Online MS in AI in Business Curriculum

Purpose

Module 0 is a non-credit, pass/fail orientation designed to prepare students for the Online MS in AI in Business program experience. It introduces Boston University and the Questrom School of Business, along with the key policies, procedures, and resources that support student success. The module provides hands-on exposure to the learning management system (LMS) and other core technologies, including setup requirements and guidance to ensure a smooth start. Students are also introduced to program support teams and communication channels, and develop an understanding of what it takes to succeed in an online learning environment.

You’ll focus on:

• Gaining familiarity with the Online MS in AI in Business program structure, policies, and key resources
• Setting up and navigating required technology platforms, including the LMS and supporting tools
• Understanding expectations for success in an online learning environment and how to access support

You leave Module 0 able to:

Navigate the program environment effectively, utilize required technologies, and engage with available resources and support systems to begin the Online MS in AI in Business program with confidence.

Purpose

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.

You’ll focus on:

• Developing a systems-level understanding of AI as a business capability spanning data, models, work systems, people, and governance
• Identifying and framing high-value business problems where AI can create or protect value
• Applying the “House of Analytics” framework for responsible, AI-enabled decision-making
• Distinguishing between improvement and innovation challenges in AI deployment and their implications for tool selection
• Evaluating where AI should—and should not—be applied within organizational contexts

You leave Module 1 able to:

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

Purpose

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.

You’ll focus on:

• Applying AI to improve efficiency, quality, and consistency in core business processes
• Integrating AI capabilities into existing workflows, roles, and decision-making routines
• Evaluating performance, risks, and tradeoffs in AI-enabled operations
• Driving adoption, incentives, and oversight for sustained improvement
• Designing scalable process improvements that deliver measurable value

You leave Module 2 able to:

Lead and implement AI-enabled process improvements that generate 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

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.

You’ll learn to:

• Using AI to surface and escalate novel insights beyond existing processes and categories
• Designing and running experiments when data, metrics, and evaluation frameworks are still emerging
• Managing uncertainty, risk, and learning in AI-enabled innovation efforts
• Aligning new opportunities and initiatives with organizational strategy and long-term goals
• Leading the development and scaling of new AI-enabled capabilities within complex organizations

You’ll leave Module 3 able to:
Lead AI-enabled innovation initiatives by identifying new opportunities, designing experiments under uncertainty, and scaling emerging 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

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.

You’ll focus on:

• Designing governance structures that enable effective oversight of evolving AI systems
• Balancing innovation, performance, risk, and accountability in AI-enabled organizations
• Addressing ethical, regulatory, and organizational implications of intelligent systems
• Defining decision rights, escalation paths, and human-AI interaction boundaries
• Leading organizational learning and stewardship to ensure AI systems remain trustworthy and aligned over time

You leave MOD 4 able to:

Exercise informed judgment and lead the governance of AI systems to ensure they create sustained value while remaining accountable, trustworthy, and aligned with organizational and societal expectations.

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.