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Course Outline

Introduction to Agentic AI in Business Automation

  • Definition of agentic AI and its strategic significance for automated operations
  • Examination of essential tools and frameworks for developing intelligent agents
  • Key operational applications including customer support, logistics management, and marketing initiatives

Identifying Automation Opportunities

  • Assessment of existing workflows and identification of operational inefficiencies
  • Analysis of viability and return on investment for AI-enabled automation
  • Establishment of performance indicators and system integration requirements

Designing Agentic Workflows

  • Architecture of agents focused on specific tasks and high-level orchestration
  • Structuring prompts and logical frameworks for automated decision-making agents
  • Incorporation of autonomous decision logic and exception handling mechanisms

Integrating Agents with Business Systems

  • Linking AI agents with customer relationship management, enterprise resource planning, and communication platforms
  • Utilization of Zapier, Make, or Power Automate for process orchestration
  • Execution of API-based integrations using Python programming

Applied Use Cases

  • Automation of customer service interactions and analysis of sentiment data
  • Prediction of supply chain demand and coordination with vendors
  • Optimization of marketing campaigns leveraging AI-generated insights

Governance, Security, and Monitoring

  • Administration of access controls and management of sensitive data
  • Configuration of monitoring dashboards and alert systems
  • Review and audit of automated decision outcomes for accountability

Hands-on Project: Building an Integrated AI Workflow

  • Selection of a specific process for automation implementation
  • Development and deployment of the AI agent architecture
  • Conducting tests, evaluating performance, and refining the system

Summary and Next Steps

Requirements

  • Fundamental understanding of business workflows and process automation concepts
  • Proficiency with Python or API-based integration methods
  • Prior experience utilizing productivity or automation tools

Audience

  • Product managers tasked with identifying automation opportunities
  • Automation engineers responsible for implementing AI-driven workflows
  • Business analysts designing data-informed operational processes
 21 Hours

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