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AIMBA I - Generative AI for Managers.pdf

Level: 500

Course Description

Generative AI (GenAI) is ushering in a new age of productivity in business. Managers who ineffectively adopt it risk being outpaced by forward-thinking competitors. This course equips students to drive impact in any industry using GenAI tools. You’ll learn to engineer effective prompts, integrate AI into workflows, and develop innovative GenAI solutions, as well as explore ethical considerations and future trends. Learn more at rize.pub/AIMBA-I.

Prerequisites

• None

Course Topics

Over 2023 and 2024, large language models (LLMs) have seen their processing capabilities explode, jumping from handling 100,000 tokens to an incredible two million tokens—that's like going from reading one research paper to digesting 20 novels at once. And it’s only going to grow further. Snowflake’s 2024 Data + AI Predictions Report suggests we're on the cusp of an AI revolution powered by natural language interfaces. Imagine being able to chat with your data like it's a person, asking it to analyze customer trends and predict future sales. Or generating an entire marketing campaign with just a few prompts, or creating personalized training programs to onboard new employees based on their unique learning styles and progress.

For aspiring managers, getting comfortable with GenAI is a modern necessity. GenAI isn’t just hype—it’s here to stay, saving not only time and resources but also unleashing creativity and productivity like never before.

• Overview of the Generative AI Landscape:
  • Introduction to Generative AI (GenAI), including its definition, importance, and impact on modern business.
  • Overview of different types of generative AI: text, visual, and multimodal AI.
  • Exploration of unique challenges and concerns for each type.
  • Examination of potential applications across various industries.
  • Familiarization with open-source generative AI tools to be used throughout the course.
• Large Language Models (LLMs):
  • Study of how LLMs can generate human-like text and their applications in business.
  • Overview of training, fine-tuning, and model-merging techniques.
  • Focus on the RAG (Retrieval-Augmented Generation) framework to enhance AI-generated content with real-time data retrieval.
• Evaluating AI Tools and Vendors:
  • Learning how to evaluate and select generative AI tools and vendors.
  • Understanding selection criteria and best practices for implementation within organizations.
• Prompt Engineering:
  • Principles of crafting effective prompts to achieve desired outputs from GenAI tools.
  • Exploration of advanced prompt engineering techniques for handling complex scenarios and maintaining context in multi-turn conversations.
  • Overview of techniques for orchestrating multiple LLMs via system prompts.
• Integrating Generative AI into Business Processes:
  • Methods for integrating generative AI into various business workflows and processes.
• Ethical Considerations and Best Practices:
  • Discussion of the ethical challenges and considerations of using generative AI in business from a managerial perspective.
  • Development of guidelines and best practices for ethical and responsible use of generative AI technologies.
  • Risk management frameworks such as the 2024 NIST AI Risk Management guide for GenAI.
• Future Trends in Generative AI:
  • Exploration and evaluation of the latest trends and future developments in generative AI.
  • Discussion of innovative applications of generative AI across various industries, such as virtual try-ons in e-commerce, AI-driven content creation, AI-assisted product design, and more.

Here are some examples of professional questions in AI management this course will help prepare the student to consider:

• How can generative AI improve customer service in a retail business? What are some potential challenges of using video AI in marketing?