Course Description:
This foundational course demystifies the rapidly evolving landscape of artificial intelligence for business leaders, innovators, and professionals. Participants will gain a clear understanding of the differences between AI systems and AI automations, learning when to use pre-built solutions and when to invest in custom development. We’ll explore LLM-powered applications vs rule-based systems, run through practical cost-benefit analyses, and unpack infrastructure and scaling requirements.

The course also introduces the fundamentals of AI agents—contrasting generative AI with agentic AI, examining reactive vs proactive systems, and understanding the varying levels of human involvement. You’ll learn how large language models actually work, how to apply prompt engineering in business contexts, and how to manage token usage for cost efficiency. Finally, we’ll cover model selection criteria to help you choose between leading AI models like GPT-4, Claude, and Gemini for specific business needs.

By the end of this module, you will have the practical knowledge to evaluate, select, and integrate the right AI approach for your organization—whether it’s building bespoke automations, leveraging pre-built platforms, or deploying AI agents for high-impact business outcomes.

Lecture 1: What are AI Systems vs AI Automations

This lecture provides a clear, practical framework to understand the differences between AI systems and AI automations, covering when and how to use each. You’ll explore pre-built vs custom solutions, LLM vs rule-based approaches, cost-benefit decisions, infrastructure needs, and process-specific automation strategies.

Key Objectives: 

• • Pre-built AI systems vs custom automations
  • LLM-powered applications vs rule-based systems
  • Cost-benefit analysis: When to build vs buy
  • Infrastructure requirements and scaling considerations
  • Custom automations for specific business processes
  • When to use each approach

Lecture 2: Introduction to AI Agents

This lecture introduces AI Agents, comparing generative AI with agentic AI, and exploring the differences between reactive and proactive systems. You’ll also learn how varying levels of human involvement shape their design, capabilities, and applications.

Key Objectives: 

• • Generative AI vs Agentic AI comparison
  • Reactive vs Proactive systems
  • Human involvement levels

Lecture 3: Introduction to AI Agents

This lecture explains how Large Language Models function in real-world business contexts, from practical workflows to prompt engineering. You’ll also learn about token economics, cost optimization, and how to choose the right model for your specific needs.

Key Objectives: 

• • How Large Language Models work in practice
  • Prompt engineering for business applications
  • Token economics and cost optimization
  • Model selection criteria (GPT-4, Claude, Gemini)