Upskill Your Workforce with Tailored Trainings

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/