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Curriculum

Overview of AI

  • Historical background of AI
  • Understanding of the key trends in AI and how these are influencing the future of business


Understanding AI Technologies

  • Basic concepts of machine learning and data analysis
  • Introduction to natural language processing (NLP) (LLMs)
  • Overview of computer vision


AI and Business Transformation

  • An overview of global industries where AI is being applied. The focus is both on general principles that work across industries as well as specific adaptations in specific business operations.
  • Identify which areas of your department are ripe for automation and digital transformation
  • Learn how to transform your organization into an innovative, efficient, and sustainable company of the future.

Introduction to Prompt Engineering

  • Overview of how prompt engineering works with AI models like ChatGPT, emphasizing its role in generating accurate and relevant outputs.
  • Discuss the strategic importance of understanding prompt engineering to harness AI for decision-making, creativity, and problem-solving.

Hands-on Exercise: How to prompt effectively

  • Understanding Prompts: How different prompts yield different results, with examples.
    Techniques for writing clear, specific, and goal-oriented prompts.
  • Create prompts for specific business scenarios (e.g., market analysis, customer feedback analysis).
  • Reviewing and refining prompts to see how changes affect AI outputs.

Integrating Prompt Engineering into Your Workflow

  • Overview of tools that support prompt engineering, such as ChatGPT, Jasper, and other AI-driven content platforms.
  • How to communicate prompt engineering needs with technical teams and ensure alignment with business objectives.
  • Strategies for incorporating prompt engineering into daily operations and strategic initiatives.

AI in Supply Chain Overview

  • Introduction to AI applications in supply chain management
  • Benefits and challenges of AI in logistics and supply chain operations

    Applications of AI in Supply Chain Management
  • How AI can improve the accuracy and responsiveness of your demand and inventory management.
  • Explore AI tools that provide real-time insights into supply chain performance- ThroughPut AI.
  • Discover AI applications that improve logistics efficiency and cost-effectiveness.


Applications of AI in Revenue Management

  • How AI transforms revenue management through automation, real-time decision-making, and data-driven insights.
  • AI-Driven Pricing Strategies
  • Aligning AI initiatives with revenue management goals.


Inventory Management

  • AI for optimizing inventory levels and reducing stockouts.
  • Automated inventory tracking and management.
  • Case studies of AI-driven inventory management

Case Study

  • Coca cola using AI to optimize various aspects of its supply chain, including demand forecasting, inventory management, distribution logistics, and sustainability practices.
  • https://www.supplychaindive.com/news/coca-cola-ai-artificial-intelligence-microsoft/714889/


Demand forecasting

  • Strategic Understanding of AI-Driven Forecasting.
  • Implementing AI Forecasting in Your Organization.
  • How AI transforms demand management through automation, real-time decision-making, and data-driven insights.

Sales Forecasting

  • AI for predicting sales trends based on customer behavior, market conditions, and historical data.
  • Measuring ROI and Long-Term Impact of forecasting.

AI in Manufacturing Overview

  • Introduction to AI applications in manufacturing.
  • Benefits and challenges of AI in production environments.


Quality Control

  • AI for automated quality inspection and defect detection.
  • Computer vision applications in quality control.
  • Case studies of AI-driven quality assurance.


Predictive Maintenance

  • Machine learning models for predicting equipment failures
    Implementing predictive maintenance systems.
  • Case studies of successful predictive maintenance applications.


Case Study:

  • Siemens maximizes productivity in the industrial and manufacturing sectors by exploring the capabilities of AI and machine learning.
  • https://blog.siemens.com/2024/02/unlocking-the-power-of-artificial-intelligence-in-manufacturing-with-siemens-industrial-edge/#:~:text=An%20ecosystem%20for%20the%20industry’s,Test%2C%20and%20in%20predictive%20maintenance.

AI in Finance Overview

  • Introduction to AI applications in finance, including fraud detection, investment analysis, and customer service.
  • Benefits and challenges of AI in financial operations, such as improved accuracy, efficiency, and potential regulatory hurdles.


Customer Support

  • AI-driven automation for instant customer support and query resolution.
  • Personalization through AI-based customer interaction analysis.
  • Case studies highlighting AI’s impact on customer service in finance.


Data Analysis

  • AI-enhanced data processing for faster insights and pattern detection.
  • Predictive analytics for improved financial forecasting and risk assessment.
  • Case studies demonstrating AI-driven data analysis in finance.


Case Study:

  • Federal Bank Limited personalized AI-assistants for customer support.
  • https://cloud.google.com/customers/federal-bank/

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