Introduction to AI

• What is Artificial Intelligence?

• AI Types: predictive, generative, RAG, and agentic AI

• How AI is reshaping industries and workflows

• Where humans still lead: the role of intuition and ethics

• Live demo: from idea to AI-generated output in 60 seconds

How Modern AI Works

• From data to intelligence: how machines learn

• Neural networks

• The attention, and the transformer revolution

• Large Language Models (LLMs) explained visually

• Beyond text: diffusion models, audio, and image generation

• Live demo: “How GPT sees language” and visual generation in action

Prompt Engineering: Speaking LLM’s Language

• Why prompting matters

• Instructional vs. conversational prompting

• The Role–Context–Task–Format framework

• Sample use cases

• Practice: real-life work scenarios — emails, summaries, analysis, ideation

AI and Decision Making

• Human + AI collaboration models

• Cognitive bias and trust calibration

• Using AI as a “red team” and second opinion

• Demo: comparing human vs. AI decision paths

• Practice: structured decision exercises

AI Tools and Use Cases Across Functions

• Sales & Marketing – personalization, lead scoring, copy generation

• HR – recruitment screening, skill mapping, learning & development

• Finance – forecasting, reporting, risk detection

• Project Management – AI copilots and workflow automation

• Development – low-code assistants and test automation

• Productivity & Creativity – text, visuals, presentations, video

• Demo & Practice: hands-on with leading tools (ChatGPT, Notion AI, Perplexity, Gamma, etc.)

No-Code AI Tools and Automation

• Overview of no-code/low-code AI tools

• Designing end-to-end automations (input → reasoning → output)

• Connecting AI to business systems (Docs, Sheets, Slack, CRM)

• The rise of “vibe coding” — pros, cons, and best practices

• Demo: build a working AI workflow live

• Practice: create your own mini automation

AI in Data Analysis

• Working with structured data in AI assistants

• Automatic data cleaning, charting, and interpretation

• Demo: natural-language queries on datasets

• Practice: use AI to analyze a sample business dataset

Agentic AI: Beyond Chatbots

• What makes an AI “agentic”?

• Reasoning, memory, and planning patterns

• Tools, APIs, and the Model Context Protocol (MCP)

• Multi-agent collaboration and orchestration

• Platforms overview (e.g., CrewAI, OpenDevin, AutoGen, LangGraph)

• Demo: see an AI agent complete a real task

• Practice: design and run a custom agent workflow

Retrieval-Augmented Generation (RAG) and Knowledge Management

• What is RAG and why it matters

• Embeddings, chunking, and media handling

• Integrating internal documents, databases, and APIs

• How agents use RAG for smarter answers

• Demo: chat with your organization’s documents

AI Adoption in Organizations

• AI risks: hallucinations, bias, compliance, and leakage

• Governance and ethical frameworks

• Measuring success: adoption KPIs and ROI

• Best practices from early adopters

Future Trends in

• Autonomous AI and continuous learning systems

• Multi-modal and context-aware assistants

• Personalization and “AI twins”

• Edge and on-device intelligence

• The evolving job landscape and new AI roles

• Agent interoperability and ecosystem convergence

Final Project: Your AI in Action

• Choose from guided project tracks

• Or propose your own workplace-relevant idea

• Presentation and peer feedback (optional)