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BUS-S 355 Topics in Information Systems: Agentic AI Systems for Business Applications

  • 16 weeks
  • 3 credits
  • Prerequisite(s): BUS-K 201/BUS-K204 with C or higher and completion of the self-paced GenAI 101 Canvas course.

Artificial Intelligence (AI), and specifically, Generative AI (GenAI) has staked its claim as a disruptive technology in the past decade. At the heart of modern GenAI approaches reside foundation models such as ChatGPT, Llama, and others that power agentic AI workflows. Increasingly, numerous organizations and startups are building their business models around the promise of agentic AI workflows being able to automate significant capabilities within their firms. Indeed, agentic AI has shown substantial capabilities in customer management, sales, financial services, supply chain management, social media management, market research, meeting minute generation, customer service/management, and more. The initial potential of agentic AI has led to a significant uptick in the demand for professionals well-versed in (a) fundamental concepts of standard and emerging foundation models and agentic AI methodologies and (2) their application across varying data types and emerging domains. This course is designed to provide students with a hands-on, no/low-code introduction to the fundamental concepts, approaches, tools, and applications of modern agentic AI systems, including developing strong business plans, use cases, and KPIs for selected technical solutions. The emphasis of this course will be on the application-oriented nature of agentic AI systems, rather than the pure theoretical or technical underpinnings of AI algorithms or agentic AI systems. Each lecture will have a combination of conceptual knowledge and practical examples from real business environments. The core of the course centers around a hands-on agentic AI project that aims to quickly provide tangible and practical outputs for selected applications while considering the business environment, use cases, stakeholders, KPIs, and monetization strategies.

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