🔹 Day 1: Understanding AI and Business Automation

Session 1: Introduction to Business Process Automation (BPA)

• What is BPA? Benefits and use cases
• Traditional automation vs. AI-driven automation
• Key areas for automation: HR, Finance, Operations, Customer Service

Session 2: Introduction to AI in Business

• Types of AI used in BPA: NLP, ML, Computer Vision, RPA
• Common AI tools and platforms (Power Automate, UiPath, ChatGPT, Azure AI)
• AI capabilities for document understanding, chat automation, and data extraction

Session 3: Identifying Automation Opportunities

• Process mapping and identifying inefficiencies
• Automation maturity model
• Selecting the right process to automate using AI

Lab Activities:

• Map and analyze a simple business process
• Use AI-based process mining tools (e.g., UiPath Process Mining or Microsoft Process Advisor)
• Identify tasks suitable for automation in your organization

🔹 Day 2: Implementing AI-Powered Automation

Session 1: Using AI for Document & Data Automation

• Extracting data from forms, invoices, emails
• Tools: Azure Form Recognizer, Google Document AI, Power Automate AI Builder
• Automating document classification and routing

Session 2: Conversational AI and Virtual Assistants

• Chatbots for internal/external processes
• Tools: Power Virtual Agents, Dialogflow, ChatGPT
• Integrating bots into websites, Teams, or Slack

Session 3: RPA + AI = Intelligent Automation

• Overview of Robotic Process Automation (RPA)
• Combining RPA with AI for smart decision-making
• Example: Automating invoice processing from email to ERP

Lab Activities:

• Build a document automation flow using AI Builder in Power Platform
• Create a basic chatbot using Power Virtual Agents or ChatGPT API
• Design an end-to-end automation workflow using RPA + AI

🔹 Day 3: Real-World Applications and Project Work

Session 1: AI-Powered Decision Making

• Using AI for classification and prediction tasks (ML models)
• Forecasting and intelligent recommendations
• Tools: Excel + AI, Power BI + AI visuals, AutoML platforms

Session 2: Automation at Scale & Governance

• Deployment considerations (security, scalability, monitoring)
• Human-in-the-loop and fallback mechanisms
• Compliance, ethics, and responsible AI use

Session 3: Capstone Project + Innovation Showcase

• Teams/individuals develop an AI automation use case
• Present business problem, automation design, and expected benefits
• Review and feedback session

Lab Activities:

• Implement a mini AI model using a no-code AutoML tool
• Design a governance strategy for AI automation
• Finalize and present capstone project