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About this course · 40 hours

RPA with AI: learn to combine UiPath automation with artificial intelligence, so your robots can read documents, understand emails, make judgement calls and handle work that fixed rules cannot. You build solid UiPath foundations first (workflows, selectors, the REFramework and Orchestrator), then bring AI into every stage: intelligent document processing, generative-AI services for extracting, summarising and classifying information, AI-assisted development, and agent-style automations with human approval. Every module is hands-on, and the course ends with a capstone that automates a real business process, such as invoice or customer-email handling, using RPA and AI together. Suitable for beginners, testers, analysts and developers moving into automation; no programming background is required.

Course content
  1. RPA with AI: the big picture
    • What RPA is, where it fits, and how it differs from scripting
    • What AI adds to automation, and where rules are still the better choice
    • Choosing processes to automate; estimating benefit
    • The automation lifecycle and the roles in a project
  2. UiPath Studio essentials
    • Studio, Assistant and Orchestrator at a glance
    • Variables, arguments and data types
    • Control flow, loops, conditions and flowcharts
    • AI-assisted development: using AI helpers to draft, explain and fix workflows and expressions
  3. UI automation and selectors
    • Building reliable selectors: anchors, wildcards and dynamic elements
    • Desktop, web and image-based automation
    • Computer vision and AI-assisted element detection for screens that are hard to automate
    • Data and screen scraping
  4. Working with data
    • Excel, CSV and database automation
    • DataTables, lists and dictionaries
    • Strings, dates and regular expressions
    • Cleaning and standardising messy data with AI (names, addresses, categories)
    • Email and PDF automation
  5. Error handling and debugging
    • Try/catch, retry scope and custom exceptions
    • Logging and debugging tools
    • Using AI to classify failures and summarise exceptions for a human
    • Designing robots that fail safely
  6. REFramework and queues
    • The Robotic Enterprise Framework and its states
    • Transaction items and work queues
    • Adding AI steps to a transaction: confidence checks, retries and a fall-back to a person
    • Configuration files and reusable components
  7. Orchestrator and deployment
    • Publishing packages; robots, machines and folders
    • Triggers, scheduling and unattended execution
    • Assets, credentials and monitoring
    • Governing AI automations: credentials for AI services, usage monitoring and cost control
  8. AI + RPA: intelligent document processing
    • OCR and document digitisation
    • Classifying documents and extracting fields with ML models
    • Validation with a human in the loop; confidence thresholds
    • Measuring accuracy and straight-through processing
  9. AI + RPA: generative AI in workflows
    • Calling language-model services from a workflow
    • Writing prompts for extraction, summarising, classification and drafting replies
    • Turning model answers into structured data (JSON to DataTable)
    • Guardrails: accuracy checks, privacy and cost control
  10. AI + RPA: agent-style and hybrid automation
    • Rule-based robot or AI agent: choosing the right tool
    • Letting an AI decide, and a robot act: designing the hand-off
    • Human approval steps and fall-back paths
    • Monitoring and auditing AI decisions
  11. Capstone project
    • Automate an end-to-end process (for example invoice or customer-email handling) using RPA and AI together
    • Code review, deployment and presentation
Weekday = Mon–Fri · Weekend = Sat/Sun