Online MS in AI in Business Curriculum

Purpose

MOD 1 establishes a shared foundation for understanding AI as a business capability rather than a set of tools. The goal is to build fluency in how AI tools (the AI technology stack) function inside organizations—across data, models, workflows, people, and governance. Students develop a capability framework (House of Analytics) for responsible AI-enabled decision-making. This module orients you to the full landscape you will revisit and deepen in subsequent MODs.

You’ll focus on:

• Framing high-value business problems where the AI Technology Stack can meaningfully create or protect value
• Understanding how AI capabilities interact with organizational processes and decision structures
• Distinguishing improvement, innovation, and governance challenges in AI deployment
• Developing judgment about where AI fits—and where it does not—in business contexts

You leave MOD 1 able to:

Reason clearly about AI tools as a business capability system and engage confidently in AI-related decisions, conversations, and initiatives without relying on tool-level expertise.

Purpose

MOD 2 focuses on using AI to improve existing business processes and decision systems. Building on the fluency developed in MOD 1, this module emphasizes disciplined execution, reliability, and performance in real organizational settings. Students learn how AI creates value through incremental improvement rather than radical change. The focus is on making AI work at scale within current operating constraints.

You’ll focus on:

• Applying AI to enhance efficiency, quality, and consistency in core business processes
• Integrating AI into existing workflows, roles, and decision routines
• Evaluating performance, risk, and tradeoffs in AI-enabled operations
• Learning how organizations build and sustain improvement over time

You leave MOD 2 able to:

Design and lead AI-enabled process improvements that deliver measurable value while maintaining operational stability and organizational trust.

MOD 2 Use Case: Improving a Core Business Process with AI

A mid-sized services firm struggles with inconsistent decision-making across regional operations, leading to cost overruns and uneven customer experience. The company has access to data, forecasting models, and decision-support tools, but these capabilities are loosely connected to frontline workflows and managerial routines.

In MOD 2, you would examine the end-to-end process, identify where AI-enabled forecasting, pattern detection, and decision support can improve consistency and performance, and redesign workflows so insights are delivered at the right moment to the right roles. You would also address adoption, incentives, and oversight to ensure improvements are trusted, sustained, and scalable across the organization.

Purpose

MOD 3 focuses on how AI enables innovation when existing processes, data, and evaluation frameworks are insufficient. A central theme is escalation—using AI to surface, amplify, and prioritize novel insights that warrant organizational attention and investment. Students learn how AI supports experimentation, learning, and strategic change under conditions of uncertainty. The emphasis is on creating new capabilities and value propositions rather than optimizing existing operations.

You’ll learn to:

• Use AI to identify, surface, and escalate novel opportunities beyond existing categories and processes
• Design experiments when data, metrics, and evaluation standards are still emerging
• Manage uncertainty, risk, and learning in AI-enabled innovation efforts
• Align escalated insights and initiatives with organizational strategy and long-term direction

You’ll leave MOD 3 able to:
Lead AI-enabled innovation by using escalation to focus organizational attention, designing experiments under uncertainty, and scaling new capabilities responsibly within complex organizations.

Use Case: Escalating Innovation with AI

In MOD 3, you would use AI capabilities such as semantic analysis, pattern discovery across unstructured data, and exploratory modeling to surface and escalate novel patterns. You would then design experiments and new evaluation frameworks—using simulations, scenario analysis, and comparative learning—to assess potential value and decide which opportunities warrant strategic investment.

Purpose

MOD 4 focuses on the leadership and governance challenges that arise as AI systems evolve and scale within organizations. Students examine how intelligent systems must be overseen to remain reliable, accountable, and aligned with organizational values over time. The module integrates lessons from improvement and innovation to address risk, responsibility, and learning at the system level. The emphasis is on stewardship and governance rather than deployment.

You’ll focus on:

• Designing governance structures for AI systems that operate and adapt over time
• Balancing innovation, performance, risk, and accountability in AI-enabled organizations
• Addressing ethical, regulatory, and organizational implications of intelligent systems
• Leading organizational learning and strategic change in environments shaped by AI

You leave MOD 4 able to:

Exercise informed judgment and stewardship over evolving AI systems, ensuring they create sustained value while remaining governable, responsible, and trusted.

MOD 4 Use Case: Governing Intelligent Systems Over Time

A global organization has deployed multiple AI-enabled systems across operations, customer engagement, and innovation initiatives. While individual systems perform well, leaders face growing challenges in overseeing how these systems interact, evolve, and shape organizational outcomes. As models are updated, data sources shift, and regulatory expectations change, governance has become fragmented—focused on compliance rather than long-term stewardship.

In MOD 4, you would design governance as an enabling leadership infrastructure that combines AI-enabled monitoring with human judgment. This includes defining decision rights, escalation paths, and accountability structures that clarify when automated decisions are appropriate and when human intervention is required. You would embed ethical considerations into everyday choices about data use, thresholds, and system boundaries, while creating feedback loops that allow intelligent systems to adapt responsibly over time. The goal is not to freeze systems in place, but to ensure they remain trustworthy, governable, and aligned with organizational and societal values as they evolve.