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AIMBA II - Business Use Cases for AI.pdf

Level: 500

Course Description

Just like you wouldn’t use a financial model to drive a marketing campaign, different business use cases require different AI tools. In this course, students will explore the potential and limitations of AI technologies, learning to identify business problems suitable for AI solutions and build effective AI implementation strategies. By the end of this course, students will address key challenges and solutions in AI implementation. Learn more at rize.pub/AIMBA-II.

Prerequisites

• None

Course Topics

A company hastily chooses a predictive model designed for general market trends without fully understanding the specifics of their business problem. They use historical sales data from all their stores combined, without distinguishing between different store locations, customer demographics, or seasonal variations. This leads to overstocking and stock-outs due to ignoring regional differences, which leads in turn to frustrated customers and financial losses.

As this hypothetical example shows, the ability to accurately match AI models to specific business challenges—and to identify AI pitfalls such as hallucination—isn't just a technical skill for business managers; it's a fundamental aspect of steering a company successfully when it comes to harnessing the power of AI. Like using a hammer when you need a screwdriver, using the wrong tool—an AI model that doesn’t fit the problem at hand—can lead to wasted resources, skewed data, and missed opportunities.

Leaders must understand what each AI tool can do, ensuring that they deploy the right solutions to enhance decision-making, optimize operations, and ultimately deliver better outcomes. This course will equip students with the knowledge to identify the right AI models for specific business needs, ensuring you can navigate and leverage AI technologies effectively to drive a company's success.

• Introduction to AI in Business
  • Overview of AI, its history, evolution, and significance in modern business.
• Types of AI Technologies
  • Exploration of different AI technologies, including Machine Learning (ML), Deep Learning (DL), Natural Language Processing (NLP), Computer Vision, Generative AI (GenAI), Evolutionary Optimization (EC), and Reinforcement Learning (RL).
  • Methods for evaluating performance of AI models.
• AI Tools and Platforms
  • Examination of popular AI platforms (Google AI, IBM Watson, Microsoft Azure AI, Amazon SageMaker) and toolkits/frameworks [such as TensorFlow, PyTorch, and Scikit-learn], and their integration into business operations.
• Data and AI
  • Discussion on the importance of data quality and quantity, data preparation and cleaning, and data privacy and ethical considerations.
  • How can LLMs help us identify opportunities and create synthetic data sets? How might those data sets be used to train a predictive model [such as TensorFlow], and that predictor used as a surrogate to train a prescriptive model?
• AI Use Cases in Various Industries
  • Analysis of AI applications in finance (fraud detection, algorithmic trading), healthcare (disease prediction, personalized medicine), marketing (customer insights, personalization), and manufacturing (predictive maintenance, quality control).
• Implementing AI in Business Strategy
  • Steps for identifying business problems suitable for AI solutions, building an AI strategy, and managing AI projects, with a focus on key challenges and solutions.
• Future Trends in AI
  • Exploration of emerging AI technologies, the impact of AI on the job market, and the ethical and social implications of AI.

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