AEM 3111

Introduction to Generative AI in Business

Course Description

Course information provided by the 2026-2027 Catalog.

Generative AI is reshaping how businesses operate, yet most enterprise AI projects produce no measurable return—failing at deployment, not at prototype. This hands-on, project-based course teaches undergraduates to use generative AI as builders and deployers in business contexts, on the premise that success in AI deployment is 95% domain knowledge and 5% AI knowledge. Module 1 builds technical fluency in how large language models work—pretraining, alignment, prompting, tool use, and agents—as students build working AI agents on a no-code workflow platform. Module 2 pivots to deployment: operations strategy, evaluation discipline, change management, and stakeholder analysis. Students complete a written conceptual prelim and a capstone project in which they design, build, and pitch an AI-enabled business application.

Enrollment Information Enrollment preference given to: Dyson students.

Last 4 Terms Offered (None)

Learning Outcomes

• Explain how large language models work as scaled next-token-prediction systems—distinguishing pretraining, post-training /alignment, and inference-time behavior—and diagnose common AI failure modes.
• Engineer context and build production-quality AI agents—multi-tool orchestration, structured outputs, memory, and hierarchical supervisor / specialist designs—that integrate with external APIs.
• Design evaluation systems for AI workflows, distinguishing verifiable from non-verifiable metrics, implementing correctness and LLM-as-judge measures, and diagnosing evaluator bias.
• Analyze AI-deployment decisions through operations-strategy, cost-quality-latency, and stakeholder lenses, and appraise why most enterprise AI initiatives fail at deployment rather than at prototype.
• Construct a business case and change-management plan for an AI initiative that links near-term operational metrics to long-term financial goals and addresses the least-likely adopters.
• Synthesize technical and domain knowledge to design, build, and pitch a working AI-enabled business application for a realistic business problem of the student's choosing.

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