Online Master of Science in AI in Business

Module 1 establishes a shared foundation for understanding AI as a business capability rather than a collection of tools. The module introduces two enduring business challenges—improvement and innovation—and develops the ability to navigate from business problem to appropriate AI-enabled solutions. Students are introduced to an analytics/AI capability framework (the “House of Analytics”) that guides decision-making when selecting or building tools. Emphasis is placed on developing sound judgment about where AI is most effective—and where it is not—within business contexts. 

Module 2 focuses on the application of AI to improve existing business processes and decision systems. Building on the foundational fluency developed in Module 1, this module emphasizes disciplined execution, operational reliability, and measurable performance within real organizational contexts. Students learn how AI creates value through incremental improvement—enhancing efficiency, quality, and consistency—rather than through radical transformation. The module explores how AI capabilities can be integrated into existing workflows, roles, and decision routines, while addressing performance evaluation, risk, and tradeoffs. Through practical scenarios, students examine end-to-end processes, identify opportunities for AI-enabled enhancements, and redesign workflows to ensure insights are effectively delivered and adopted at scale. 

Module 3 focuses on how AI enables innovation in situations where existing processes, data, and evaluation frameworks are insufficient. Building on prior modules, the emphasis shifts from improvement to exploration—using AI to surface, amplify, and prioritize novel insights that may not fit within current organizational structures. A central concept in this module is escalation: directing attention and resources toward emerging opportunities that warrant further investigation and investment. Students learn how to design and manage experiments in environments where data is incomplete and success metrics are still evolving, while navigating uncertainty, risk, and learning. The module also explores how to align AI-driven innovation efforts with broader organizational strategy. 

Module 4 focuses on the leadership and governance challenges that emerge as AI systems scale and evolve within organizations. Moving beyond deployment, the emphasis is on stewardship—ensuring that intelligent systems remain reliable, accountable, and aligned with organizational values over time. Integrating lessons from both improvement and innovation, this module addresses how organizations balance performance, risk, and responsibility at the system level. Students explore how to design governance structures that combine AI-enabled monitoring with human judgment, clarify decision rights and accountability, and embed ethical and regulatory considerations into ongoing operations. The module also examines how organizations can create feedback loops and learning mechanisms that allow AI systems to adapt responsibly while maintaining trust.