In Module 3, students estimate links in the service-profit chain to improve customer service for Hilton and present their projects to Hilton leaders, who use insights from the student projects for real decision-making.
In Module 4, students build a real-world natural language processing (NLP) system to evaluate the U.S. Federal Drug Administration’s MedWatch reports and compare them with known and unknown adverse drug reactions using the FDALabel database. For the project, student teams have experimented with fine-tuned LLMs, advanced text classifiers and fine-tuned ChatGPT4.0 models. “This project consistently produces some of the strongest AI narratives,” Li said.
One of the most engaging, hands-on courses, taught by Professor Reza Mousavi, comes in Module 4. It requires students to build an AI agent in Python to collect a user’s travel preferences and use them to find relevant travel deals. “Students love seeing how an AI agent can orchestrate external APIs, reason over constraints and tailor results in real time,” Li said. “It’s a concrete, memorable example of applied AI systems thinking.”
By the final capstone after Module 5, students apply analytical modeling, AI product thinking and ethical AI practices with other skills developed throughout the program. Teams partner with business sponsors—including Microsoft, PwC, Capital One, Major League Baseball, and McKinsey, among others—to select projects aligned with sponsors’ business needs. Using real data, students then work with their sponsors to address active analytics and AI challenges inside these organizations using all the tools and skills developed over the course of the program.
During Module 5, students also have the opportunity to participate in global immersion trips internationally to Finland and Estonia, during which students meet leaders implementing advanced AI systems, and domestically to Washington, DC, during which they explore AI policy and ethics as well as AI adoption in federal agencies and the private sector.
From the Classroom to the Workplace
Most UVA MSBA students pursue their degree while continuing to work full time. The Class of 2026 entered with an average of 10 years of work experience. As such, students often take AI learning from courses and projects back to their workplace immediately.
For example, one student working in health care built an NLP model for patient feedback inspired by the Module 4 FDA adverse drug reaction project. The effort delivered major time savings for the student’s clinical operations team.
Another student who worked for a federal government agency used survival analysis methods taught in Module 3 to build attrition models that informed the agency’s workforce planning policy. One student who worked in marketing used donor prediction modeling learned in Module 2 to improve customer reactivations.
“Our capstone experiences make these stories commonplace among graduates,” Venkatesan said.
Students say their AI-focused highlights of their MSBA experience include how all the learning connects, rather than being taught in isolation, and their ability to apply newly learned skills at their jobs right away, according to Li and Venkatesan.
AI Is Evolving. The UVA MSBA Is, Too
Though the MSBA is less than 10 years old, AI has evolved and advanced dramatically during that time. It was only late 2022 when OpenAI released ChatGPT 3.5 to the public, setting up an AI arms race that has redefined business strategies, public discourse, and the global economy since.
Li and Venkatesan say the MSBA curriculum is designed for the next phase of AI evolution. “We will continue expanding generative AI and LLM-based coursework, and we expect growth in prompt engineering, AI-assisted analytics and multimodal modeling,” Venkatesan said. “The program will increase its emphasis on AI deployment, machine learning operations, agentic AI and responsible AI governance.”
Following the program’s foundational approaches, it will continue to expand partner-sponsored projects and global immersion experiences to deliver that learning.
As professionals around the world increasingly look to degree programs to prepare them for the immediate future of AI, Li and Venkatesan believe UVA’s MSBA stands out because it has always been ahead of the curve.
“We built AI into the foundation of the curriculum long before the generative AI boom,” Li said. “Students choose the UVA MSBA because it provides the AI skills the market now demands—and it has been doing so for years.”
This story was originally published on The Darden Report Jan. 12, 2026.
